# ecfp ECFP feature calculator. Extended-Connectivity fingerprints (ECFP), a.k.a. Morgan fingerprints, calculated with [RDKit cheminformatics library](http://rdkit.org/docs/source/rdkit.Chem.rdMolDescriptors.html?highlight=getmorganfingerprintasbitvect#rdkit.Chem.rdMolDescriptors.GetMorganFingerprintAsBitVect) > Rogers, David, and Mathew Hahn. “Extended-Connectivity Fingerprints.” > Journal of Chemical Information and Modeling 50, no. 5 (May 24, 2010): > 742–54. https://doi.org/10.1021/ci100050t. Each feature is a single bit of the feature vector. ## Parameters * size (type: int; default: 2048): The fingerprint size. It should be 1024, 2048 or 4096. # padel_ALOGP ALOGP PaDEL descriptor The following features are calculated: * ALogP: Ghose-Crippen LogKow * ALogp2: Square of ALogP * AMR: Molar refractivity All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_AP2DFP PaDEL AP2DFPfingerprint 2D atom pairs fingerprint - Presence of atom pairs at various topological distances * Number of bits: 780 * Bit prefix: AP2DFP All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. ## Parameters * size (type: Optional; default: None): Description unavailable. * search_depth (type: Optional; default: None): Description unavailable. # padel_AP2DFPC PaDEL AP2DFPCfingerprint 2D atom pairs fingerprint count - Count of atom pairs at various topological distances * Number of bits: 780 * Bit prefix: AP2DFPC All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. ## Parameters * size (type: Optional; default: None): Description unavailable. * search_depth (type: Optional; default: None): Description unavailable. # padel_APol APol PaDEL descriptor The following features are calculated: * apol: Sum of the atomic polarizabilities (including implicit hydrogens) All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_AcidicGroupCount AcidicGroupCount PaDEL descriptor The following features are calculated: * nAcid: Number of acidic groups. The list of acidic groups is defined by these SMARTS "$([O H1]-[C,S,P]=O)", "$([* - !$(*~[* +])])", "$([NH](S(=O)=O)C(F)(F)F)", and "$(n1nnnc1)" originally presented in JOELib All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_AromaticAtomsCount AromaticAtomsCount PaDEL descriptor The following features are calculated: * naAromAtom: Number of aromatic atoms All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_AromaticBondsCount AromaticBondsCount PaDEL descriptor The following features are calculated: * nAromBond: Number of aromatic bonds All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_AtomCount AtomCount PaDEL descriptor The following features are calculated: * nAtom: Number of atoms * nHeavyAtom: Number of heavy atoms (i.e. not hydrogen) * nH: Number of hydrogen atoms * nB: Number of boron atoms * nC: Number of carbon atoms * nN: Number of nitrogen atoms * nO: Number of oxygen atoms * nS: Number of sulphur atoms * nP: Number of phosphorus atoms * nF: Number of fluorine atoms * nCl: Number of chlorine atoms * nBr: Number of bromine atoms * nI: Number of iodine atoms * nX: Number of halogen atoms (F, Cl, Br, I, At, Uus) All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_Autocorrelation Autocorrelation PaDEL descriptor The following features are calculated: * ATS0m: Broto-Moreau autocorrelation - lag 0 / weighted by mass * ATS1m: Broto-Moreau autocorrelation - lag 1 / weighted by mass * ATS2m: Broto-Moreau autocorrelation - lag 2 / weighted by mass * ATS3m: Broto-Moreau autocorrelation - lag 3 / weighted by mass * ATS4m: Broto-Moreau autocorrelation - lag 4 / weighted by mass * ATS5m: Broto-Moreau autocorrelation - lag 5 / weighted by mass * ATS6m: Broto-Moreau autocorrelation - lag 6 / weighted by mass * ATS7m: Broto-Moreau autocorrelation - lag 7 / weighted by mass * ATS8m: Broto-Moreau autocorrelation - lag 8 / weighted by mass * ATS0v: Broto-Moreau autocorrelation - lag 0 / weighted by van der Waals volumes * ATS1v: Broto-Moreau autocorrelation - lag 1 / weighted by van der Waals volumes * ATS2v: Broto-Moreau autocorrelation - lag 2 / weighted by van der Waals volumes * ATS3v: Broto-Moreau autocorrelation - lag 3 / weighted by van der Waals volumes * ATS4v: Broto-Moreau autocorrelation - lag 4 / weighted by van der Waals volumes * ATS5v: Broto-Moreau autocorrelation - lag 5 / weighted by van der Waals volumes * ATS6v: Broto-Moreau autocorrelation - lag 6 / weighted by van der Waals volumes * ATS7v: Broto-Moreau autocorrelation - lag 7 / weighted by van der Waals volumes * ATS8v: Broto-Moreau autocorrelation - lag 8 / weighted by van der Waals volumes * ATS0e: Broto-Moreau autocorrelation - lag 0 / weighted by Sanderson electronegativities * ATS1e: Broto-Moreau autocorrelation - lag 1 / weighted by Sanderson electronegativities * ATS2e: Broto-Moreau autocorrelation - lag 2 / weighted by Sanderson electronegativities * ATS3e: Broto-Moreau autocorrelation - lag 3 / weighted by Sanderson electronegativities * ATS4e: Broto-Moreau autocorrelation - lag 4 / weighted by Sanderson electronegativities * ATS5e: Broto-Moreau autocorrelation - lag 5 / weighted by Sanderson electronegativities * ATS6e: Broto-Moreau autocorrelation - lag 6 / weighted by Sanderson electronegativities * ATS7e: Broto-Moreau autocorrelation - lag 7 / weighted by Sanderson electronegativities * ATS8e: Broto-Moreau autocorrelation - lag 8 / weighted by Sanderson electronegativities * ATS0p: Broto-Moreau autocorrelation - lag 0 / weighted by polarizabilities * ATS1p: Broto-Moreau autocorrelation - lag 1 / weighted by polarizabilities * ATS2p: Broto-Moreau autocorrelation - lag 2 / weighted by polarizabilities * ATS3p: Broto-Moreau autocorrelation - lag 3 / weighted by polarizabilities * ATS4p: Broto-Moreau autocorrelation - lag 4 / weighted by polarizabilities * ATS5p: Broto-Moreau autocorrelation - lag 5 / weighted by polarizabilities * ATS6p: Broto-Moreau autocorrelation - lag 6 / weighted by polarizabilities * ATS7p: Broto-Moreau autocorrelation - lag 7 / weighted by polarizabilities * ATS8p: Broto-Moreau autocorrelation - lag 8 / weighted by polarizabilities * ATS0i: Broto-Moreau autocorrelation - lag 0 / weighted by first ionization potential * ATS1i: Broto-Moreau autocorrelation - lag 1 / weighted by first ionization potential * ATS2i: Broto-Moreau autocorrelation - lag 2 / weighted by first ionization potential * ATS3i: Broto-Moreau autocorrelation - lag 3 / weighted by first ionization potential * ATS4i: Broto-Moreau autocorrelation - lag 4 / weighted by first ionization potential * ATS5i: Broto-Moreau autocorrelation - lag 5 / weighted by first ionization potential * ATS6i: Broto-Moreau autocorrelation - lag 6 / weighted by first ionization potential * ATS7i: Broto-Moreau autocorrelation - lag 7 / weighted by first ionization potential * ATS8i: Broto-Moreau autocorrelation - lag 8 / weighted by first ionization potential * ATS0s: Broto-Moreau autocorrelation - lag 0 / weighted by I-state * ATS1s: Broto-Moreau autocorrelation - lag 1 / weighted by I-state * ATS2s: Broto-Moreau autocorrelation - lag 2 / weighted by I-state * ATS3s: Broto-Moreau autocorrelation - lag 3 / weighted by I-state * ATS4s: Broto-Moreau autocorrelation - lag 4 / weighted by I-state * ATS5s: Broto-Moreau autocorrelation - lag 5 / weighted by I-state * ATS6s: Broto-Moreau autocorrelation - lag 6 / weighted by I-state * ATS7s: Broto-Moreau autocorrelation - lag 7 / weighted by I-state * ATS8s: Broto-Moreau autocorrelation - lag 8 / weighted by I-state * AATS0m: Average Broto-Moreau autocorrelation - lag 0 / weighted by mass * AATS1m: Average Broto-Moreau autocorrelation - lag 1 / weighted by mass * AATS2m: Average Broto-Moreau autocorrelation - lag 2 / weighted by mass * AATS3m: Average Broto-Moreau autocorrelation - lag 3 / weighted by mass * AATS4m: Average Broto-Moreau autocorrelation - lag 4 / weighted by mass * AATS5m: Average Broto-Moreau autocorrelation - lag 5 / weighted by mass * AATS6m: Average Broto-Moreau autocorrelation - lag 6 / weighted by mass * AATS7m: Average Broto-Moreau autocorrelation - lag 7 / weighted by mass * AATS8m: Average Broto-Moreau autocorrelation - lag 8 / weighted by mass * AATS0v: Average Broto-Moreau autocorrelation - lag 0 / weighted by van der Waals volumes * AATS1v: Average Broto-Moreau autocorrelation - lag 1 / weighted by van der Waals volumes * AATS2v: Average Broto-Moreau autocorrelation - lag 2 / weighted by van der Waals volumes * AATS3v: Average Broto-Moreau autocorrelation - lag 3 / weighted by van der Waals volumes * AATS4v: Average Broto-Moreau autocorrelation - lag 4 / weighted by van der Waals volumes * AATS5v: Average Broto-Moreau autocorrelation - lag 5 / weighted by van der Waals volumes * AATS6v: Average Broto-Moreau autocorrelation - lag 6 / weighted by van der Waals volumes * AATS7v: Average Broto-Moreau autocorrelation - lag 7 / weighted by van der Waals volumes * AATS8v: Average Broto-Moreau autocorrelation - lag 8 / weighted by van der Waals volumes * AATS0e: Average Broto-Moreau autocorrelation - lag 0 / weighted by Sanderson electronegativities * AATS1e: Average Broto-Moreau autocorrelation - lag 1 / weighted by Sanderson electronegativities * AATS2e: Average Broto-Moreau autocorrelation - lag 2 / weighted by Sanderson electronegativities * AATS3e: Average Broto-Moreau autocorrelation - lag 3 / weighted by Sanderson electronegativities * AATS4e: Average Broto-Moreau autocorrelation - lag 4 / weighted by Sanderson electronegativities * AATS5e: Average Broto-Moreau autocorrelation - lag 5 / weighted by Sanderson electronegativities * AATS6e: Average Broto-Moreau autocorrelation - lag 6 / weighted by Sanderson electronegativities * AATS7e: Average Broto-Moreau autocorrelation - lag 7 / weighted by Sanderson electronegativities * AATS8e: Average Broto-Moreau autocorrelation - lag 8 / weighted by Sanderson electronegativities * AATS0p: Average Broto-Moreau autocorrelation - lag 0 / weighted by polarizabilities * AATS1p: Average Broto-Moreau autocorrelation - lag 1 / weighted by polarizabilities * AATS2p: Average Broto-Moreau autocorrelation - lag 2 / weighted by polarizabilities * AATS3p: Average Broto-Moreau autocorrelation - lag 3 / weighted by polarizabilities * AATS4p: Average Broto-Moreau autocorrelation - lag 4 / weighted by polarizabilities * AATS5p: Average Broto-Moreau autocorrelation - lag 5 / weighted by polarizabilities * AATS6p: Average Broto-Moreau autocorrelation - lag 6 / weighted by polarizabilities * AATS7p: Average Broto-Moreau autocorrelation - lag 7 / weighted by polarizabilities * AATS8p: Average Broto-Moreau autocorrelation - lag 8 / weighted by polarizabilities * AATS0i: Average Broto-Moreau autocorrelation - lag 0 / weighted by first ionization potential * AATS1i: Average Broto-Moreau autocorrelation - lag 1 / weighted by first ionization potential * AATS2i: Average Broto-Moreau autocorrelation - lag 2 / weighted by first ionization potential * AATS3i: Average Broto-Moreau autocorrelation - lag 3 / weighted by first ionization potential * AATS4i: Average Broto-Moreau autocorrelation - lag 4 / weighted by first ionization potential * AATS5i: Average Broto-Moreau autocorrelation - lag 5 / weighted by first ionization potential * AATS6i: Average Broto-Moreau autocorrelation - lag 6 / weighted by first ionization potential * AATS7i: Average Broto-Moreau autocorrelation - lag 7 / weighted by first ionization potential * AATS8i: Average Broto-Moreau autocorrelation - lag 8 / weighted by first ionization potential * AATS0s: Average Broto-Moreau autocorrelation - lag 0 / weighted by I-state * AATS1s: Average Broto-Moreau autocorrelation - lag 1 / weighted by I-state * AATS2s: Average Broto-Moreau autocorrelation - lag 2 / weighted by I-state * AATS3s: Average Broto-Moreau autocorrelation - lag 3 / weighted by I-state * AATS4s: Average Broto-Moreau autocorrelation - lag 4 / weighted by I-state * AATS5s: Average Broto-Moreau autocorrelation - lag 5 / weighted by I-state * AATS6s: Average Broto-Moreau autocorrelation - lag 6 / weighted by I-state * AATS7s: Average Broto-Moreau autocorrelation - lag 7 / weighted by I-state * AATS8s: Average Broto-Moreau autocorrelation - lag 8 / weighted by I-state * ATSC0c: Centered Broto-Moreau autocorrelation - lag 0 / weighted by charges * ATSC1c: Centered Broto-Moreau autocorrelation - lag 1 / weighted by charges * ATSC2c: Centered Broto-Moreau autocorrelation - lag 2 / weighted by charges * ATSC3c: Centered Broto-Moreau autocorrelation - lag 3 / weighted by charges * ATSC4c: Centered Broto-Moreau autocorrelation - lag 4 / weighted by charges * ATSC5c: Centered Broto-Moreau autocorrelation - lag 5 / weighted by charges * ATSC6c: Centered Broto-Moreau autocorrelation - lag 6 / weighted by charges * ATSC7c: Centered Broto-Moreau autocorrelation - lag 7 / weighted by charges * ATSC8c: Centered Broto-Moreau autocorrelation - lag 8 / weighted by charges * ATSC0m: Centered Broto-Moreau autocorrelation - lag 0 / weighted by mass * ATSC1m: Centered Broto-Moreau autocorrelation - lag 1 / weighted by mass * ATSC2m: Centered Broto-Moreau autocorrelation - lag 2 / weighted by mass * ATSC3m: Centered Broto-Moreau autocorrelation - lag 3 / weighted by mass * ATSC4m: Centered Broto-Moreau autocorrelation - lag 4 / weighted by mass * ATSC5m: Centered Broto-Moreau autocorrelation - lag 5 / weighted by mass * ATSC6m: Centered Broto-Moreau autocorrelation - lag 6 / weighted by mass * ATSC7m: Centered Broto-Moreau autocorrelation - lag 7 / weighted by mass * ATSC8m: Centered Broto-Moreau autocorrelation - lag 8 / weighted by mass * ATSC0v: Centered Broto-Moreau autocorrelation - lag 0 / weighted by van der Waals volumes * ATSC1v: Centered Broto-Moreau autocorrelation - lag 1 / weighted by van der Waals volumes * ATSC2v: Centered Broto-Moreau autocorrelation - lag 2 / weighted by van der Waals volumes * ATSC3v: Centered Broto-Moreau autocorrelation - lag 3 / weighted by van der Waals volumes * ATSC4v: Centered Broto-Moreau autocorrelation - lag 4 / weighted by van der Waals volumes * ATSC5v: Centered Broto-Moreau autocorrelation - lag 5 / weighted by van der Waals volumes * ATSC6v: Centered Broto-Moreau autocorrelation - lag 6 / weighted by van der Waals volumes * ATSC7v: Centered Broto-Moreau autocorrelation - lag 7 / weighted by van der Waals volumes * ATSC8v: Centered Broto-Moreau autocorrelation - lag 8 / weighted by van der Waals volumes * ATSC0e: Centered Broto-Moreau autocorrelation - lag 0 / weighted by Sanderson electronegativities * ATSC1e: Centered Broto-Moreau autocorrelation - lag 1 / weighted by Sanderson electronegativities * ATSC2e: Centered Broto-Moreau autocorrelation - lag 2 / weighted by Sanderson electronegativities * ATSC3e: Centered Broto-Moreau autocorrelation - lag 3 / weighted by Sanderson electronegativities * ATSC4e: Centered Broto-Moreau autocorrelation - lag 4 / weighted by Sanderson electronegativities * ATSC5e: Centered Broto-Moreau autocorrelation - lag 5 / weighted by Sanderson electronegativities * ATSC6e: Centered Broto-Moreau autocorrelation - lag 6 / weighted by Sanderson electronegativities * ATSC7e: Centered Broto-Moreau autocorrelation - lag 7 / weighted by Sanderson electronegativities * ATSC8e: Centered Broto-Moreau autocorrelation - lag 8 / weighted by Sanderson electronegativities * ATSC0p: Centered Broto-Moreau autocorrelation - lag 0 / weighted by polarizabilities * ATSC1p: Centered Broto-Moreau autocorrelation - lag 1 / weighted by polarizabilities * ATSC2p: Centered Broto-Moreau autocorrelation - lag 2 / weighted by polarizabilities * ATSC3p: Centered Broto-Moreau autocorrelation - lag 3 / weighted by polarizabilities * ATSC4p: Centered Broto-Moreau autocorrelation - lag 4 / weighted by polarizabilities * ATSC5p: Centered Broto-Moreau autocorrelation - lag 5 / weighted by polarizabilities * ATSC6p: Centered Broto-Moreau autocorrelation - lag 6 / weighted by polarizabilities * ATSC7p: Centered Broto-Moreau autocorrelation - lag 7 / weighted by polarizabilities * ATSC8p: Centered Broto-Moreau autocorrelation - lag 8 / weighted by polarizabilities * ATSC0i: Centered Broto-Moreau autocorrelation - lag 0 / weighted by first ionization potential * ATSC1i: Centered Broto-Moreau autocorrelation - lag 1 / weighted by first ionization potential * ATSC2i: Centered Broto-Moreau autocorrelation - lag 2 / weighted by first ionization potential * ATSC3i: Centered Broto-Moreau autocorrelation - lag 3 / weighted by first ionization potential * ATSC4i: Centered Broto-Moreau autocorrelation - lag 4 / weighted by first ionization potential * ATSC5i: Centered Broto-Moreau autocorrelation - lag 5 / weighted by first ionization potential * ATSC6i: Centered Broto-Moreau autocorrelation - lag 6 / weighted by first ionization potential * ATSC7i: Centered Broto-Moreau autocorrelation - lag 7 / weighted by first ionization potential * ATSC8i: Centered Broto-Moreau autocorrelation - lag 8 / weighted by first ionization potential * ATSC0s: Centered Broto-Moreau autocorrelation - lag 0 / weighted by I-state * ATSC1s: Centered Broto-Moreau autocorrelation - lag 1 / weighted by I-state * ATSC2s: Centered Broto-Moreau autocorrelation - lag 2 / weighted by I-state * ATSC3s: Centered Broto-Moreau autocorrelation - lag 3 / weighted by I-state * ATSC4s: Centered Broto-Moreau autocorrelation - lag 4 / weighted by I-state * ATSC5s: Centered Broto-Moreau autocorrelation - lag 5 / weighted by I-state * ATSC6s: Centered Broto-Moreau autocorrelation - lag 6 / weighted by I-state * ATSC7s: Centered Broto-Moreau autocorrelation - lag 7 / weighted by I-state * ATSC8s: Centered Broto-Moreau autocorrelation - lag 8 / weighted by I-state * AATSC0c: Average centered Broto-Moreau autocorrelation - lag 0 / weighted by charges * AATSC1c: Average centered Broto-Moreau autocorrelation - lag 1 / weighted by charges * AATSC2c: Average centered Broto-Moreau autocorrelation - lag 2 / weighted by charges * AATSC3c: Average centered Broto-Moreau autocorrelation - lag 3 / weighted by charges * AATSC4c: Average centered Broto-Moreau autocorrelation - lag 4 / weighted by charges * AATSC5c: Average centered Broto-Moreau autocorrelation - lag 5 / weighted by charges * AATSC6c: Average centered Broto-Moreau autocorrelation - lag 6 / weighted by charges * AATSC7c: Average centered Broto-Moreau autocorrelation - lag 7 / weighted by charges * AATSC8c: Average centered Broto-Moreau autocorrelation - lag 8 / weighted by charges * AATSC0m: Average centered Broto-Moreau autocorrelation - lag 0 / weighted by mass * AATSC1m: Average centered Broto-Moreau autocorrelation - lag 1 / weighted by mass * AATSC2m: Average centered Broto-Moreau autocorrelation - lag 2 / weighted by mass * AATSC3m: Average centered Broto-Moreau autocorrelation - lag 3 / weighted by mass * AATSC4m: Average centered Broto-Moreau autocorrelation - lag 4 / weighted by mass * AATSC5m: Average centered Broto-Moreau autocorrelation - lag 5 / weighted by mass * AATSC6m: Average centered Broto-Moreau autocorrelation - lag 6 / weighted by mass * AATSC7m: Average centered Broto-Moreau autocorrelation - lag 7 / weighted by mass * AATSC8m: Average centered Broto-Moreau autocorrelation - lag 8 / weighted by mass * AATSC0v: Average centered Broto-Moreau autocorrelation - lag 0 / weighted by van der Waals volumes * AATSC1v: Average centered Broto-Moreau autocorrelation - lag 1 / weighted by van der Waals volumes * AATSC2v: Average centered Broto-Moreau autocorrelation - lag 2 / weighted by van der Waals volumes * AATSC3v: Average centered Broto-Moreau autocorrelation - lag 3 / weighted by van der Waals volumes * AATSC4v: Average centered Broto-Moreau autocorrelation - lag 4 / weighted by van der Waals volumes * AATSC5v: Average centered Broto-Moreau autocorrelation - lag 5 / weighted by van der Waals volumes * AATSC6v: Average centered Broto-Moreau autocorrelation - lag 6 / weighted by van der Waals volumes * AATSC7v: Average centered Broto-Moreau autocorrelation - lag 7 / weighted by van der Waals volumes * AATSC8v: Average centered Broto-Moreau autocorrelation - lag 8 / weighted by van der Waals volumes * AATSC0e: Average centered Broto-Moreau autocorrelation - lag 0 / weighted by Sanderson electronegativities * AATSC1e: Average centered Broto-Moreau autocorrelation - lag 1 / weighted by Sanderson electronegativities * AATSC2e: Average centered Broto-Moreau autocorrelation - lag 2 / weighted by Sanderson electronegativities * AATSC3e: Average centered Broto-Moreau autocorrelation - lag 3 / weighted by Sanderson electronegativities * AATSC4e: Average centered Broto-Moreau autocorrelation - lag 4 / weighted by Sanderson electronegativities * AATSC5e: Average centered Broto-Moreau autocorrelation - lag 5 / weighted by Sanderson electronegativities * AATSC6e: Average centered Broto-Moreau autocorrelation - lag 6 / weighted by Sanderson electronegativities * AATSC7e: Average centered Broto-Moreau autocorrelation - lag 7 / weighted by Sanderson electronegativities * AATSC8e: Average centered Broto-Moreau autocorrelation - lag 8 / weighted by Sanderson electronegativities * AATSC0p: Average centered Broto-Moreau autocorrelation - lag 0 / weighted by polarizabilities * AATSC1p: Average centered Broto-Moreau autocorrelation - lag 1 / weighted by polarizabilities * AATSC2p: Average centered Broto-Moreau autocorrelation - lag 2 / weighted by polarizabilities * AATSC3p: Average centered Broto-Moreau autocorrelation - lag 3 / weighted by polarizabilities * AATSC4p: Average centered Broto-Moreau autocorrelation - lag 4 / weighted by polarizabilities * AATSC5p: Average centered Broto-Moreau autocorrelation - lag 5 / weighted by polarizabilities * AATSC6p: Average centered Broto-Moreau autocorrelation - lag 6 / weighted by polarizabilities * AATSC7p: Average centered Broto-Moreau autocorrelation - lag 7 / weighted by polarizabilities * AATSC8p: Average centered Broto-Moreau autocorrelation - lag 8 / weighted by polarizabilities * AATSC0i: Average centered Broto-Moreau autocorrelation - lag 0 / weighted by first ionization potential * AATSC1i: Average centered Broto-Moreau autocorrelation - lag 1 / weighted by first ionization potential * AATSC2i: Average centered Broto-Moreau autocorrelation - lag 2 / weighted by first ionization potential * AATSC3i: Average centered Broto-Moreau autocorrelation - lag 3 / weighted by first ionization potential * AATSC4i: Average centered Broto-Moreau autocorrelation - lag 4 / weighted by first ionization potential * AATSC5i: Average centered Broto-Moreau autocorrelation - lag 5 / weighted by first ionization potential * AATSC6i: Average centered Broto-Moreau autocorrelation - lag 6 / weighted by first ionization potential * AATSC7i: Average centered Broto-Moreau autocorrelation - lag 7 / weighted by first ionization potential * AATSC8i: Average centered Broto-Moreau autocorrelation - lag 8 / weighted by first ionization potential * AATSC0s: Average centered Broto-Moreau autocorrelation - lag 0 / weighted by I-state * AATSC1s: Average centered Broto-Moreau autocorrelation - lag 1 / weighted by I-state * AATSC2s: Average centered Broto-Moreau autocorrelation - lag 2 / weighted by I-state * AATSC3s: Average centered Broto-Moreau autocorrelation - lag 3 / weighted by I-state * AATSC4s: Average centered Broto-Moreau autocorrelation - lag 4 / weighted by I-state * AATSC5s: Average centered Broto-Moreau autocorrelation - lag 5 / weighted by I-state * AATSC6s: Average centered Broto-Moreau autocorrelation - lag 6 / weighted by I-state * AATSC7s: Average centered Broto-Moreau autocorrelation - lag 7 / weighted by I-state * AATSC8s: Average centered Broto-Moreau autocorrelation - lag 8 / weighted by I-state * MATS1c: Moran autocorrelation - lag 1 / weighted by charges * MATS2c: Moran autocorrelation - lag 2 / weighted by charges * MATS3c: Moran autocorrelation - lag 3 / weighted by charges * MATS4c: Moran autocorrelation - lag 4 / weighted by charges * MATS5c: Moran autocorrelation - lag 5 / weighted by charges * MATS6c: Moran autocorrelation - lag 6 / weighted by charges * MATS7c: Moran autocorrelation - lag 7 / weighted by charges * MATS8c: Moran autocorrelation - lag 8 / weighted by charges * MATS1m: Moran autocorrelation - lag 1 / weighted by mass * MATS2m: Moran autocorrelation - lag 2 / weighted by mass * MATS3m: Moran autocorrelation - lag 3 / weighted by mass * MATS4m: Moran autocorrelation - lag 4 / weighted by mass * MATS5m: Moran autocorrelation - lag 5 / weighted by mass * MATS6m: Moran autocorrelation - lag 6 / weighted by mass * MATS7m: Moran autocorrelation - lag 7 / weighted by mass * MATS8m: Moran autocorrelation - lag 8 / weighted by mass * MATS1v: Moran autocorrelation - lag 1 / weighted by van der Waals volumes * MATS2v: Moran autocorrelation - lag 2 / weighted by van der Waals volumes * MATS3v: Moran autocorrelation - lag 3 / weighted by van der Waals volumes * MATS4v: Moran autocorrelation - lag 4 / weighted by van der Waals volumes * MATS5v: Moran autocorrelation - lag 5 / weighted by van der Waals volumes * MATS6v: Moran autocorrelation - lag 6 / weighted by van der Waals volumes * MATS7v: Moran autocorrelation - lag 7 / weighted by van der Waals volumes * MATS8v: Moran autocorrelation - lag 8 / weighted by van der Waals volumes * MATS1e: Moran autocorrelation - lag 1 / weighted by Sanderson electronegativities * MATS2e: Moran autocorrelation - lag 2 / weighted by Sanderson electronegativities * MATS3e: Moran autocorrelation - lag 3 / weighted by Sanderson electronegativities * MATS4e: Moran autocorrelation - lag 4 / weighted by Sanderson electronegativities * MATS5e: Moran autocorrelation - lag 5 / weighted by Sanderson electronegativities * MATS6e: Moran autocorrelation - lag 6 / weighted by Sanderson electronegativities * MATS7e: Moran autocorrelation - lag 7 / weighted by Sanderson electronegativities * MATS8e: Moran autocorrelation - lag 8 / weighted by Sanderson electronegativities * MATS1p: Moran autocorrelation - lag 1 / weighted by polarizabilities * MATS2p: Moran autocorrelation - lag 2 / weighted by polarizabilities * MATS3p: Moran autocorrelation - lag 3 / weighted by polarizabilities * MATS4p: Moran autocorrelation - lag 4 / weighted by polarizabilities * MATS5p: Moran autocorrelation - lag 5 / weighted by polarizabilities * MATS6p: Moran autocorrelation - lag 6 / weighted by polarizabilities * MATS7p: Moran autocorrelation - lag 7 / weighted by polarizabilities * MATS8p: Moran autocorrelation - lag 8 / weighted by polarizabilities * MATS1i: Moran autocorrelation - lag 1 / weighted by first ionization potential * MATS2i: Moran autocorrelation - lag 2 / weighted by first ionization potential * MATS3i: Moran autocorrelation - lag 3 / weighted by first ionization potential * MATS4i: Moran autocorrelation - lag 4 / weighted by first ionization potential * MATS5i: Moran autocorrelation - lag 5 / weighted by first ionization potential * MATS6i: Moran autocorrelation - lag 6 / weighted by first ionization potential * MATS7i: Moran autocorrelation - lag 7 / weighted by first ionization potential * MATS8i: Moran autocorrelation - lag 8 / weighted by first ionization potential * MATS1s: Moran autocorrelation - lag 1 / weighted by I-state * MATS2s: Moran autocorrelation - lag 2 / weighted by I-state * MATS3s: Moran autocorrelation - lag 3 / weighted by I-state * MATS4s: Moran autocorrelation - lag 4 / weighted by I-state * MATS5s: Moran autocorrelation - lag 5 / weighted by I-state * MATS6s: Moran autocorrelation - lag 6 / weighted by I-state * MATS7s: Moran autocorrelation - lag 7 / weighted by I-state * MATS8s: Moran autocorrelation - lag 8 / weighted by I-state * GATS1c: Geary autocorrelation - lag 1 / weighted by charges * GATS2c: Geary autocorrelation - lag 2 / weighted by charges * GATS3c: Geary autocorrelation - lag 3 / weighted by charges * GATS4c: Geary autocorrelation - lag 4 / weighted by charges * GATS5c: Geary autocorrelation - lag 5 / weighted by charges * GATS6c: Geary autocorrelation - lag 6 / weighted by charges * GATS7c: Geary autocorrelation - lag 7 / weighted by charges * GATS8c: Geary autocorrelation - lag 8 / weighted by charges * GATS1m: Geary autocorrelation - lag 1 / weighted by mass * GATS2m: Geary autocorrelation - lag 2 / weighted by mass * GATS3m: Geary autocorrelation - lag 3 / weighted by mass * GATS4m: Geary autocorrelation - lag 4 / weighted by mass * GATS5m: Geary autocorrelation - lag 5 / weighted by mass * GATS6m: Geary autocorrelation - lag 6 / weighted by mass * GATS7m: Geary autocorrelation - lag 7 / weighted by mass * GATS8m: Geary autocorrelation - lag 8 / weighted by mass * GATS1v: Geary autocorrelation - lag 1 / weighted by van der Waals volumes * GATS2v: Geary autocorrelation - lag 2 / weighted by van der Waals volumes * GATS3v: Geary autocorrelation - lag 3 / weighted by van der Waals volumes * GATS4v: Geary autocorrelation - lag 4 / weighted by van der Waals volumes * GATS5v: Geary autocorrelation - lag 5 / weighted by van der Waals volumes * GATS6v: Geary autocorrelation - lag 6 / weighted by van der Waals volumes * GATS7v: Geary autocorrelation - lag 7 / weighted by van der Waals volumes * GATS8v: Geary autocorrelation - lag 8 / weighted by van der Waals volumes * GATS1e: Geary autocorrelation - lag 1 / weighted by Sanderson electronegativities * GATS2e: Geary autocorrelation - lag 2 / weighted by Sanderson electronegativities * GATS3e: Geary autocorrelation - lag 3 / weighted by Sanderson electronegativities * GATS4e: Geary autocorrelation - lag 4 / weighted by Sanderson electronegativities * GATS5e: Geary autocorrelation - lag 5 / weighted by Sanderson electronegativities * GATS6e: Geary autocorrelation - lag 6 / weighted by Sanderson electronegativities * GATS7e: Geary autocorrelation - lag 7 / weighted by Sanderson electronegativities * GATS8e: Geary autocorrelation - lag 8 / weighted by Sanderson electronegativities * GATS1p: Geary autocorrelation - lag 1 / weighted by polarizabilities * GATS2p: Geary autocorrelation - lag 2 / weighted by polarizabilities * GATS3p: Geary autocorrelation - lag 3 / weighted by polarizabilities * GATS4p: Geary autocorrelation - lag 4 / weighted by polarizabilities * GATS5p: Geary autocorrelation - lag 5 / weighted by polarizabilities * GATS6p: Geary autocorrelation - lag 6 / weighted by polarizabilities * GATS7p: Geary autocorrelation - lag 7 / weighted by polarizabilities * GATS8p: Geary autocorrelation - lag 8 / weighted by polarizabilities * GATS1i: Geary autocorrelation - lag 1 / weighted by first ionization potential * GATS2i: Geary autocorrelation - lag 2 / weighted by first ionization potential * GATS3i: Geary autocorrelation - lag 3 / weighted by first ionization potential * GATS4i: Geary autocorrelation - lag 4 / weighted by first ionization potential * GATS5i: Geary autocorrelation - lag 5 / weighted by first ionization potential * GATS6i: Geary autocorrelation - lag 6 / weighted by first ionization potential * GATS7i: Geary autocorrelation - lag 7 / weighted by first ionization potential * GATS8i: Geary autocorrelation - lag 8 / weighted by first ionization potential * GATS1s: Geary autocorrelation - lag 1 / weighted by I-state * GATS2s: Geary autocorrelation - lag 2 / weighted by I-state * GATS3s: Geary autocorrelation - lag 3 / weighted by I-state * GATS4s: Geary autocorrelation - lag 4 / weighted by I-state * GATS5s: Geary autocorrelation - lag 5 / weighted by I-state * GATS6s: Geary autocorrelation - lag 6 / weighted by I-state * GATS7s: Geary autocorrelation - lag 7 / weighted by I-state * GATS8s: Geary autocorrelation - lag 8 / weighted by I-state All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_Autocorrelation3D Autocorrelation3D 3D PaDEL descriptor The following features are calculated: * TDB1u: 3D topological distance based autocorrelation - lag 1 / unweighted * TDB2u: 3D topological distance based autocorrelation - lag 2 / unweighted * TDB3u: 3D topological distance based autocorrelation - lag 3 / unweighted * TDB4u: 3D topological distance based autocorrelation - lag 4 / unweighted * TDB5u: 3D topological distance based autocorrelation - lag 5 / unweighted * TDB6u: 3D topological distance based autocorrelation - lag 6 / unweighted * TDB7u: 3D topological distance based autocorrelation - lag 7 / unweighted * TDB8u: 3D topological distance based autocorrelation - lag 8 / unweighted * TDB9u: 3D topological distance based autocorrelation - lag 9 / unweighted * TDB10u: 3D topological distance based autocorrelation - lag 10 / unweighted * TDB1m: 3D topological distance based autocorrelation - lag 1 / weighted by mass * TDB2m: 3D topological distance based autocorrelation - lag 2 / weighted by mass * TDB3m: 3D topological distance based autocorrelation - lag 3 / weighted by mass * TDB4m: 3D topological distance based autocorrelation - lag 4 / weighted by mass * TDB5m: 3D topological distance based autocorrelation - lag 5 / weighted by mass * TDB6m: 3D topological distance based autocorrelation - lag 6 / weighted by mass * TDB7m: 3D topological distance based autocorrelation - lag 7 / weighted by mass * TDB8m: 3D topological distance based autocorrelation - lag 8 / weighted by mass * TDB9m: 3D topological distance based autocorrelation - lag 9 / weighted by mass * TDB10m: 3D topological distance based autocorrelation - lag 10 / weighted by mass * TDB1v: 3D topological distance based autocorrelation - lag 1 / weighted by van der Waals volumes * TDB2v: 3D topological distance based autocorrelation - lag 2 / weighted by van der Waals volumes * TDB3v: 3D topological distance based autocorrelation - lag 3 / weighted by van der Waals volumes * TDB4v: 3D topological distance based autocorrelation - lag 4 / weighted by van der Waals volumes * TDB5v: 3D topological distance based autocorrelation - lag 5 / weighted by van der Waals volumes * TDB6v: 3D topological distance based autocorrelation - lag 6 / weighted by van der Waals volumes * TDB7v: 3D topological distance based autocorrelation - lag 7 / weighted by van der Waals volumes * TDB8v: 3D topological distance based autocorrelation - lag 8 / weighted by van der Waals volumes * TDB9v: 3D topological distance based autocorrelation - lag 9 / weighted by van der Waals volumes * TDB10v: 3D topological distance based autocorrelation - lag 10 / weighted by van der Waals volumes * TDB1e: 3D topological distance based autocorrelation - lag 1 / weighted by Sanderson electronegativities * TDB2e: 3D topological distance based autocorrelation - lag 2 / weighted by Sanderson electronegativities * TDB3e: 3D topological distance based autocorrelation - lag 3 / weighted by Sanderson electronegativities * TDB4e: 3D topological distance based autocorrelation - lag 4 / weighted by Sanderson electronegativities * TDB5e: 3D topological distance based autocorrelation - lag 5 / weighted by Sanderson electronegativities * TDB6e: 3D topological distance based autocorrelation - lag 6 / weighted by Sanderson electronegativities * TDB7e: 3D topological distance based autocorrelation - lag 7 / weighted by Sanderson electronegativities * TDB8e: 3D topological distance based autocorrelation - lag 8 / weighted by Sanderson electronegativities * TDB9e: 3D topological distance based autocorrelation - lag 9 / weighted by Sanderson electronegativities * TDB10e: 3D topological distance based autocorrelation - lag 10 / weighted by Sanderson electronegativities * TDB1p: 3D topological distance based autocorrelation - lag 1 / weighted by polarizabilities * TDB2p: 3D topological distance based autocorrelation - lag 2 / weighted by polarizabilities * TDB3p: 3D topological distance based autocorrelation - lag 3 / weighted by polarizabilities * TDB4p: 3D topological distance based autocorrelation - lag 4 / weighted by polarizabilities * TDB5p: 3D topological distance based autocorrelation - lag 5 / weighted by polarizabilities * TDB6p: 3D topological distance based autocorrelation - lag 6 / weighted by polarizabilities * TDB7p: 3D topological distance based autocorrelation - lag 7 / weighted by polarizabilities * TDB8p: 3D topological distance based autocorrelation - lag 8 / weighted by polarizabilities * TDB9p: 3D topological distance based autocorrelation - lag 9 / weighted by polarizabilities * TDB10p: 3D topological distance based autocorrelation - lag 10 / weighted by polarizabilities * TDB1i: 3D topological distance based autocorrelation - lag 1 / weighted by first ionization potential * TDB2i: 3D topological distance based autocorrelation - lag 2 / weighted by first ionization potential * TDB3i: 3D topological distance based autocorrelation - lag 3 / weighted by first ionization potential * TDB4i: 3D topological distance based autocorrelation - lag 4 / weighted by first ionization potential * TDB5i: 3D topological distance based autocorrelation - lag 5 / weighted by first ionization potential * TDB6i: 3D topological distance based autocorrelation - lag 6 / weighted by first ionization potential * TDB7i: 3D topological distance based autocorrelation - lag 7 / weighted by first ionization potential * TDB8i: 3D topological distance based autocorrelation - lag 8 / weighted by first ionization potential * TDB9i: 3D topological distance based autocorrelation - lag 9 / weighted by first ionization potential * TDB10i: 3D topological distance based autocorrelation - lag 10 / weighted by first ionization potential * TDB1s: 3D topological distance based autocorrelation - lag 1 / weighted by I-state * TDB2s: 3D topological distance based autocorrelation - lag 2 / weighted by I-state * TDB3s: 3D topological distance based autocorrelation - lag 3 / weighted by I-state * TDB4s: 3D topological distance based autocorrelation - lag 4 / weighted by I-state * TDB5s: 3D topological distance based autocorrelation - lag 5 / weighted by I-state * TDB6s: 3D topological distance based autocorrelation - lag 6 / weighted by I-state * TDB7s: 3D topological distance based autocorrelation - lag 7 / weighted by I-state * TDB8s: 3D topological distance based autocorrelation - lag 8 / weighted by I-state * TDB9s: 3D topological distance based autocorrelation - lag 9 / weighted by I-state * TDB10s: 3D topological distance based autocorrelation - lag 10 / weighted by I-state * TDB1r: 3D topological distance based autocorrelation - lag 1 / weighted by covalent radius * TDB2r: 3D topological distance based autocorrelation - lag 2 / weighted by covalent radius * TDB3r: 3D topological distance based autocorrelation - lag 3 / weighted by covalent radius * TDB4r: 3D topological distance based autocorrelation - lag 4 / weighted by covalent radius * TDB5r: 3D topological distance based autocorrelation - lag 5 / weighted by covalent radius * TDB6r: 3D topological distance based autocorrelation - lag 6 / weighted by covalent radius * TDB7r: 3D topological distance based autocorrelation - lag 7 / weighted by covalent radius * TDB8r: 3D topological distance based autocorrelation - lag 8 / weighted by covalent radius * TDB9r: 3D topological distance based autocorrelation - lag 9 / weighted by covalent radius * TDB10r: 3D topological distance based autocorrelation - lag 10 / weighted by covalent radius All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_BCUT BCUT PaDEL descriptor The following features are calculated: * BCUTw-1l: nhigh lowest atom weighted BCUTS * BCUTw-1h: nlow highest atom weighted BCUTS * BCUTc-1l: nhigh lowest partial charge weighted BCUTS * BCUTc-1h: nlow highest partial charge weighted BCUTS * BCUTp-1l: nhigh lowest polarizability weighted BCUTS * BCUTp-1h: nlow highest polarizability weighted BCUTS All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_BPol BPol PaDEL descriptor The following features are calculated: * bpol: Sum of the absolute value of the difference between atomic polarizabilities of all bonded atoms in the molecule (including implicit hydrogens) All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_BaryszMatrix BaryszMatrix PaDEL descriptor The following features are calculated: * SpAbs_DzZ: Graph energy from Barysz matrix / weighted by atomic number * SpMax_DzZ: Leading eigenvalue from Barysz matrix / weighted by atomic number * SpDiam_DzZ: Spectral diameter from Barysz matrix / weighted by atomic number * SpAD_DzZ: Spectral absolute deviation from Barysz matrix / weighted by atomic number * SpMAD_DzZ: Spectral mean absolute deviation from Barysz matrix / weighted by atomic number * EE_DzZ: Estrada-like index from Barysz matrix / weighted by atomic number (ln(1+x)) * SM1_DzZ: Spectral moment of order 1 from Barysz matrix / weighted by atomic number * VE1_DzZ: Coefficient sum of the last eigenvector from Barysz matrix / weighted by atomic number * VE2_DzZ: Average coefficient sum of the last eigenvector from Barysz matrix / weighted by atomic number * VE3_DzZ: Logarithmic coefficient sum of the last eigenvector from Barysz matrix / weighted by atomic number * VR1_DzZ: Randic-like eigenvector-based index from Barysz matrix / weighted by atomic number * VR2_DzZ: Normalized Randic-like eigenvector-based index from Barysz matrix / weighted by atomic number * VR3_DzZ: Logarithmic Randic-like eigenvector-based index from Barysz matrix / weighted by atomic number * SpAbs_Dzm: Graph energy from Barysz matrix / weighted by mass * SpMax_Dzm: Leading eigenvalue from Barysz matrix / weighted by mass * SpDiam_Dzm: Spectral diameter from Barysz matrix / weighted by mass * SpAD_Dzm: Spectral absolute deviation from Barysz matrix / weighted by mass * SpMAD_Dzm: Spectral mean absolute deviation from Barysz matrix / weighted by mass * EE_Dzm: Estrada-like index from Barysz matrix / weighted by mass (ln(1+x)) * SM1_Dzm: Spectral moment of order 1 from Barysz matrix / weighted by mass * VE1_Dzm: Coefficient sum of the last eigenvector from Barysz matrix / weighted by mass * VE2_Dzm: Average coefficient sum of the last eigenvector from Barysz matrix / weighted by mass * VE3_Dzm: Logarithmic coefficient sum of the last eigenvector from Barysz matrix / weighted by mass * VR1_Dzm: Randic-like eigenvector-based index from Barysz matrix / weighted by mass * VR2_Dzm: Normalized Randic-like eigenvector-based index from Barysz matrix / weighted by mass * VR3_Dzm: Logarithmic Randic-like eigenvector-based index from Barysz matrix / weighted by mass * SpAbs_Dzv: Graph energy from Barysz matrix / weighted by van der Waals volumes * SpMax_Dzv: Leading eigenvalue from Barysz matrix / weighted by van der Waals volumes * SpDiam_Dzv: Spectral diameter from Barysz matrix / weighted by van der Waals volumes * SpAD_Dzv: Spectral absolute deviation from Barysz matrix / weighted by van der Waals volumes * SpMAD_Dzv: Spectral mean absolute deviation from Barysz matrix / weighted by van der Waals volumes * EE_Dzv: Estrada-like index from Barysz matrix / weighted by van der Waals volumes (ln(1+x)) * SM1_Dzv: Spectral moment of order 1 from Barysz matrix / weighted by van der Waals volumes * VE1_Dzv: Coefficient sum of the last eigenvector from Barysz matrix / weighted by van der Waals volumes * VE2_Dzv: Average coefficient sum of the last eigenvector from Barysz matrix / weighted by van der Waals volumes * VE3_Dzv: Logarithmic coefficient sum of the last eigenvector from Barysz matrix / weighted by van der Waals volumes * VR1_Dzv: Randic-like eigenvector-based index from Barysz matrix / weighted by van der Waals volumes * VR2_Dzv: Normalized Randic-like eigenvector-based index from Barysz matrix / weighted by van der Waals volumes * VR3_Dzv: Logarithmic Randic-like eigenvector-based index from Barysz matrix / weighted by van der Waals volumes * SpAbs_Dze: Graph energy from Barysz matrix / weighted by Sanderson electronegativities * SpMax_Dze: Leading eigenvalue from Barysz matrix / weighted by Sanderson electronegativities * SpDiam_Dze: Spectral diameter from Barysz matrix / weighted by Sanderson electronegativities * SpAD_Dze: Spectral absolute deviation from Barysz matrix / weighted by Sanderson electronegativities * SpMAD_Dze: Spectral mean absolute deviation from Barysz matrix / weighted by Sanderson electronegativities * EE_Dze: Estrada-like index from Barysz matrix / weighted by Sanderson electronegativities (ln(1+x)) * SM1_Dze: Spectral moment of order 1 from Barysz matrix / weighted by Sanderson electronegativities * VE1_Dze: Coefficient sum of the last eigenvector from Barysz matrix / weighted by Sanderson electronegativities * VE2_Dze: Average coefficient sum of the last eigenvector from Barysz matrix / weighted by Sanderson electronegativities * VE3_Dze: Logarithmic coefficient sum of the last eigenvector from Barysz matrix / weighted by Sanderson electronegativities * VR1_Dze: Randic-like eigenvector-based index from Barysz matrix / weighted by Sanderson electronegativities * VR2_Dze: Normalized Randic-like eigenvector-based index from Barysz matrix / weighted by Sanderson electronegativities * VR3_Dze: Logarithmic Randic-like eigenvector-based index from Barysz matrix / weighted by Sanderson electronegativities * SpAbs_Dzp: Graph energy from Barysz matrix / weighted by polarizabilities * SpMax_Dzp: Leading eigenvalue from Barysz matrix / weighted by polarizabilities * SpDiam_Dzp: Spectral diameter from Barysz matrix / weighted by polarizabilities * SpAD_Dzp: Spectral absolute deviation from Barysz matrix / weighted by polarizabilities * SpMAD_Dzp: Spectral mean absolute deviation from Barysz matrix / weighted by polarizabilities * EE_Dzp: Estrada-like index from Barysz matrix / weighted by polarizabilities (ln(1+x)) * SM1_Dzp: Spectral moment of order 1 from Barysz matrix / weighted by polarizabilities * VE1_Dzp: Coefficient sum of the last eigenvector from Barysz matrix / weighted by polarizabilities * VE2_Dzp: Average coefficient sum of the last eigenvector from Barysz matrix / weighted by polarizabilities * VE3_Dzp: Logarithmic coefficient sum of the last eigenvector from Barysz matrix / weighted by polarizabilities * VR1_Dzp: Randic-like eigenvector-based index from Barysz matrix / weighted by polarizabilities * VR2_Dzp: Normalized Randic-like eigenvector-based index from Barysz matrix / weighted by polarizabilities * VR3_Dzp: Logarithmic Randic-like eigenvector-based index from Barysz matrix / weighted by polarizabilities * SpAbs_Dzi: Graph energy from Barysz matrix / weighted by first ionization potential * SpMax_Dzi: Leading eigenvalue from Barysz matrix / weighted by first ionization potential * SpDiam_Dzi: Spectral diameter from Barysz matrix / weighted by first ionization potential * SpAD_Dzi: Spectral absolute deviation from Barysz matrix / weighted by first ionization potential * SpMAD_Dzi: Spectral mean absolute deviation from Barysz matrix / weighted by first ionization potential * EE_Dzi: Estrada-like index from Barysz matrix / weighted by first ionization potential (ln(1+x)) * SM1_Dzi: Spectral moment of order 1 from Barysz matrix / weighted by first ionization potential * VE1_Dzi: Coefficient sum of the last eigenvector from Barysz matrix / weighted by first ionization potential * VE2_Dzi: Average coefficient sum of the last eigenvector from Barysz matrix / weighted by first ionization potential * VE3_Dzi: Logarithmic coefficient sum of the last eigenvector from Barysz matrix / weighted by first ionization potential * VR1_Dzi: Randic-like eigenvector-based index from Barysz matrix / weighted by first ionization potential * VR2_Dzi: Normalized Randic-like eigenvector-based index from Barysz matrix / weighted by first ionization potential * VR3_Dzi: Logarithmic Randic-like eigenvector-based index from Barysz matrix / weighted by first ionization potential * SpAbs_Dzs: Graph energy from Barysz matrix / weighted by I-state * SpMax_Dzs: Leading eigenvalue from Barysz matrix / weighted by I-state * SpDiam_Dzs: Spectral diameter from Barysz matrix / weighted by I-state * SpAD_Dzs: Spectral absolute deviation from Barysz matrix / weighted by I-state * SpMAD_Dzs: Spectral mean absolute deviation from Barysz matrix / weighted by I-state * EE_Dzs: Estrada-like index from Barysz matrix / weighted by I-state (ln(1+x)) * SM1_Dzs: Spectral moment of order 1 from Barysz matrix / weighted by I-state * VE1_Dzs: Coefficient sum of the last eigenvector from Barysz matrix / weighted by I-state * VE2_Dzs: Average coefficient sum of the last eigenvector from Barysz matrix / weighted by I-state * VE3_Dzs: Logarithmic coefficient sum of the last eigenvector from Barysz matrix / weighted by I-state * VR1_Dzs: Randic-like eigenvector-based index from Barysz matrix / weighted by I-state * VR2_Dzs: Normalized Randic-like eigenvector-based index from Barysz matrix / weighted by I-state * VR3_Dzs: Logarithmic Randic-like eigenvector-based index from Barysz matrix / weighted by I-state All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_BasicGroupCount BasicGroupCount PaDEL descriptor The following features are calculated: * nBase: Number of basic groups. The list of basic groups is defined by this SMARTS "[$([NH2]-[CX4])]", "[$([NH](-[CX4])-[CX4])]", "[$(N(-[CX4])(-[CX4])-[CX4])]", "[$([* + !$(*~[* -])])]", "[$(N=C-N)]", and "[$(N-C=N)]" originally presented in JOELib All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_BondCount BondCount PaDEL descriptor The following features are calculated: * nBonds: Number of bonds (excluding bonds with hydrogen) * nBonds2: Total number of bonds (including bonds to hydrogens) * nBondsS: Number of single bonds (including bonds with hydrogen) * nBondsS2: Total number of single bonds (including bonds to hydrogens, excluding aromatic bonds) * nBondsS3: Total number of single bonds (excluding bonds to hydrogens and aromatic bonds) * nBondsD: Number of double bonds * nBondsD2: Total number of double bonds (excluding bonds to aromatic bonds) * nBondsT: Number of triple bonds * nBondsQ: Number of quadruple bonds * nBondsM: Total number of bonds that have bond order greater than one (aromatic bonds have bond order 1.5). All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_BurdenModifiedEigenvalues BurdenModifiedEigenvalues PaDEL descriptor The following features are calculated: * SpMax1_Bhm: Largest absolute eigenvalue of Burden modified matrix - n 1 / weighted by relative mass * SpMax2_Bhm: Largest absolute eigenvalue of Burden modified matrix - n 2 / weighted by relative mass * SpMax3_Bhm: Largest absolute eigenvalue of Burden modified matrix - n 3 / weighted by relative mass * SpMax4_Bhm: Largest absolute eigenvalue of Burden modified matrix - n 4 / weighted by relative mass * SpMax5_Bhm: Largest absolute eigenvalue of Burden modified matrix - n 5 / weighted by relative mass * SpMax6_Bhm: Largest absolute eigenvalue of Burden modified matrix - n 6 / weighted by relative mass * SpMax7_Bhm: Largest absolute eigenvalue of Burden modified matrix - n 7 / weighted by relative mass * SpMax8_Bhm: Largest absolute eigenvalue of Burden modified matrix - n 8 / weighted by relative mass * SpMin1_Bhm: Smallest absolute eigenvalue of Burden modified matrix - n 1 / weighted by relative mass * SpMin2_Bhm: Smallest absolute eigenvalue of Burden modified matrix - n 2 / weighted by relative mass * SpMin3_Bhm: Smallest absolute eigenvalue of Burden modified matrix - n 3 / weighted by relative mass * SpMin4_Bhm: Smallest absolute eigenvalue of Burden modified matrix - n 4 / weighted by relative mass * SpMin5_Bhm: Smallest absolute eigenvalue of Burden modified matrix - n 5 / weighted by relative mass * SpMin6_Bhm: Smallest absolute eigenvalue of Burden modified matrix - n 6 / weighted by relative mass * SpMin7_Bhm: Smallest absolute eigenvalue of Burden modified matrix - n 7 / weighted by relative mass * SpMin8_Bhm: Smallest absolute eigenvalue of Burden modified matrix - n 8 / weighted by relative mass * SpMax1_Bhv: Largest absolute eigenvalue of Burden modified matrix - n 1 / weighted by relative van der Waals volumes * SpMax2_Bhv: Largest absolute eigenvalue of Burden modified matrix - n 2 / weighted by relative van der Waals volumes * SpMax3_Bhv: Largest absolute eigenvalue of Burden modified matrix - n 3 / weighted by relative van der Waals volumes * SpMax4_Bhv: Largest absolute eigenvalue of Burden modified matrix - n 4 / weighted by relative van der Waals volumes * SpMax5_Bhv: Largest absolute eigenvalue of Burden modified matrix - n 5 / weighted by relative van der Waals volumes * SpMax6_Bhv: Largest absolute eigenvalue of Burden modified matrix - n 6 / weighted by relative van der Waals volumes * SpMax7_Bhv: Largest absolute eigenvalue of Burden modified matrix - n 7 / weighted by relative van der Waals volumes * SpMax8_Bhv: Largest absolute eigenvalue of Burden modified matrix - n 8 / weighted by relative van der Waals volumes * SpMin1_Bhv: Smallest absolute eigenvalue of Burden modified matrix - n 1 / weighted by relative van der Waals volumes * SpMin2_Bhv: Smallest absolute eigenvalue of Burden modified matrix - n 2 / weighted by relative van der Waals volumes * SpMin3_Bhv: Smallest absolute eigenvalue of Burden modified matrix - n 3 / weighted by relative van der Waals volumes * SpMin4_Bhv: Smallest absolute eigenvalue of Burden modified matrix - n 4 / weighted by relative van der Waals volumes * SpMin5_Bhv: Smallest absolute eigenvalue of Burden modified matrix - n 5 / weighted by relative van der Waals volumes * SpMin6_Bhv: Smallest absolute eigenvalue of Burden modified matrix - n 6 / weighted by relative van der Waals volumes * SpMin7_Bhv: Smallest absolute eigenvalue of Burden modified matrix - n 7 / weighted by relative van der Waals volumes * SpMin8_Bhv: Smallest absolute eigenvalue of Burden modified matrix - n 8 / weighted by relative van der Waals volumes * SpMax1_Bhe: Largest absolute eigenvalue of Burden modified matrix - n 1 / weighted by relative Sanderson electronegativities * SpMax2_Bhe: Largest absolute eigenvalue of Burden modified matrix - n 2 / weighted by relative Sanderson electronegativities * SpMax3_Bhe: Largest absolute eigenvalue of Burden modified matrix - n 3 / weighted by relative Sanderson electronegativities * SpMax4_Bhe: Largest absolute eigenvalue of Burden modified matrix - n 4 / weighted by relative Sanderson electronegativities * SpMax5_Bhe: Largest absolute eigenvalue of Burden modified matrix - n 5 / weighted by relative Sanderson electronegativities * SpMax6_Bhe: Largest absolute eigenvalue of Burden modified matrix - n 6 / weighted by relative Sanderson electronegativities * SpMax7_Bhe: Largest absolute eigenvalue of Burden modified matrix - n 7 / weighted by relative Sanderson electronegativities * SpMax8_Bhe: Largest absolute eigenvalue of Burden modified matrix - n 8 / weighted by relative Sanderson electronegativities * SpMin1_Bhe: Smallest absolute eigenvalue of Burden modified matrix - n 1 / weighted by relative Sanderson electronegativities * SpMin2_Bhe: Smallest absolute eigenvalue of Burden modified matrix - n 2 / weighted by relative Sanderson electronegativities * SpMin3_Bhe: Smallest absolute eigenvalue of Burden modified matrix - n 3 / weighted by relative Sanderson electronegativities * SpMin4_Bhe: Smallest absolute eigenvalue of Burden modified matrix - n 4 / weighted by relative Sanderson electronegativities * SpMin5_Bhe: Smallest absolute eigenvalue of Burden modified matrix - n 5 / weighted by relative Sanderson electronegativities * SpMin6_Bhe: Smallest absolute eigenvalue of Burden modified matrix - n 6 / weighted by relative Sanderson electronegativities * SpMin7_Bhe: Smallest absolute eigenvalue of Burden modified matrix - n 7 / weighted by relative Sanderson electronegativities * SpMin8_Bhe: Smallest absolute eigenvalue of Burden modified matrix - n 8 / weighted by relative Sanderson electronegativities * SpMax1_Bhp: Largest absolute eigenvalue of Burden modified matrix - n 1 / weighted by relative polarizabilities * SpMax2_Bhp: Largest absolute eigenvalue of Burden modified matrix - n 2 / weighted by relative polarizabilities * SpMax3_Bhp: Largest absolute eigenvalue of Burden modified matrix - n 3 / weighted by relative polarizabilities * SpMax4_Bhp: Largest absolute eigenvalue of Burden modified matrix - n 4 / weighted by relative polarizabilities * SpMax5_Bhp: Largest absolute eigenvalue of Burden modified matrix - n 5 / weighted by relative polarizabilities * SpMax6_Bhp: Largest absolute eigenvalue of Burden modified matrix - n 6 / weighted by relative polarizabilities * SpMax7_Bhp: Largest absolute eigenvalue of Burden modified matrix - n 7 / weighted by relative polarizabilities * SpMax8_Bhp: Largest absolute eigenvalue of Burden modified matrix - n 8 / weighted by relative polarizabilities * SpMin1_Bhp: Smallest absolute eigenvalue of Burden modified matrix - n 1 / weighted by relative polarizabilities * SpMin2_Bhp: Smallest absolute eigenvalue of Burden modified matrix - n 2 / weighted by relative polarizabilities * SpMin3_Bhp: Smallest absolute eigenvalue of Burden modified matrix - n 3 / weighted by relative polarizabilities * SpMin4_Bhp: Smallest absolute eigenvalue of Burden modified matrix - n 4 / weighted by relative polarizabilities * SpMin5_Bhp: Smallest absolute eigenvalue of Burden modified matrix - n 5 / weighted by relative polarizabilities * SpMin6_Bhp: Smallest absolute eigenvalue of Burden modified matrix - n 6 / weighted by relative polarizabilities * SpMin7_Bhp: Smallest absolute eigenvalue of Burden modified matrix - n 7 / weighted by relative polarizabilities * SpMin8_Bhp: Smallest absolute eigenvalue of Burden modified matrix - n 8 / weighted by relative polarizabilities * SpMax1_Bhi: Largest absolute eigenvalue of Burden modified matrix - n 1 / weighted by relative first ionization potential * SpMax2_Bhi: Largest absolute eigenvalue of Burden modified matrix - n 2 / weighted by relative first ionization potential * SpMax3_Bhi: Largest absolute eigenvalue of Burden modified matrix - n 3 / weighted by relative first ionization potential * SpMax4_Bhi: Largest absolute eigenvalue of Burden modified matrix - n 4 / weighted by relative first ionization potential * SpMax5_Bhi: Largest absolute eigenvalue of Burden modified matrix - n 5 / weighted by relative first ionization potential * SpMax6_Bhi: Largest absolute eigenvalue of Burden modified matrix - n 6 / weighted by relative first ionization potential * SpMax7_Bhi: Largest absolute eigenvalue of Burden modified matrix - n 7 / weighted by relative first ionization potential * SpMax8_Bhi: Largest absolute eigenvalue of Burden modified matrix - n 8 / weighted by relative first ionization potential * SpMin1_Bhi: Smallest absolute eigenvalue of Burden modified matrix - n 1 / weighted by relative first ionization potential * SpMin2_Bhi: Smallest absolute eigenvalue of Burden modified matrix - n 2 / weighted by relative first ionization potential * SpMin3_Bhi: Smallest absolute eigenvalue of Burden modified matrix - n 3 / weighted by relative first ionization potential * SpMin4_Bhi: Smallest absolute eigenvalue of Burden modified matrix - n 4 / weighted by relative first ionization potential * SpMin5_Bhi: Smallest absolute eigenvalue of Burden modified matrix - n 5 / weighted by relative first ionization potential * SpMin6_Bhi: Smallest absolute eigenvalue of Burden modified matrix - n 6 / weighted by relative first ionization potential * SpMin7_Bhi: Smallest absolute eigenvalue of Burden modified matrix - n 7 / weighted by relative first ionization potential * SpMin8_Bhi: Smallest absolute eigenvalue of Burden modified matrix - n 8 / weighted by relative first ionization potential * SpMax1_Bhs: Largest absolute eigenvalue of Burden modified matrix - n 1 / weighted by relative I-state * SpMax2_Bhs: Largest absolute eigenvalue of Burden modified matrix - n 2 / weighted by relative I-state * SpMax3_Bhs: Largest absolute eigenvalue of Burden modified matrix - n 3 / weighted by relative I-state * SpMax4_Bhs: Largest absolute eigenvalue of Burden modified matrix - n 4 / weighted by relative I-state * SpMax5_Bhs: Largest absolute eigenvalue of Burden modified matrix - n 5 / weighted by relative I-state * SpMax6_Bhs: Largest absolute eigenvalue of Burden modified matrix - n 6 / weighted by relative I-state * SpMax7_Bhs: Largest absolute eigenvalue of Burden modified matrix - n 7 / weighted by relative I-state * SpMax8_Bhs: Largest absolute eigenvalue of Burden modified matrix - n 8 / weighted by relative I-state * SpMin1_Bhs: Smallest absolute eigenvalue of Burden modified matrix - n 1 / weighted by relative I-state * SpMin2_Bhs: Smallest absolute eigenvalue of Burden modified matrix - n 2 / weighted by relative I-state * SpMin3_Bhs: Smallest absolute eigenvalue of Burden modified matrix - n 3 / weighted by relative I-state * SpMin4_Bhs: Smallest absolute eigenvalue of Burden modified matrix - n 4 / weighted by relative I-state * SpMin5_Bhs: Smallest absolute eigenvalue of Burden modified matrix - n 5 / weighted by relative I-state * SpMin6_Bhs: Smallest absolute eigenvalue of Burden modified matrix - n 6 / weighted by relative I-state * SpMin7_Bhs: Smallest absolute eigenvalue of Burden modified matrix - n 7 / weighted by relative I-state * SpMin8_Bhs: Smallest absolute eigenvalue of Burden modified matrix - n 8 / weighted by relative I-state All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_CPSA CPSA 3D PaDEL descriptor The following features are calculated: * PPSA-1: Partial positive surface area -- sum of surface area on positive parts of molecule * PPSA-2: Partial positive surface area * total positive charge on the molecule * PPSA-3: Charge weighted partial positive surface area * PNSA-1: Partial negative surface area -- sum of surface area on negative parts of molecule * PNSA-2: Partial negative surface area * total negative charge on the molecule * PNSA-3: Charge weighted partial negative surface area * DPSA-1: Difference of PPSA-1 and PNSA-1 * DPSA-2: Difference of FPSA-2 and PNSA-2 * DPSA-3: Difference of PPSA-3 and PNSA-3 * FPSA-1: PPSA-1 / total molecular surface area * FPSA-2: PPSA-2 / total molecular surface area * FPSA-3: PPSA-3 / total molecular surface area * FNSA-1: PNSA-1 / total molecular surface area * FNSA-2: PNSA-2 / total molecular surface area * FNSA-3: PNSA-3 / total molecular surface area * WPSA-1: PPSA-1 * total molecular surface area / 1000 * WPSA-2: PPSA-2 * total molecular surface area /1000 * WPSA-3: PPSA-3 * total molecular surface area / 1000 * WNSA-1: PNSA-1 * total molecular surface area /1000 * WNSA-2: PNSA-2 * total molecular surface area / 1000 * WNSA-3: PNSA-3 * total molecular surface area / 1000 * RPCG: Relative positive charge -- most positive charge / total positive charge * RNCG: Relative negative charge -- most negative charge / total negative charge * RPCS: Relative positive charge surface area -- most positive surface area * RPCG * RNCS: Relative negative charge surface area -- most negative surface area * RNCG * THSA: Sum of solvent accessible surface areas of atoms with absolute value of partial charges less than 0.2 * TPSA: Sum of solvent accessible surface areas of atoms with absolute value of partial charges greater than or equal 0.2 * RHSA: THSA / total molecular surface area * RPSA: TPSA / total molecular surface area All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_CarbonTypes CarbonTypes PaDEL descriptor The following features are calculated: * C1SP1: Triply bound carbon bound to one other carbon * C2SP1: Triply bound carbon bound to two other carbons * C1SP2: Doubly bound carbon bound to one other carbon * C2SP2: Doubly bound carbon bound to two other carbons * C3SP2: Doubly bound carbon bound to three other carbons * C1SP3: Singly bound carbon bound to one other carbon * C2SP3: Singly bound carbon bound to two other carbons * C3SP3: Singly bound carbon bound to three other carbons * C4SP3: Singly bound carbon bound to four other carbons All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_ChiChain ChiChain PaDEL descriptor The following features are calculated: * SCH-3: Simple chain, order 3 * SCH-4: Simple chain, order 4 * SCH-5: Simple chain, order 5 * SCH-6: Simple chain, order 6 * SCH-7: Simple chain, order 7 * VCH-3: Valence chain, order 3 * VCH-4: Valence chain, order 4 * VCH-5: Valence chain, order 5 * VCH-6: Valence chain, order 6 * VCH-7: Valence chain, order 7 All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_ChiCluster ChiCluster PaDEL descriptor The following features are calculated: * SC-3: Simple cluster, order 3 * SC-4: Simple cluster, order 4 * SC-5: Simple cluster, order 5 * SC-6: Simple cluster, order 6 * VC-3: Valence cluster, order 3 * VC-4: Valence cluster, order 4 * VC-5: Valence cluster, order 5 * VC-6: Valence cluster, order 6 All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_ChiPath ChiPath PaDEL descriptor The following features are calculated: * SP-0: Simple path, order 0 * SP-1: Simple path, order 1 * SP-2: Simple path, order 2 * SP-3: Simple path, order 3 * SP-4: Simple path, order 4 * SP-5: Simple path, order 5 * SP-6: Simple path, order 6 * SP-7: Simple path, order 7 * ASP-0: Average simple path, order 0 * ASP-1: Average simple path, order 1 * ASP-2: Average simple path, order 2 * ASP-3: Average simple path, order 3 * ASP-4: Average simple path, order 4 * ASP-5: Average simple path, order 5 * ASP-6: Average simple path, order 6 * ASP-7: Average simple path, order 7 * VP-0: Valence path, order 0 * VP-1: Valence path, order 1 * VP-2: Valence path, order 2 * VP-3: Valence path, order 3 * VP-4: Valence path, order 4 * VP-5: Valence path, order 5 * VP-6: Valence path, order 6 * VP-7: Valence path, order 7 * AVP-0: Average valence path, order 0 * AVP-1: Average valence path, order 1 * AVP-2: Average valence path, order 2 * AVP-3: Average valence path, order 3 * AVP-4: Average valence path, order 4 * AVP-5: Average valence path, order 5 * AVP-6: Average valence path, order 6 * AVP-7: Average valence path, order 7 All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_ChiPathCluster ChiPathCluster PaDEL descriptor The following features are calculated: * SPC-4: Simple path cluster, order 4 * SPC-5: Simple path cluster, order 5 * SPC-6: Simple path cluster, order 6 * VPC-4: Valence path cluster, order 4 * VPC-5: Valence path cluster, order 5 * VPC-6: Valence path cluster, order 6 All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_Constitutional Constitutional PaDEL descriptor The following features are calculated: * Sv: Sum of atomic van der Waals volumes (scaled on carbon atom) * Sse: Sum of atomic Sanderson electronegativities (scaled on carbon atom) * Spe: Sum of atomic Pauling electronegativities (scaled on carbon atom) * Sare: Sum of atomic Allred-Rochow electronegativities (scaled on carbon atom) * Sp: Sum of atomic polarizabilities (scaled on carbon atom) * Si: Sum of first first ionization potentials (scaled on carbon atom) * Mv: Mean atomic van der Waals volumes (scaled on carbon atom) * Mse: Mean atomic Sanderson electronegativities (scaled on carbon atom) * Mpe: Mean atomic Pauling electronegativities (scaled on carbon atom) * Mare: Mean atomic Allred-Rochow electronegativities (scaled on carbon atom) * Mp: Mean atomic polarizabilities (scaled on carbon atom) * Mi: Mean first first ionization potentials (scaled on carbon atom) All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_Crippen Crippen PaDEL descriptor The following features are calculated: * CrippenLogP: Crippen's LogP * CrippenMR: Crippen's molar refractivity All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_DetourMatrix DetourMatrix PaDEL descriptor The following features are calculated: * SpMax_Dt: Leading eigenvalue from detour matrix * SpDiam_Dt: Spectral diameter from detour matrix * SpAD_Dt: Spectral absolute deviation from detour matrix * SpMAD_Dt: Spectral mean absolute deviation from detour matrix * EE_Dt: Estrada-like index from detour matrix * VE1_Dt: Coefficient sum of the last eigenvector from detour matrix * VE2_Dt: Average coefficient sum of the last eigenvector from detour matrix * VE3_Dt: Logarithmic coefficient sum of the last eigenvector from detour matrix * VR1_Dt: Randic-like eigenvector-based index from detour matrix * VR2_Dt: Normalized Randic-like eigenvector-based index from detour matrix * VR3_Dt: Logarithmic Randic-like eigenvector-based index from detour matrix All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_EStateFP PaDEL EStateFPfingerprint Estate fingerprint - E-State fragments * Number of bits: 79 * Bit prefix: EStateFP All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. ## Parameters * size (type: Optional; default: None): Description unavailable. * search_depth (type: Optional; default: None): Description unavailable. # padel_EccentricConnectivityIndex EccentricConnectivityIndex PaDEL descriptor The following features are calculated: * ECCEN: A topological descriptor combining distance and adjacency information All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_ElectrotopologicalStateAtomType ElectrotopologicalStateAtomType PaDEL descriptor The following features are calculated: * nHBd: Count of E-States for (strong) Hydrogen Bond donors * nwHBd: Count of E-States for weak Hydrogen Bond donors * nHBa: Count of E-States for (strong) Hydrogen Bond acceptors * nwHBa: Count of E-States for weak Hydrogen Bond acceptors * nHBint2: Count of E-State descriptors of strength for potential Hydrogen Bonds of path length 2 * nHBint3: Count of E-State descriptors of strength for potential Hydrogen Bonds of path length 3 * nHBint4: Count of E-State descriptors of strength for potential Hydrogen Bonds of path length 4 * nHBint5: Count of E-State descriptors of strength for potential Hydrogen Bonds of path length 5 * nHBint6: Count of E-State descriptors of strength for potential Hydrogen Bonds of path length 6 * nHBint7: Count of E-State descriptors of strength for potential Hydrogen Bonds of path length 7 * nHBint8: Count of E-State descriptors of strength for potential Hydrogen Bonds of path length 8 * nHBint9: Count of E-State descriptors of strength for potential Hydrogen Bonds of path length 9 * nHBint10: Count of E-State descriptors of strength for potential Hydrogen Bonds of path length 10 * nHsOH: Count of atom-type H E-State: -OH * nHdNH: Count of atom-type H E-State: =NH * nHsSH: Count of atom-type H E-State: -SH * nHsNH2: Count of atom-type H E-State: -NH2 * nHssNH: Count of atom-type H E-State: -NH- * nHaaNH: Count of atom-type H E-State: :NH: * nHsNH3p: Count of atom-type H E-State: -NH3+ * nHssNH2p: Count of atom-type H E-State: -NH2-+ * nHsssNHp: Count of atom-type H E-State: >NH-+ * nHtCH: Count of atom-type H E-State: #CH * nHdCH2: Count of atom-type H E-State: =CH2 * nHdsCH: Count of atom-type H E-State: =CH- * nHaaCH: Count of atom-type H E-State: :CH: * nHCHnX: Count of atom-type H E-State: CHnX * nHCsats: Count of atom-type H E-State: H bonded to B, Si, P, Ge, As, Se, Sn or Pb * nHCsatu: Count of atom-type H E-State: H on C sp3 bonded to unsaturated C * nHAvin: Count of atom-type H E-State: H on C vinyl bonded to C aromatic * nHother: Count of atom-type H E-State: H on aaCH, dCH2 or dsCH * nHmisc: Count of atom-type H E-State: H bonded to B, Si, P, Ge, As, Se, Sn or Pb * nsLi: Count of atom-type E-State: -Li * nssBe: Count of atom-type E-State: -Be- * nssssBem: Count of atom-type E-State: >Be<-2 * nsBH2: Count of atom-type E-State: -BH2 * nssBH: Count of atom-type E-State: -BH- * nsssB: Count of atom-type E-State: -B< * nssssBm: Count of atom-type E-State: >B<- * nsCH3: Count of atom-type E-State: -CH3 * ndCH2: Count of atom-type E-State: =CH2 * nssCH2: Count of atom-type E-State: -CH2- * ntCH: Count of atom-type E-State: #CH * ndsCH: Count of atom-type E-State: =CH- * naaCH: Count of atom-type E-State: :CH: * nsssCH: Count of atom-type E-State: >CH- * nddC: Count of atom-type E-State: =C= * ntsC: Count of atom-type E-State: #C- * ndssC: Count of atom-type E-State: =C< * naasC: Count of atom-type E-State: :C:- * naaaC: Count of atom-type E-State: ::C: * nssssC: Count of atom-type E-State: >C< * nsNH3p: Count of atom-type E-State: -NH3+ * nsNH2: Count of atom-type E-State: -NH2 * nssNH2p: Count of atom-type E-State: -NH2-+ * ndNH: Count of atom-type E-State: =NH * nssNH: Count of atom-type E-State: -NH- * naaNH: Count of atom-type E-State: :NH: * ntN: Count of atom-type E-State: #N * nsssNHp: Count of atom-type E-State: >NH-+ * ndsN: Count of atom-type E-State: =N- * naaN: Count of atom-type E-State: :N: * nsssN: Count of atom-type E-State: >N- * nddsN: Count of atom-type E-State: -N<< * naasN: Count of atom-type E-State: :N:- * nssssNp: Count of atom-type E-State: >N<+ * nsOH: Count of atom-type E-State: -OH * ndO: Count of atom-type E-State: =O * nssO: Count of atom-type E-State: -O- * naaO: Count of atom-type E-State: :O: * naOm: Count of atom-type E-State: :O-0.5 * nsOm: Count of atom-type E-State: -O- * nsF: Count of atom-type E-State: -F * nsSiH3: Count of atom-type E-State: -SiH3 * nssSiH2: Count of atom-type E-State: -SiH2- * nsssSiH: Count of atom-type E-State: >SiH- * nssssSi: Count of atom-type E-State: >Si< * nsPH2: Count of atom-type E-State: -PH2 * nssPH: Count of atom-type E-State: -PH- * nsssP: Count of atom-type E-State: >P- * ndsssP: Count of atom-type E-State: ->P= * nddsP: Count of atom-type E-State: -=P= * nsssssP: Count of atom-type E-State: ->P< * nsSH: Count of atom-type E-State: -SH * ndS: Count of atom-type E-State: =S * nssS: Count of atom-type E-State: -S- * naaS: Count of atom-type E-State: aSa * ndssS: Count of atom-type E-State: >S= * nddssS: Count of atom-type E-State: >S== * nssssssS: Count of atom-type E-State: >S<< * nSm: Count of atom-type E-State: -S- * nsCl: Count of atom-type E-State: -Cl * nsGeH3: Count of atom-type E-State: -GeH3 * nssGeH2: Count of atom-type E-State: -GeH2- * nsssGeH: Count of atom-type E-State: >GeH- * nssssGe: Count of atom-type E-State: >Ge< * nsAsH2: Count of atom-type E-State: -AsH2 * nssAsH: Count of atom-type E-State: -AsH- * nsssAs: Count of atom-type E-State: >As- * ndsssAs: Count of atom-type E-State: ->As= * nddsAs: Count of atom-type E-State: -=As= * nsssssAs: Count of atom-type E-State: ->As< * nsSeH: Count of atom-type E-State: -SeH * ndSe: Count of atom-type E-State: =Se * nssSe: Count of atom-type E-State: -Se- * naaSe: Count of atom-type E-State: aSea * ndssSe: Count of atom-type E-State: >Se= * nssssssSe: Count of atom-type E-State: >Se<< * nddssSe: Count of atom-type E-State: -=Se=- * nsBr: Count of atom-type E-State: -Br * nsSnH3: Count of atom-type E-State: -SnH3 * nssSnH2: Count of atom-type E-State: -SnH2- * nsssSnH: Count of atom-type E-State: >SnH- * nssssSn: Count of atom-type E-State: >Sn< * nsI: Count of atom-type E-State: -I * nsPbH3: Count of atom-type E-State: -PbH3 * nssPbH2: Count of atom-type E-State: -PbH2- * nsssPbH: Count of atom-type E-State: >PbH- * nssssPb: Count of atom-type E-State: >Pb< * SHBd: Sum of E-States for (strong) hydrogen bond donors * SwHBd: Sum of E-States for weak hydrogen bond donors * SHBa: Sum of E-States for (strong) hydrogen bond acceptors * SwHBa: Sum of E-States for weak hydrogen bond acceptors * SHBint2: Sum of E-State descriptors of strength for potential hydrogen bonds of path length 2 * SHBint3: Sum of E-State descriptors of strength for potential hydrogen bonds of path length 3 * SHBint4: Sum of E-State descriptors of strength for potential hydrogen bonds of path length 4 * SHBint5: Sum of E-State descriptors of strength for potential hydrogen bonds of path length 5 * SHBint6: Sum of E-State descriptors of strength for potential hydrogen bonds of path length 6 * SHBint7: Sum of E-State descriptors of strength for potential hydrogen bonds of path length 7 * SHBint8: Sum of E-State descriptors of strength for potential hydrogen bonds of path length 8 * SHBint9: Sum of E-State descriptors of strength for potential hydrogen bonds of path length 9 * SHBint10: Sum of E-State descriptors of strength for potential hydrogen bonds of path length 10 * SHsOH: Sum of atom-type H E-State: -OH * SHdNH: Sum of atom-type H E-State: =NH * SHsSH: Sum of atom-type H E-State: -SH * SHsNH2: Sum of atom-type H E-State: -NH2 * SHssNH: Sum of atom-type H E-State: -NH- * SHaaNH: Sum of atom-type H E-State: :NH: * SHsNH3p: Sum of atom-type H E-State: -NH3+ * SHssNH2p: Sum of atom-type H E-State: -NH2-+ * SHsssNHp: Sum of atom-type H E-State: >NH-+ * SHtCH: Sum of atom-type H E-State: #CH * SHdCH2: Sum of atom-type H E-State: =CH2 * SHdsCH: Sum of atom-type H E-State: =CH- * SHaaCH: Sum of atom-type H E-State: :CH: * SHCHnX: Sum of atom-type H E-State: CHnX * SHCsats: Sum of atom-type H E-State: H on C sp3 bonded to saturated C * SHCsatu: Sum of atom-type H E-State: H on C sp3 bonded to unsaturated C * SHAvin: Sum of atom-type H E-State: H on C vinyl bonded to C aromatic * SHother: Sum of atom-type H E-State: H on aaCH, dCH2 or dsCH * SHmisc: Sum of atom-type H E-State: H bonded to B, Si, P, Ge, As, Se, Sn or Pb * SsLi: Sum of atom-type E-State: -Li * SssBe: Sum of atom-type E-State: -Be- * SssssBem: Sum of atom-type E-State: >Be<-2 * SsBH2: Sum of atom-type E-State: -BH2 * SssBH: Sum of atom-type E-State: -BH- * SsssB: Sum of atom-type E-State: -B< * SssssBm: Sum of atom-type E-State: >B<- * SsCH3: Sum of atom-type E-State: -CH3 * SdCH2: Sum of atom-type E-State: =CH2 * SssCH2: Sum of atom-type E-State: -CH2- * StCH: Sum of atom-type E-State: #CH * SdsCH: Sum of atom-type E-State: =CH- * SaaCH: Sum of atom-type E-State: :CH: * SsssCH: Sum of atom-type E-State: >CH- * SddC: Sum of atom-type E-State: =C= * StsC: Sum of atom-type E-State: #C- * SdssC: Sum of atom-type E-State: =C< * SaasC: Sum of atom-type E-State: :C:- * SaaaC: Sum of atom-type E-State: ::C: * SssssC: Sum of atom-type E-State: >C< * SsNH3p: Sum of atom-type E-State: -NH3+ * SsNH2: Sum of atom-type E-State: -NH2 * SssNH2p: Sum of atom-type E-State: -NH2-+ * SdNH: Sum of atom-type E-State: =NH * SssNH: Sum of atom-type E-State: -NH- * SaaNH: Sum of atom-type E-State: :NH: * StN: Sum of atom-type E-State: #N * SsssNHp: Sum of atom-type E-State: >NH-+ * SdsN: Sum of atom-type E-State: =N- * SaaN: Sum of atom-type E-State: :N: * SsssN: Sum of atom-type E-State: >N- * SddsN: Sum of atom-type E-State: -N<< * SaasN: Sum of atom-type E-State: :N:- * SssssNp: Sum of atom-type E-State: >N<+ * SsOH: Sum of atom-type E-State: -OH * SdO: Sum of atom-type E-State: =O * SssO: Sum of atom-type E-State: -O- * SaaO: Sum of atom-type E-State: :O: * SaOm: Sum of atom-type E-State: :O-0.5 * SsOm: Sum of atom-type E-State: -O- * SsF: Sum of atom-type E-State: -F * SsSiH3: Sum of atom-type E-State: -SiH3 * SssSiH2: Sum of atom-type E-State: -SiH2- * SsssSiH: Sum of atom-type E-State: >SiH- * SssssSi: Sum of atom-type E-State: >Si< * SsPH2: Sum of atom-type E-State: -PH2 * SssPH: Sum of atom-type E-State: -PH- * SsssP: Sum of atom-type E-State: >P- * SdsssP: Sum of atom-type E-State: ->P= * SddsP: Sum of atom-type E-State: -=P= * SsssssP: Sum of atom-type E-State: ->P< * SsSH: Sum of atom-type E-State: -SH * SdS: Sum of atom-type E-State: =S * SssS: Sum of atom-type E-State: -S- * SaaS: Sum of atom-type E-State: aSa * SdssS: Sum of atom-type E-State: >S= * SddssS: Sum of atom-type E-State: >S== * SssssssS: Sum of atom-type E-State: >S<< * SSm: Sum of atom-type E-State: -S- * SsCl: Sum of atom-type E-State: -Cl * SsGeH3: Sum of atom-type E-State: -GeH3 * SssGeH2: Sum of atom-type E-State: -GeH2- * SsssGeH: Sum of atom-type E-State: >GeH- * SssssGe: Sum of atom-type E-State: >Ge< * SsAsH2: Sum of atom-type E-State: -AsH2 * SssAsH: Sum of atom-type E-State: -AsH- * SsssAs: Sum of atom-type E-State: >As- * SdsssAs: Sum of atom-type E-State: ->As= * SddsAs: Sum of atom-type E-State: -=As= * SsssssAs: Sum of atom-type E-State: ->As< * SsSeH: Sum of atom-type E-State: -SeH * SdSe: Sum of atom-type E-State: =Se * SssSe: Sum of atom-type E-State: -Se- * SaaSe: Sum of atom-type E-State: aSea * SdssSe: Sum of atom-type E-State: >Se= * SssssssSe: Sum of atom-type E-State: >Se<< * SddssSe: Sum of atom-type E-State: -=Se=- * SsBr: Sum of atom-type E-State: -Br * SsSnH3: Sum of atom-type E-State: -SnH3 * SssSnH2: Sum of atom-type E-State: -SnH2- * SsssSnH: Sum of atom-type E-State: >SnH- * SssssSn: Sum of atom-type E-State: >Sn< * SsI: Sum of atom-type E-State: -I * SsPbH3: Sum of atom-type E-State: -PbH3 * SssPbH2: Sum of atom-type E-State: -PbH2- * SsssPbH: Sum of atom-type E-State: >PbH- * SssssPb: Sum of atom-type E-State: >Pb< * minHBd: Minimum E-States for (strong) Hydrogen Bond donors * minwHBd: Minimum E-States for weak Hydrogen Bond donors * minHBa: Minimum E-States for (strong) Hydrogen Bond acceptors * minwHBa: Minimum E-States for weak Hydrogen Bond acceptors * minHBint2: Minimum E-State descriptors of strength for potential Hydrogen Bonds of path length 2 * minHBint3: Minimum E-State descriptors of strength for potential Hydrogen Bonds of path length 3 * minHBint4: Minimum E-State descriptors of strength for potential Hydrogen Bonds of path length 4 * minHBint5: Minimum E-State descriptors of strength for potential Hydrogen Bonds of path length 5 * minHBint6: Minimum E-State descriptors of strength for potential Hydrogen Bonds of path length 6 * minHBint7: Minimum E-State descriptors of strength for potential Hydrogen Bonds of path length 7 * minHBint8: Minimum E-State descriptors of strength for potential Hydrogen Bonds of path length 8 * minHBint9: Minimum E-State descriptors of strength for potential Hydrogen Bonds of path length 9 * minHBint10: Minimum E-State descriptors of strength for potential Hydrogen Bonds of path length 10 * minHsOH: Minimum atom-type H E-State: -OH * minHdNH: Minimum atom-type H E-State: =NH * minHsSH: Minimum atom-type H E-State: -SH * minHsNH2: Minimum atom-type H E-State: -NH2 * minHssNH: Minimum atom-type H E-State: -NH- * minHaaNH: Minimum atom-type H E-State: :NH: * minHsNH3p: Minimum atom-type H E-State: -NH3+ * minHssNH2p: Minimum atom-type H E-State: -NH2-+ * minHsssNHp: Minimum atom-type H E-State: >NH-+ * minHtCH: Minimum atom-type H E-State: #CH * minHdCH2: Minimum atom-type H E-State: =CH2 * minHdsCH: Minimum atom-type H E-State: =CH- * minHaaCH: Minimum atom-type H E-State: :CH: * minHCHnX: Minimum atom-type H E-State: CHnX * minHCsats: Minimum atom-type H E-State: H bonded to B, Si, P, Ge, As, Se, Sn or Pb * minHCsatu: Minimum atom-type H E-State: H on C sp3 bonded to unsaturated C * minHAvin: Minimum atom-type H E-State: H on C vinyl bonded to C aromatic * minHother: Minimum atom-type H E-State: H on aaCH, dCH2 or dsCH * minHmisc: Minimum atom-type H E-State: H bonded to B, Si, P, Ge, As, Se, Sn or Pb * minsLi: Minimum atom-type E-State: -Li * minssBe: Minimum atom-type E-State: -Be- * minssssBem: Minimum atom-type E-State: >Be<-2 * minsBH2: Minimum atom-type E-State: -BH2 * minssBH: Minimum atom-type E-State: -BH- * minsssB: Minimum atom-type E-State: -B< * minssssBm: Minimum atom-type E-State: >B<- * minsCH3: Minimum atom-type E-State: -CH3 * mindCH2: Minimum atom-type E-State: =CH2 * minssCH2: Minimum atom-type E-State: -CH2- * mintCH: Minimum atom-type E-State: #CH * mindsCH: Minimum atom-type E-State: =CH- * minaaCH: Minimum atom-type E-State: :CH: * minsssCH: Minimum atom-type E-State: >CH- * minddC: Minimum atom-type E-State: =C= * mintsC: Minimum atom-type E-State: #C- * mindssC: Minimum atom-type E-State: =C< * minaasC: Minimum atom-type E-State: :C:- * minaaaC: Minimum atom-type E-State: ::C: * minssssC: Minimum atom-type E-State: >C< * minsNH3p: Minimum atom-type E-State: -NH3+ * minsNH2: Minimum atom-type E-State: -NH2 * minssNH2p: Minimum atom-type E-State: -NH2-+ * mindNH: Minimum atom-type E-State: =NH * minssNH: Minimum atom-type E-State: -NH- * minaaNH: Minimum atom-type E-State: :NH: * mintN: Minimum atom-type E-State: #N * minsssNHp: Minimum atom-type E-State: >NH-+ * mindsN: Minimum atom-type E-State: =N- * minaaN: Minimum atom-type E-State: :N: * minsssN: Minimum atom-type E-State: >N- * minddsN: Minimum atom-type E-State: -N<< * minaasN: Minimum atom-type E-State: :N:- * minssssNp: Minimum atom-type E-State: >N<+ * minsOH: Minimum atom-type E-State: -OH * mindO: Minimum atom-type E-State: =O * minssO: Minimum atom-type E-State: -O- * minaaO: Minimum atom-type E-State: :O: * minaOm: Minimum atom-type E-State: :O-0.5 * minsOm: Minimum atom-type E-State: -O- * minsF: Minimum atom-type E-State: -F * minsSiH3: Minimum atom-type E-State: -SiH3 * minssSiH2: Minimum atom-type E-State: -SiH2- * minsssSiH: Minimum atom-type E-State: >SiH- * minssssSi: Minimum atom-type E-State: >Si< * minsPH2: Minimum atom-type E-State: -PH2 * minssPH: Minimum atom-type E-State: -PH- * minsssP: Minimum atom-type E-State: >P- * mindsssP: Minimum atom-type E-State: ->P= * minddsP: Minimum atom-type E-State: -=P= * minsssssP: Minimum atom-type E-State: ->P< * minsSH: Minimum atom-type E-State: -SH * mindS: Minimum atom-type E-State: =S * minssS: Minimum atom-type E-State: -S- * minaaS: Minimum atom-type E-State: aSa * mindssS: Minimum atom-type E-State: >S= * minddssS: Minimum atom-type E-State: >S== * minssssssS: Minimum atom-type E-State: >S<< * minSm: Minimum atom-type E-State: -S- * minsCl: Minimum atom-type E-State: -Cl * minsGeH3: Minimum atom-type E-State: -GeH3 * minssGeH2: Minimum atom-type E-State: -GeH2- * minsssGeH: Minimum atom-type E-State: >GeH- * minssssGe: Minimum atom-type E-State: >Ge< * minsAsH2: Minimum atom-type E-State: -AsH2 * minssAsH: Minimum atom-type E-State: -AsH- * minsssAs: Minimum atom-type E-State: >As- * mindsssAs: Minimum atom-type E-State: ->As= * minddsAs: Minimum atom-type E-State: -=As= * minsssssAs: Minimum atom-type E-State: ->As< * minsSeH: Minimum atom-type E-State: -SeH * mindSe: Minimum atom-type E-State: =Se * minssSe: Minimum atom-type E-State: -Se- * minaaSe: Minimum atom-type E-State: aSea * mindssSe: Minimum atom-type E-State: >Se= * minssssssSe: Minimum atom-type E-State: >Se<< * minddssSe: Minimum atom-type E-State: -=Se=- * minsBr: Minimum atom-type E-State: -Br * minsSnH3: Minimum atom-type E-State: -SnH3 * minssSnH2: Minimum atom-type E-State: -SnH2- * minsssSnH: Minimum atom-type E-State: >SnH- * minssssSn: Minimum atom-type E-State: >Sn< * minsI: Minimum atom-type E-State: -I * minsPbH3: Minimum atom-type E-State: -PbH3 * minssPbH2: Minimum atom-type E-State: -PbH2- * minsssPbH: Minimum atom-type E-State: >PbH- * minssssPb: Minimum atom-type E-State: >Pb< * maxHBd: Maximum E-States for (strong) Hydrogen Bond donors * maxwHBd: Maximum E-States for weak Hydrogen Bond donors * maxHBa: Maximum E-States for (strong) Hydrogen Bond acceptors * maxwHBa: Maximum E-States for weak Hydrogen Bond acceptors * maxHBint2: Maximum E-State descriptors of strength for potential Hydrogen Bonds of path length 2 * maxHBint3: Maximum E-State descriptors of strength for potential Hydrogen Bonds of path length 3 * maxHBint4: Maximum E-State descriptors of strength for potential Hydrogen Bonds of path length 4 * maxHBint5: Maximum E-State descriptors of strength for potential Hydrogen Bonds of path length 5 * maxHBint6: Maximum E-State descriptors of strength for potential Hydrogen Bonds of path length 6 * maxHBint7: Maximum E-State descriptors of strength for potential Hydrogen Bonds of path length 7 * maxHBint8: Maximum E-State descriptors of strength for potential Hydrogen Bonds of path length 8 * maxHBint9: Maximum E-State descriptors of strength for potential Hydrogen Bonds of path length 9 * maxHBint10: Maximum E-State descriptors of strength for potential Hydrogen Bonds of path length 10 * maxHsOH: Maximum atom-type H E-State: -OH * maxHdNH: Maximum atom-type H E-State: =NH * maxHsSH: Maximum atom-type H E-State: -SH * maxHsNH2: Maximum atom-type H E-State: -NH2 * maxHssNH: Maximum atom-type H E-State: -NH- * maxHaaNH: Maximum atom-type H E-State: :NH: * maxHsNH3p: Maximum atom-type H E-State: -NH3+ * maxHssNH2p: Maximum atom-type H E-State: -NH2-+ * maxHsssNHp: Maximum atom-type H E-State: >NH-+ * maxHtCH: Maximum atom-type H E-State: #CH * maxHdCH2: Maximum atom-type H E-State: =CH2 * maxHdsCH: Maximum atom-type H E-State: =CH- * maxHaaCH: Maximum atom-type H E-State: :CH: * maxHCHnX: Maximum atom-type H E-State: CHnX * maxHCsats: Maximum atom-type H E-State: H bonded to B, Si, P, Ge, As, Se, Sn or Pb * maxHCsatu: Maximum atom-type H E-State: H on C sp3 bonded to unsaturated C * maxHAvin: Maximum atom-type H E-State: H on C vinyl bonded to C aromatic * maxHother: Maximum atom-type H E-State: H on aaCH, dCH2 or dsCH * maxHmisc: Maximum atom-type H E-State: H bonded to B, Si, P, Ge, As, Se, Sn or Pb * maxsLi: Maximum atom-type E-State: -Li * maxssBe: Maximum atom-type E-State: -Be- * maxssssBem: Maximum atom-type E-State: >Be<-2 * maxsBH2: Maximum atom-type E-State: -BH2 * maxssBH: Maximum atom-type E-State: -BH- * maxsssB: Maximum atom-type E-State: -B< * maxssssBm: Maximum atom-type E-State: >B<- * maxsCH3: Maximum atom-type E-State: -CH3 * maxdCH2: Maximum atom-type E-State: =CH2 * maxssCH2: Maximum atom-type E-State: -CH2- * maxtCH: Maximum atom-type E-State: #CH * maxdsCH: Maximum atom-type E-State: =CH- * maxaaCH: Maximum atom-type E-State: :CH: * maxsssCH: Maximum atom-type E-State: >CH- * maxddC: Maximum atom-type E-State: =C= * maxtsC: Maximum atom-type E-State: #C- * maxdssC: Maximum atom-type E-State: =C< * maxaasC: Maximum atom-type E-State: :C:- * maxaaaC: Maximum atom-type E-State: ::C: * maxssssC: Maximum atom-type E-State: >C< * maxsNH3p: Maximum atom-type E-State: -NH3+ * maxsNH2: Maximum atom-type E-State: -NH2 * maxssNH2p: Maximum atom-type E-State: -NH2-+ * maxdNH: Maximum atom-type E-State: =NH * maxssNH: Maximum atom-type E-State: -NH- * maxaaNH: Maximum atom-type E-State: :NH: * maxtN: Maximum atom-type E-State: #N * maxsssNHp: Maximum atom-type E-State: >NH-+ * maxdsN: Maximum atom-type E-State: =N- * maxaaN: Maximum atom-type E-State: :N: * maxsssN: Maximum atom-type E-State: >N- * maxddsN: Maximum atom-type E-State: -N<< * maxaasN: Maximum atom-type E-State: :N:- * maxssssNp: Maximum atom-type E-State: >N<+ * maxsOH: Maximum atom-type E-State: -OH * maxdO: Maximum atom-type E-State: =O * maxssO: Maximum atom-type E-State: -O- * maxaaO: Maximum atom-type E-State: :O: * maxaOm: Maximum atom-type E-State: :O-0.5 * maxsOm: Maximum atom-type E-State: -O- * maxsF: Maximum atom-type E-State: -F * maxsSiH3: Maximum atom-type E-State: -SiH3 * maxssSiH2: Maximum atom-type E-State: -SiH2- * maxsssSiH: Maximum atom-type E-State: >SiH- * maxssssSi: Maximum atom-type E-State: >Si< * maxsPH2: Maximum atom-type E-State: -PH2 * maxssPH: Maximum atom-type E-State: -PH- * maxsssP: Maximum atom-type E-State: >P- * maxdsssP: Maximum atom-type E-State: ->P= * maxddsP: Maximum atom-type E-State: -=P= * maxsssssP: Maximum atom-type E-State: ->P< * maxsSH: Maximum atom-type E-State: -SH * maxdS: Maximum atom-type E-State: =S * maxssS: Maximum atom-type E-State: -S- * maxaaS: Maximum atom-type E-State: aSa * maxdssS: Maximum atom-type E-State: >S= * maxddssS: Maximum atom-type E-State: >S== * maxssssssS: Maximum atom-type E-State: >S<< * maxSm: Maximum atom-type E-State: -S- * maxsCl: Maximum atom-type E-State: -Cl * maxsGeH3: Maximum atom-type E-State: -GeH3 * maxssGeH2: Maximum atom-type E-State: -GeH2- * maxsssGeH: Maximum atom-type E-State: >GeH- * maxssssGe: Maximum atom-type E-State: >Ge< * maxsAsH2: Maximum atom-type E-State: -AsH2 * maxssAsH: Maximum atom-type E-State: -AsH- * maxsssAs: Maximum atom-type E-State: >As- * maxdsssAs: Maximum atom-type E-State: ->As= * maxddsAs: Maximum atom-type E-State: -=As= * maxsssssAs: Maximum atom-type E-State: ->As< * maxsSeH: Maximum atom-type E-State: -SeH * maxdSe: Maximum atom-type E-State: =Se * maxssSe: Maximum atom-type E-State: -Se- * maxaaSe: Maximum atom-type E-State: aSea * maxdssSe: Maximum atom-type E-State: >Se= * maxssssssSe: Maximum atom-type E-State: >Se<< * maxddssSe: Maximum atom-type E-State: -=Se=- * maxsBr: Maximum atom-type E-State: -Br * maxsSnH3: Maximum atom-type E-State: -SnH3 * maxssSnH2: Maximum atom-type E-State: -SnH2- * maxsssSnH: Maximum atom-type E-State: >SnH- * maxssssSn: Maximum atom-type E-State: >Sn< * maxsI: Maximum atom-type E-State: -I * maxsPbH3: Maximum atom-type E-State: -PbH3 * maxssPbH2: Maximum atom-type E-State: -PbH2- * maxsssPbH: Maximum atom-type E-State: >PbH- * maxssssPb: Maximum atom-type E-State: >Pb< * sumI: Sum of the intrinsic state values I * meanI: Mean intrinsic state values I * hmax: Maximum H E-State * gmax: Maximum E-State * hmin: Minimum H E-State * gmin: Minimum E-State * LipoaffinityIndex: Lipoaffinity index * MAXDN: Maximum negative intrinsic state di?fference in the molecule (related to the nucleophilicity of the molecule). Using deltaV = (Zv-maxBondedHydrogens) /(atomicNumber-Zv-1). Gramatica, P., Corradi, M., and Consonni, V. (2000). Modelling and prediction of soil sorption coefficients of non-ionic organic pesticides by molecular descriptors. Chemosphere 41, 763-777. * MAXDP: Maximum positive intrinsic state di?fference in the molecule (related to the electrophilicity of the molecule). Using deltaV = (Zv-maxBondedHydrogens) /(atomicNumber-Zv-1). Gramatica, P., Corradi, M., and Consonni, V. (2000). Modelling and prediction of soil sorption coefficients of non-ionic organic pesticides by molecular descriptors. Chemosphere 41, 763-777. * DELS: Sum of all atoms intrinsic state differences (measure of total charge transfer in the molecule). Using deltaV = (Zv-maxBondedHydrogens) /(atomicNumber-Zv-1). Gramatica, P., Corradi, M., and Consonni, V. (2000). Modelling and prediction of soil sorption coefficients of non-ionic organic pesticides by molecular descriptors. Chemosphere 41, 763-777. * MAXDN2: Maximum negative intrinsic state di?fference in the molecule (related to the nucleophilicity of the molecule). Using deltaV = Zv-maxBondedHydrogens. Gramatica, P., Corradi, M., and Consonni, V. (2000). Modelling and prediction of soil sorption coefficients of non-ionic organic pesticides by molecular descriptors. Chemosphere 41, 763-777. * MAXDP2: Maximum positive intrinsic state di?fference in the molecule (related to the electrophilicity of the molecule). Using deltaV = Zv-maxBondedHydrogens. Gramatica, P., Corradi, M., and Consonni, V. (2000). Modelling and prediction of soil sorption coefficients of non-ionic organic pesticides by molecular descriptors. Chemosphere 41, 763-777. * DELS2: Sum of all atoms intrinsic state differences (measure of total charge transfer in the molecule). Using deltaV = Zv-maxBondedHydrogens. Gramatica, P., Corradi, M., and Consonni, V. (2000). Modelling and prediction of soil sorption coefficients of non-ionic organic pesticides by molecular descriptors. Chemosphere 41, 763-777. All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_ExtFP PaDEL ExtFPfingerprint CDK extended fingerprint - Extends the Fingerprinter with additional bits describing ring features * Number of bits: 1024 * Bit prefix: ExtFP All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. ## Parameters * size (type: Optional; default: None): Description unavailable. * search_depth (type: Optional; default: None): Description unavailable. # padel_ExtendedTopochemicalAtom ExtendedTopochemicalAtom PaDEL descriptor The following features are calculated: * ETA_Alpha: Sum of alpha values of all non-hydrogen vertices of a molecule * ETA_AlphaP: Sum of alpha values of all non-hydrogen vertices of a molecule relative to molecular size * ETA_dAlpha_A: A measure of count of non-hydrogen heteroatoms * ETA_dAlpha_B: A measure of count of hydrogen bond acceptor atoms and/or polar surface area * ETA_Epsilon_1: A measure of electronegative atom count * ETA_Epsilon_2: A measure of electronegative atom count * ETA_Epsilon_3: A measure of electronegative atom count * ETA_Epsilon_4: A measure of electronegative atom count * ETA_Epsilon_5: A measure of electronegative atom count * ETA_dEpsilon_A: A measure of contribution of unsaturation and electronegative atom count * ETA_dEpsilon_B: A measure of contribution of unsaturation * ETA_dEpsilon_C: A measure of contribution of electronegativity * ETA_dEpsilon_D: A measure of contribution of hydrogen bond donor atoms * ETA_Psi_1: A measure of hydrogen bonding propensity of the molecules and/or polar surface area * ETA_dPsi_A: A measure of hydrogen bonding propensity of the molecules * ETA_dPsi_B: A measure of hydrogen bonding propensity of the molecules * ETA_Shape_P: Shape index P * ETA_Shape_Y: Shape index Y * ETA_Shape_X: Shape index X * ETA_Beta: A measure of electronic features of the molecule * ETA_BetaP: A measure of electronic features of the molecule relative to molecular size * ETA_Beta_s: A measure of electronegative atom count of the molecule * ETA_BetaP_s: A measure of electronegative atom count of the molecule relative to molecular size * ETA_Beta_ns: A measure of electron-richness of the molecule * ETA_BetaP_ns: A measure of electron-richness of the molecule relative to molecular size * ETA_dBeta: A measure of relative unsaturation content * ETA_dBetaP: A measure of relative unsaturation content relative to molecular size * ETA_Beta_ns_d: A measure of lone electrons entering into resonance * ETA_BetaP_ns_d: A measure of lone electrons entering into resonance relative to molecular size * ETA_Eta: Composite index Eta * ETA_EtaP: Composite index Eta relative to molecular size * ETA_Eta_R: Composite index Eta for reference alkane * ETA_Eta_F: Functionality index EtaF * ETA_EtaP_F: Functionality index EtaF relative to molecular size * ETA_Eta_L: Local index Eta_local * ETA_EtaP_L: Local index Eta_local relative to molecular size * ETA_Eta_R_L: Local index Eta_local for reference alkane * ETA_Eta_F_L: Local functionality contribution EtaF_local * ETA_EtaP_F_L: Local functionality contribution EtaF_local relative to molecular size * ETA_Eta_B: Branching index EtaB * ETA_EtaP_B: Branching index EtaB relative to molecular size * ETA_Eta_B_RC: Branching index EtaB (with ring correction) * ETA_EtaP_B_RC: Branching index EtaB (with ring correction) relative to molecular size All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_FMF FMF PaDEL descriptor The following features are calculated: * FMF: Complexity of a molecule All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_FP PaDEL FPfingerprint CDK fingerprint - Fingerprint of length 1024 and search depth of 8 * Number of bits: nan * Bit prefix: FP All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. ## Parameters * size (type: Optional; default: None): Description unavailable. * search_depth (type: Optional; default: None): Description unavailable. # padel_FragmentComplexity FragmentComplexity PaDEL descriptor The following features are calculated: * fragC: Complexity of a system All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_GraphFP PaDEL GraphFPfingerprint CDK graph only fingerprint - Specialized version of the Fingerprinter which does not take bond orders into account * Number of bits: nan * Bit prefix: GraphFP All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. ## Parameters * size (type: Optional; default: None): Description unavailable. * search_depth (type: Optional; default: None): Description unavailable. # padel_GravitationalIndex GravitationalIndex 3D PaDEL descriptor The following features are calculated: * GRAV-1: Gravitational index of heavy atoms * GRAV-2: Square root of gravitational index of heavy atoms * GRAV-3: Cube root of gravitational index of heavy atoms * GRAVH-1: Gravitational index - hydrogens included * GRAVH-2: Square root of hydrogen-included gravitational index * GRAVH-3: Cube root of hydrogen-included gravitational index * GRAV-4: Gravitational index of all pairs of atoms (not just bonded pairs) * GRAV-5: Square root of gravitational index of all pairs of atoms (not just bonded pairs) * GRAV-6: Cube root of gravitational index of all pairs of atoms (not just bonded pairs) All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_HBondAcceptorCount HBondAcceptorCount PaDEL descriptor The following features are calculated: * nHBAcc: Number of hydrogen bond acceptors (using CDK HBondAcceptorCountDescriptor algorithm) * nHBAcc2: Number of hydrogen bond acceptors (any oxygen any nitrogen where the formal charge of the nitrogen is non-positive (i.e. formal charge <= 0) except a non-aromatic nitrogen that is adjacent to an oxygen and aromatic ring, or an aromatic nitrogen with a hydrogen atom in a ring, or an aromatic nitrogen with 3 neighouring atoms in a ring, or a nitrogen with total bond order >=4 any fluorine) * nHBAcc3: Number of hydrogen bond acceptors (any oxygen any nitrogen where the formal charge of the nitrogen is non-positive (i.e. formal charge <= 0) except a non-aromatic nitrogen that is adjacent to an oxygen and aromatic ring, or an aromatic nitrogen with a hydrogen atom in a ring, or an aromatic nitrogen with 3 neighouring atoms in a ring, or a nitrogen with total bond order >=4, or a nitrogen in an amide bond any fluorine) * nHBAcc_Lipinski: Number of hydrogen bond acceptors (using Lipinski's definition: any nitrogen any oxygen) All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_HBondDonorCount HBondDonorCount PaDEL descriptor The following features are calculated: * nHBDon: Number of hydrogen bond donors (using CDK HBondDonorCountDescriptor algorithm) * nHBDon_Lipinski: Number of hydrogen bond donors (using Lipinski's definition: Any OH or NH. Each available hydrogen atom is counted as one hydrogen bond donor) All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_HybridizationRatio HybridizationRatio PaDEL descriptor The following features are calculated: * HybRatio: Fraction of sp3 carbons to sp2 carbons All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_InformationContent InformationContent PaDEL descriptor The following features are calculated: * IC0: Information content index (neighborhood symmetry of 0-order) * IC1: Information content index (neighborhood symmetry of 1-order) * IC2: Information content index (neighborhood symmetry of 2-order) * IC3: Information content index (neighborhood symmetry of 3-order) * IC4: Information content index (neighborhood symmetry of 4-order) * IC5: Information content index (neighborhood symmetry of 5-order) * TIC0: Total information content index (neighborhood symmetry of 0-order) * TIC1: Total information content index (neighborhood symmetry of 1-order) * TIC2: Total information content index (neighborhood symmetry of 2-order) * TIC3: Total information content index (neighborhood symmetry of 3-order) * TIC4: Total information content index (neighborhood symmetry of 4-order) * TIC5: Total information content index (neighborhood symmetry of 5-order) * SIC0: Structural information content index (neighborhood symmetry of 0-order) * SIC1: Structural information content index (neighborhood symmetry of 1-order) * SIC2: Structural information content index (neighborhood symmetry of 2-order) * SIC3: Structural information content index (neighborhood symmetry of 3-order) * SIC4: Structural information content index (neighborhood symmetry of 4-order) * SIC5: Structural information content index (neighborhood symmetry of 5-order) * CIC0: Complementary information content index (neighborhood symmetry of 0-order) * CIC1: Complementary information content index (neighborhood symmetry of 1-order) * CIC2: Complementary information content index (neighborhood symmetry of 2-order) * CIC3: Complementary information content index (neighborhood symmetry of 3-order) * CIC4: Complementary information content index (neighborhood symmetry of 4-order) * CIC5: Complementary information content index (neighborhood symmetry of 5-order) * BIC0: Bond information content index (neighborhood symmetry of 0-order) * BIC1: Bond information content index (neighborhood symmetry of 1-order) * BIC2: Bond information content index (neighborhood symmetry of 2-order) * BIC3: Bond information content index (neighborhood symmetry of 3-order) * BIC4: Bond information content index (neighborhood symmetry of 4-order) * BIC5: Bond information content index (neighborhood symmetry of 5-order) * MIC0: Modified information content index (neighborhood symmetry of 0-order) * MIC1: Modified information content index (neighborhood symmetry of 1-order) * MIC2: Modified information content index (neighborhood symmetry of 2-order) * MIC3: Modified information content index (neighborhood symmetry of 3-order) * MIC4: Modified information content index (neighborhood symmetry of 4-order) * MIC5: Modified information content index (neighborhood symmetry of 5-order) * ZMIC0: Z-modified information content index (neighborhood symmetry of 0-order) * ZMIC1: Z-modified information content index (neighborhood symmetry of 1-order) * ZMIC2: Z-modified information content index (neighborhood symmetry of 2-order) * ZMIC3: Z-modified information content index (neighborhood symmetry of 3-order) * ZMIC4: Z-modified information content index (neighborhood symmetry of 4-order) * ZMIC5: Z-modified information content index (neighborhood symmetry of 5-order) All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_KRFP PaDEL KRFPfingerprint Klekota-Roth fingerprint - Presence of chemical substructures * Number of bits: 4860 * Bit prefix: KRFP All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. ## Parameters * size (type: Optional; default: None): Description unavailable. * search_depth (type: Optional; default: None): Description unavailable. # padel_KRFPC PaDEL KRFPCfingerprint Klekota-Roth fingerprint count - Count of chemical substructures * Number of bits: 4860 * Bit prefix: KRFPC All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. ## Parameters * size (type: Optional; default: None): Description unavailable. * search_depth (type: Optional; default: None): Description unavailable. # padel_KappaShapeIndices KappaShapeIndices PaDEL descriptor The following features are calculated: * Kier1: First kappa shape index * Kier2: Second kappa shape index * Kier3: Third kappa (?) shape index All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_LargestChain LargestChain PaDEL descriptor The following features are calculated: * nAtomLC: Number of atoms in the largest chain All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_LargestPiSystem LargestPiSystem PaDEL descriptor The following features are calculated: * nAtomP: Number of atoms in the largest pi system All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_LengthOverBreadth LengthOverBreadth 3D PaDEL descriptor The following features are calculated: * LOBMAX: The maximum L/B ratio * LOBMIN: The L/B ratio for the rotation that results in the minimum area All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_LongestAliphaticChain LongestAliphaticChain PaDEL descriptor The following features are calculated: * nAtomLAC: Number of atoms in the longest aliphatic chain All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_MACCSFP PaDEL MACCSFPfingerprint MACCS fingerprint - MACCS keys * Number of bits: 166 * Bit prefix: MACCSFP All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. ## Parameters * size (type: Optional; default: None): Description unavailable. * search_depth (type: Optional; default: None): Description unavailable. # padel_MDE MDE PaDEL descriptor The following features are calculated: * MDEC-11: Molecular distance edge between all primary carbons * MDEC-12: Molecular distance edge between all primary and secondary carbons * MDEC-13: Molecular distance edge between all primary and tertiary carbons * MDEC-14: Molecular distance edge between all primary and quaternary carbons * MDEC-22: Molecular distance edge between all secondary carbons * MDEC-23: Molecular distance edge between all secondary and tertiary carbons * MDEC-24: Molecular distance edge between all secondary and quaternary carbons * MDEC-33: Molecular distance edge between all tertiary carbons * MDEC-34: Molecular distance edge between all tertiary and quaternary carbons * MDEC-44: Molecular distance edge between all quaternary carbons * MDEO-11: Molecular distance edge between all primary oxygens * MDEO-12: Molecular distance edge between all primary and secondary oxygens * MDEO-22: Molecular distance edge between all secondary oxygens * MDEN-11: Molecular distance edge between all primary nitrogens * MDEN-12: Molecular distance edge between all primary and secondary nitrogens * MDEN-13: Molecular distance edge between all primary and tertiary niroqens * MDEN-22: Molecular distance edge between all secondary nitroqens * MDEN-23: Molecular distance edge between all secondary and tertiary nitrogens * MDEN-33: Molecular distance edge between all tertiary nitrogens All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_MLFER MLFER PaDEL descriptor The following features are calculated: * MLFER_A: Overall or summation solute hydrogen bond acidity * MLFER_BH: Overall or summation solute hydrogen bond basicity * MLFER_BO: Overall or summation solute hydrogen bond basicity * MLFER_S: Combined dipolarity/polarizability * MLFER_E: Excessive molar refraction * MLFER_L: Solute gas-hexadecane partition coefficient All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_MannholdLogP MannholdLogP PaDEL descriptor The following features are calculated: * MLogP: Mannhold LogP All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_McGowanVolume McGowanVolume PaDEL descriptor The following features are calculated: * McGowan_Volume: McGowan characteristic volume All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_MomentOfInertia MomentOfInertia 3D PaDEL descriptor The following features are calculated: * MOMI-X: Moment of inertia along X axis * MOMI-Y: Moment of inertia along Y axis * MOMI-Z: Moment of inertia along Z axis * MOMI-XY: X/Y * MOMI-XZ: X/Z * MOMI-YZ: Y/Z * MOMI-R: Radius of gyration All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_PathCount PathCount PaDEL descriptor The following features are calculated: * MPC2: Molecular path count of order 2 * MPC3: Molecular path count of order 3 * MPC4: Molecular path count of order 4 * MPC5: Molecular path count of order 5 * MPC6: Molecular path count of order 6 * MPC7: Molecular path count of order 7 * MPC8: Molecular path count of order 8 * MPC9: Molecular path count of order 9 * MPC10: Molecular path count of order 10 * TPC: Total path count (up to order 10) * piPC1: Conventional bond order ID number of order 1 (ln(1+x) * piPC2: Conventional bond order ID number of order 2 (ln(1+x) * piPC3: Conventional bond order ID number of order 3 (ln(1+x) * piPC4: Conventional bond order ID number of order 4 (ln(1+x) * piPC5: Conventional bond order ID number of order 5 (ln(1+x) * piPC6: Conventional bond order ID number of order 6 (ln(1+x) * piPC7: Conventional bond order ID number of order 7 (ln(1+x) * piPC8: Conventional bond order ID number of order 8 (ln(1+x) * piPC9: Conventional bond order ID number of order 9 (ln(1+x) * piPC10: Conventional bond order ID number of order 10 (ln(1+x) * TpiPC: Total conventional bond order (up to order 10) (ln(1+x)) * R_TpiPCTPC: Ratio of total conventional bond order (up to order 10) with total path count (up to order 10) All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_PetitjeanNumber PetitjeanNumber PaDEL descriptor The following features are calculated: * PetitjeanNumber: Petitjean number All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_PetitjeanShapeIndex PetitjeanShapeIndex 3D PaDEL descriptor The following features are calculated: * geomRadius: Geometrical radius (minimum geometric eccentricity) * geomDiameter: Geometrical diameter (maximum geometric eccentricity) * geomShape: Petitjean geometric shape index All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_PubchemFP PaDEL PubchemFPfingerprint Pubchem fingerprint - Pubchem fingerprint * Number of bits: 881 * Bit prefix: PubchemFP All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. ## Parameters * size (type: Optional; default: None): Description unavailable. * search_depth (type: Optional; default: None): Description unavailable. # padel_RDF RDF 3D PaDEL descriptor The following features are calculated: * RDF10u: Radial distribution function - 010 / unweighted * RDF15u: Radial distribution function - 015 / unweighted * RDF20u: Radial distribution function - 020 / unweighted * RDF25u: Radial distribution function - 025 / unweighted * RDF30u: Radial distribution function - 030 / unweighted * RDF35u: Radial distribution function - 035 / unweighted * RDF40u: Radial distribution function - 040 / unweighted * RDF45u: Radial distribution function - 045 / unweighted * RDF50u: Radial distribution function - 050 / unweighted * RDF55u: Radial distribution function - 055 / unweighted * RDF60u: Radial distribution function - 060 / unweighted * RDF65u: Radial distribution function - 065 / unweighted * RDF70u: Radial distribution function - 070 / unweighted * RDF75u: Radial distribution function - 075 / unweighted * RDF80u: Radial distribution function - 080 / unweighted * RDF85u: Radial distribution function - 085 / unweighted * RDF90u: Radial distribution function - 090 / unweighted * RDF95u: Radial distribution function - 095 / unweighted * RDF100u: Radial distribution function - 100 / unweighted * RDF105u: Radial distribution function - 105 / unweighted * RDF110u: Radial distribution function - 110 / unweighted * RDF115u: Radial distribution function - 115 / unweighted * RDF120u: Radial distribution function - 120 / unweighted * RDF125u: Radial distribution function - 125 / unweighted * RDF130u: Radial distribution function - 130 / unweighted * RDF135u: Radial distribution function - 135 / unweighted * RDF140u: Radial distribution function - 140 / unweighted * RDF145u: Radial distribution function - 145 / unweighted * RDF150u: Radial distribution function - 150 / unweighted * RDF155u: Radial distribution function - 155 / unweighted * RDF10m: Radial distribution function - 010 / weighted by relative mass * RDF15m: Radial distribution function - 015 / weighted by relative mass * RDF20m: Radial distribution function - 020 / weighted by relative mass * RDF25m: Radial distribution function - 025 / weighted by relative mass * RDF30m: Radial distribution function - 030 / weighted by relative mass * RDF35m: Radial distribution function - 035 / weighted by relative mass * RDF40m: Radial distribution function - 040 / weighted by relative mass * RDF45m: Radial distribution function - 045 / weighted by relative mass * RDF50m: Radial distribution function - 050 / weighted by relative mass * RDF55m: Radial distribution function - 055 / weighted by relative mass * RDF60m: Radial distribution function - 060 / weighted by relative mass * RDF65m: Radial distribution function - 065 / weighted by relative mass * RDF70m: Radial distribution function - 070 / weighted by relative mass * RDF75m: Radial distribution function - 075 / weighted by relative mass * RDF80m: Radial distribution function - 080 / weighted by relative mass * RDF85m: Radial distribution function - 085 / weighted by relative mass * RDF90m: Radial distribution function - 090 / weighted by relative mass * RDF95m: Radial distribution function - 095 / weighted by relative mass * RDF100m: Radial distribution function - 100 / weighted by relative mass * RDF105m: Radial distribution function - 105 / weighted by relative mass * RDF110m: Radial distribution function - 110 / weighted by relative mass * RDF115m: Radial distribution function - 115 / weighted by relative mass * RDF120m: Radial distribution function - 120 / weighted by relative mass * RDF125m: Radial distribution function - 125 / weighted by relative mass * RDF130m: Radial distribution function - 130 / weighted by relative mass * RDF135m: Radial distribution function - 135 / weighted by relative mass * RDF140m: Radial distribution function - 140 / weighted by relative mass * RDF145m: Radial distribution function - 145 / weighted by relative mass * RDF150m: Radial distribution function - 150 / weighted by relative mass * RDF155m: Radial distribution function - 155 / weighted by relative mass * RDF10v: Radial distribution function - 010 / weighted by relative van der Waals volumes * RDF15v: Radial distribution function - 015 / weighted by relative van der Waals volumes * RDF20v: Radial distribution function - 020 / weighted by relative van der Waals volumes * RDF25v: Radial distribution function - 025 / weighted by relative van der Waals volumes * RDF30v: Radial distribution function - 030 / weighted by relative van der Waals volumes * RDF35v: Radial distribution function - 035 / weighted by relative van der Waals volumes * RDF40v: Radial distribution function - 040 / weighted by relative van der Waals volumes * RDF45v: Radial distribution function - 045 / weighted by relative van der Waals volumes * RDF50v: Radial distribution function - 050 / weighted by relative van der Waals volumes * RDF55v: Radial distribution function - 055 / weighted by relative van der Waals volumes * RDF60v: Radial distribution function - 060 / weighted by relative van der Waals volumes * RDF65v: Radial distribution function - 065 / weighted by relative van der Waals volumes * RDF70v: Radial distribution function - 070 / weighted by relative van der Waals volumes * RDF75v: Radial distribution function - 075 / weighted by relative van der Waals volumes * RDF80v: Radial distribution function - 080 / weighted by relative van der Waals volumes * RDF85v: Radial distribution function - 085 / weighted by relative van der Waals volumes * RDF90v: Radial distribution function - 090 / weighted by relative van der Waals volumes * RDF95v: Radial distribution function - 095 / weighted by relative van der Waals volumes * RDF100v: Radial distribution function - 100 / weighted by relative van der Waals volumes * RDF105v: Radial distribution function - 105 / weighted by relative van der Waals volumes * RDF110v: Radial distribution function - 110 / weighted by relative van der Waals volumes * RDF115v: Radial distribution function - 115 / weighted by relative van der Waals volumes * RDF120v: Radial distribution function - 120 / weighted by relative van der Waals volumes * RDF125v: Radial distribution function - 125 / weighted by relative van der Waals volumes * RDF130v: Radial distribution function - 130 / weighted by relative van der Waals volumes * RDF135v: Radial distribution function - 135 / weighted by relative van der Waals volumes * RDF140v: Radial distribution function - 140 / weighted by relative van der Waals volumes * RDF145v: Radial distribution function - 145 / weighted by relative van der Waals volumes * RDF150v: Radial distribution function - 150 / weighted by relative van der Waals volumes * RDF155v: Radial distribution function - 155 / weighted by relative van der Waals volumes * RDF10e: Radial distribution function - 010 / weighted by relative Sanderson electronegativities * RDF15e: Radial distribution function - 015 / weighted by relative Sanderson electronegativities * RDF20e: Radial distribution function - 020 / weighted by relative Sanderson electronegativities * RDF25e: Radial distribution function - 025 / weighted by relative Sanderson electronegativities * RDF30e: Radial distribution function - 030 / weighted by relative Sanderson electronegativities * RDF35e: Radial distribution function - 035 / weighted by relative Sanderson electronegativities * RDF40e: Radial distribution function - 040 / weighted by relative Sanderson electronegativities * RDF45e: Radial distribution function - 045 / weighted by relative Sanderson electronegativities * RDF50e: Radial distribution function - 050 / weighted by relative Sanderson electronegativities * RDF55e: Radial distribution function - 055 / weighted by relative Sanderson electronegativities * RDF60e: Radial distribution function - 060 / weighted by relative Sanderson electronegativities * RDF65e: Radial distribution function - 065 / weighted by relative Sanderson electronegativities * RDF70e: Radial distribution function - 070 / weighted by relative Sanderson electronegativities * RDF75e: Radial distribution function - 075 / weighted by relative Sanderson electronegativities * RDF80e: Radial distribution function - 080 / weighted by relative Sanderson electronegativities * RDF85e: Radial distribution function - 085 / weighted by relative Sanderson electronegativities * RDF90e: Radial distribution function - 090 / weighted by relative Sanderson electronegativities * RDF95e: Radial distribution function - 095 / weighted by relative Sanderson electronegativities * RDF100e: Radial distribution function - 100 / weighted by relative Sanderson electronegativities * RDF105e: Radial distribution function - 105 / weighted by relative Sanderson electronegativities * RDF110e: Radial distribution function - 110 / weighted by relative Sanderson electronegativities * RDF115e: Radial distribution function - 115 / weighted by relative Sanderson electronegativities * RDF120e: Radial distribution function - 120 / weighted by relative Sanderson electronegativities * RDF125e: Radial distribution function - 125 / weighted by relative Sanderson electronegativities * RDF130e: Radial distribution function - 130 / weighted by relative Sanderson electronegativities * RDF135e: Radial distribution function - 135 / weighted by relative Sanderson electronegativities * RDF140e: Radial distribution function - 140 / weighted by relative Sanderson electronegativities * RDF145e: Radial distribution function - 145 / weighted by relative Sanderson electronegativities * RDF150e: Radial distribution function - 150 / weighted by relative Sanderson electronegativities * RDF155e: Radial distribution function - 155 / weighted by relative Sanderson electronegativities * RDF10p: Radial distribution function - 010 / weighted by relative polarizabilities * RDF15p: Radial distribution function - 015 / weighted by relative polarizabilities * RDF20p: Radial distribution function - 020 / weighted by relative polarizabilities * RDF25p: Radial distribution function - 025 / weighted by relative polarizabilities * RDF30p: Radial distribution function - 030 / weighted by relative polarizabilities * RDF35p: Radial distribution function - 035 / weighted by relative polarizabilities * RDF40p: Radial distribution function - 040 / weighted by relative polarizabilities * RDF45p: Radial distribution function - 045 / weighted by relative polarizabilities * RDF50p: Radial distribution function - 050 / weighted by relative polarizabilities * RDF55p: Radial distribution function - 055 / weighted by relative polarizabilities * RDF60p: Radial distribution function - 060 / weighted by relative polarizabilities * RDF65p: Radial distribution function - 065 / weighted by relative polarizabilities * RDF70p: Radial distribution function - 070 / weighted by relative polarizabilities * RDF75p: Radial distribution function - 075 / weighted by relative polarizabilities * RDF80p: Radial distribution function - 080 / weighted by relative polarizabilities * RDF85p: Radial distribution function - 085 / weighted by relative polarizabilities * RDF90p: Radial distribution function - 090 / weighted by relative polarizabilities * RDF95p: Radial distribution function - 095 / weighted by relative polarizabilities * RDF100p: Radial distribution function - 100 / weighted by relative polarizabilities * RDF105p: Radial distribution function - 105 / weighted by relative polarizabilities * RDF110p: Radial distribution function - 110 / weighted by relative polarizabilities * RDF115p: Radial distribution function - 115 / weighted by relative polarizabilities * RDF120p: Radial distribution function - 120 / weighted by relative polarizabilities * RDF125p: Radial distribution function - 125 / weighted by relative polarizabilities * RDF130p: Radial distribution function - 130 / weighted by relative polarizabilities * RDF135p: Radial distribution function - 135 / weighted by relative polarizabilities * RDF140p: Radial distribution function - 140 / weighted by relative polarizabilities * RDF145p: Radial distribution function - 145 / weighted by relative polarizabilities * RDF150p: Radial distribution function - 150 / weighted by relative polarizabilities * RDF155p: Radial distribution function - 155 / weighted by relative polarizabilities * RDF10i: Radial distribution function - 010 / weighted by relative first ionization potential * RDF15i: Radial distribution function - 015 / weighted by relative first ionization potential * RDF20i: Radial distribution function - 020 / weighted by relative first ionization potential * RDF25i: Radial distribution function - 025 / weighted by relative first ionization potential * RDF30i: Radial distribution function - 030 / weighted by relative first ionization potential * RDF35i: Radial distribution function - 035 / weighted by relative first ionization potential * RDF40i: Radial distribution function - 040 / weighted by relative first ionization potential * RDF45i: Radial distribution function - 045 / weighted by relative first ionization potential * RDF50i: Radial distribution function - 050 / weighted by relative first ionization potential * RDF55i: Radial distribution function - 055 / weighted by relative first ionization potential * RDF60i: Radial distribution function - 060 / weighted by relative first ionization potential * RDF65i: Radial distribution function - 065 / weighted by relative first ionization potential * RDF70i: Radial distribution function - 070 / weighted by relative first ionization potential * RDF75i: Radial distribution function - 075 / weighted by relative first ionization potential * RDF80i: Radial distribution function - 080 / weighted by relative first ionization potential * RDF85i: Radial distribution function - 085 / weighted by relative first ionization potential * RDF90i: Radial distribution function - 090 / weighted by relative first ionization potential * RDF95i: Radial distribution function - 095 / weighted by relative first ionization potential * RDF100i: Radial distribution function - 100 / weighted by relative first ionization potential * RDF105i: Radial distribution function - 105 / weighted by relative first ionization potential * RDF110i: Radial distribution function - 110 / weighted by relative first ionization potential * RDF115i: Radial distribution function - 115 / weighted by relative first ionization potential * RDF120i: Radial distribution function - 120 / weighted by relative first ionization potential * RDF125i: Radial distribution function - 125 / weighted by relative first ionization potential * RDF130i: Radial distribution function - 130 / weighted by relative first ionization potential * RDF135i: Radial distribution function - 135 / weighted by relative first ionization potential * RDF140i: Radial distribution function - 140 / weighted by relative first ionization potential * RDF145i: Radial distribution function - 145 / weighted by relative first ionization potential * RDF150i: Radial distribution function - 150 / weighted by relative first ionization potential * RDF155i: Radial distribution function - 155 / weighted by relative first ionization potential * RDF10s: Radial distribution function - 010 / weighted by relative I-state * RDF15s: Radial distribution function - 015 / weighted by relative I-state * RDF20s: Radial distribution function - 020 / weighted by relative I-state * RDF25s: Radial distribution function - 025 / weighted by relative I-state * RDF30s: Radial distribution function - 030 / weighted by relative I-state * RDF35s: Radial distribution function - 035 / weighted by relative I-state * RDF40s: Radial distribution function - 040 / weighted by relative I-state * RDF45s: Radial distribution function - 045 / weighted by relative I-state * RDF50s: Radial distribution function - 050 / weighted by relative I-state * RDF55s: Radial distribution function - 055 / weighted by relative I-state * RDF60s: Radial distribution function - 060 / weighted by relative I-state * RDF65s: Radial distribution function - 065 / weighted by relative I-state * RDF70s: Radial distribution function - 070 / weighted by relative I-state * RDF75s: Radial distribution function - 075 / weighted by relative I-state * RDF80s: Radial distribution function - 080 / weighted by relative I-state * RDF85s: Radial distribution function - 085 / weighted by relative I-state * RDF90s: Radial distribution function - 090 / weighted by relative I-state * RDF95s: Radial distribution function - 095 / weighted by relative I-state * RDF100s: Radial distribution function - 100 / weighted by relative I-state * RDF105s: Radial distribution function - 105 / weighted by relative I-state * RDF110s: Radial distribution function - 110 / weighted by relative I-state * RDF115s: Radial distribution function - 115 / weighted by relative I-state * RDF120s: Radial distribution function - 120 / weighted by relative I-state * RDF125s: Radial distribution function - 125 / weighted by relative I-state * RDF130s: Radial distribution function - 130 / weighted by relative I-state * RDF135s: Radial distribution function - 135 / weighted by relative I-state * RDF140s: Radial distribution function - 140 / weighted by relative I-state * RDF145s: Radial distribution function - 145 / weighted by relative I-state * RDF150s: Radial distribution function - 150 / weighted by relative I-state * RDF155s: Radial distribution function - 155 / weighted by relative I-state All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_RingCount RingCount PaDEL descriptor The following features are calculated: * nRing: Number of rings * n3Ring: Number of 3-membered rings * n4Ring: Number of 4-membered rings * n5Ring: Number of 5-membered rings * n6Ring: Number of 6-membered rings * n7Ring: Number of 7-membered rings * n8Ring: Number of 8-membered rings * n9Ring: Number of 9-membered rings * n10Ring: Number of 10-membered rings * n11Ring: Number of 11-membered rings * n12Ring: Number of 12-membered rings * nG12Ring: Number of >12-membered rings * nFRing: Number of fused rings * nF4Ring: Number of 4-membered fused rings * nF5Ring: Number of 5-membered fused rings * nF6Ring: Number of 6-membered fused rings * nF7Ring: Number of 7-membered fused rings * nF8Ring: Number of 8-membered fused rings * nF9Ring: Number of 9-membered fused rings * nF10Ring: Number of 10-membered fused rings * nF11Ring: Number of 11-membered fused rings * nF12Ring: Number of 12-membered fused rings * nFG12Ring: Number of >12-membered fused rings * nTRing: Number of rings (includes counts from fused rings) * nT4Ring: Number of 4-membered rings (includes counts from fused rings) * nT5Ring: Number of 5-membered rings (includes counts from fused rings) * nT6Ring: Number of 6-membered rings (includes counts from fused rings) * nT7Ring: Number of 7-membered rings (includes counts from fused rings) * nT8Ring: Number of 8-membered rings (includes counts from fused rings) * nT9Ring: Number of 9-membered rings (includes counts from fused rings) * nT10Ring: Number of 10-membered rings (includes counts from fused rings) * nT11Ring: Number of 11-membered rings (includes counts from fused rings) * nT12Ring: Number of 12-membered rings (includes counts from fused rings) * nTG12Ring: Number of >12-membered rings (includes counts from fused rings) * nHeteroRing: Number of rings containing heteroatoms (N, O, P, S, or halogens) * n3HeteroRing: Number of 3-membered rings containing heteroatoms (N, O, P, S, or halogens) * n4HeteroRing: Number of 4-membered rings containing heteroatoms (N, O, P, S, or halogens) * n5HeteroRing: Number of 5-membered rings containing heteroatoms (N, O, P, S, or halogens) * n6HeteroRing: Number of 6-membered rings containing heteroatoms (N, O, P, S, or halogens) * n7HeteroRing: Number of 7-membered rings containing heteroatoms (N, O, P, S, or halogens) * n8HeteroRing: Number of 8-membered rings containing heteroatoms (N, O, P, S, or halogens) * n9HeteroRing: Number of 9-membered rings containing heteroatoms (N, O, P, S, or halogens) * n10HeteroRing: Number of 10-membered rings containing heteroatoms (N, O, P, S, or halogens) * n11HeteroRing: Number of 11-membered rings containing heteroatoms (N, O, P, S, or halogens) * n12HeteroRing: Number of 12-membered rings containing heteroatoms (N, O, P, S, or halogens) * nG12HeteroRing: Number of >12-membered rings containing heteroatoms (N, O, P, S, or halogens) * nFHeteroRing: Number of fused rings containing heteroatoms (N, O, P, S, or halogens) * nF4HeteroRing: Number of 4-membered fused rings containing heteroatoms (N, O, P, S, or halogens) * nF5HeteroRing: Number of 5-membered fused rings containing heteroatoms (N, O, P, S, or halogens) * nF6HeteroRing: Number of 6-membered fused rings containing heteroatoms (N, O, P, S, or halogens) * nF7HeteroRing: Number of 7-membered fused rings containing heteroatoms (N, O, P, S, or halogens) * nF8HeteroRing: Number of 8-membered fused rings containing heteroatoms (N, O, P, S, or halogens) * nF9HeteroRing: Number of 9-membered fused rings containing heteroatoms (N, O, P, S, or halogens) * nF10HeteroRing: Number of 10-membered fused rings containing heteroatoms (N, O, P, S, or halogens) * nF11HeteroRing: Number of 11-membered fused rings containing heteroatoms (N, O, P, S, or halogens) * nF12HeteroRing: Number of 12-membered fused rings containing heteroatoms (N, O, P, S, or halogens) * nFG12HeteroRing: Number of >12-membered fused rings containing heteroatoms (N, O, P, S, or halogens) * nTHeteroRing: Number of rings (includes counts from fused rings) containing heteroatoms (N, O, P, S, or halogens) * nT4HeteroRing: Number of 4-membered rings (includes counts from fused rings) containing heteroatoms (N, O, P, S, or halogens) * nT5HeteroRing: Number of 5-membered rings (includes counts from fused rings) containing heteroatoms (N, O, P, S, or halogens) * nT6HeteroRing: Number of 6-membered rings (includes counts from fused rings) containing heteroatoms (N, O, P, S, or halogens) * nT7HeteroRing: Number of 7-membered rings (includes counts from fused rings) containing heteroatoms (N, O, P, S, or halogens) * nT8HeteroRing: Number of 8-membered rings (includes counts from fused rings) containing heteroatoms (N, O, P, S, or halogens) * nT9HeteroRing: Number of 9-membered rings (includes counts from fused rings) containing heteroatoms (N, O, P, S, or halogens) * nT10HeteroRing: Number of 10-membered rings (includes counts from fused rings) containing heteroatoms (N, O, P, S, or halogens) * nT11HeteroRing: Number of 11-membered rings (includes counts from fused rings) containing heteroatoms (N, O, P, S, or halogens) * nT12HeteroRing: Number of 12-membered rings (includes counts from fused rings) containing heteroatoms (N, O, P, S, or halogens) * nTG12HeteroRing: Number of >12-membered rings (includes counts from fused rings) containing heteroatoms (N, O, P, S, or halogens) All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_RotatableBondsCount RotatableBondsCount PaDEL descriptor The following features are calculated: * nRotB: Number of rotatable bonds, excluding terminal bonds * RotBFrac: Fraction of rotatable bonds, excluding terminal bonds * nRotBt: Number of rotatable bonds, including terminal bonds * RotBtFrac: Fraction of rotatable bonds, including terminal bonds All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_RuleOfFive RuleOfFive PaDEL descriptor The following features are calculated: * LipinskiFailures: Number failures of the Lipinski's Rule Of 5 All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_SubFP PaDEL SubFPfingerprint Substructure fingerprint - Presence of SMARTS Patterns for Functional Group Classification by Christian Laggner * Number of bits: 307 * Bit prefix: SubFP All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. ## Parameters * size (type: Optional; default: None): Description unavailable. * search_depth (type: Optional; default: None): Description unavailable. # padel_SubFPC PaDEL SubFPCfingerprint Substructure fingerprint count - Count of SMARTS Patterns for Functional Group Classification by Christian Laggner * Number of bits: 307 * Bit prefix: SubFPC All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. ## Parameters * size (type: Optional; default: None): Description unavailable. * search_depth (type: Optional; default: None): Description unavailable. # padel_TPSA TPSA PaDEL descriptor The following features are calculated: * TopoPSA: Topological polar surface area All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_Topological Topological PaDEL descriptor The following features are calculated: * topoRadius: Topological radius (minimum atom eccentricity) * topoDiameter: Topological diameter (maximum atom eccentricity) * topoShape: Petitjean topological shape index All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_TopologicalCharge TopologicalCharge PaDEL descriptor The following features are calculated: * GGI1: Topological charge index of order 1 * GGI2: Topological charge index of order 2 * GGI3: Topological charge index of order 3 * GGI4: Topological charge index of order 4 * GGI5: Topological charge index of order 5 * GGI6: Topological charge index of order 6 * GGI7: Topological charge index of order 7 * GGI8: Topological charge index of order 8 * GGI9: Topological charge index of order 9 * GGI10: Topological charge index of order 10 * JGI1: Mean topological charge index of order 1 * JGI2: Mean topological charge index of order 2 * JGI3: Mean topological charge index of order 3 * JGI4: Mean topological charge index of order 4 * JGI5: Mean topological charge index of order 5 * JGI6: Mean topological charge index of order 6 * JGI7: Mean topological charge index of order 7 * JGI8: Mean topological charge index of order 8 * JGI9: Mean topological charge index of order 9 * JGI10: Mean topological charge index of order 10 * JGT: Global topological charge index All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_TopologicalDistanceMatrix TopologicalDistanceMatrix PaDEL descriptor The following features are calculated: * SpMax_D: Leading eigenvalue from topological distance matrix * SpDiam_D: Spectral diameter from topological distance matrix * SpAD_D: Spectral absolute deviation from topological distance matrix * SpMAD_D: Spectral mean absolute deviation from topological distance matrix * EE_D: Estrada-like index from topological distance matrix * VE1_D: Coefficient sum of the last eigenvector from topological distance matrix * VE2_D: Average coefficient sum of the last eigenvector from topological distance matrix * VE3_D: Logarithmic coefficient sum of the last eigenvector from topological distance matrix * VR1_D: Randic-like eigenvector-based index from topological distance matrix * VR2_D: Normalized Randic-like eigenvector-based index from topological distance matrix * VR3_D: Logarithmic Randic-like eigenvector-based index from topological distance matrix All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_VABC VABC PaDEL descriptor The following features are calculated: * VABC: Van der Waals volume calculated using the method proposed in [Zhao, Yuan H. and Abraham, Michael H. and Zissimos, Andreas M., Fast Calculation of van der Waals Volume as a Sum of Atomic and Bond Contributions and Its Application to Drug Compounds, The Journal of Organic Chemistry, 2003, 68:7368-7373] All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_VAdjMa VAdjMa PaDEL descriptor The following features are calculated: * VAdjMat: Vertex adjacency information (magnitude) All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_WHIM WHIM 3D PaDEL descriptor The following features are calculated: * L1u: 1st component size directional WHIM index / unweighted * L2u: 2nd component size directional WHIM index / unweighted * L3u: 3rd component size directional WHIM index / unweighted * P1u: 1st component shape directional WHIM index / unweighted * P2u: 2nd component shape directional WHIM index / unweighted * E1u: 1st component accessibility directional WHIM index / unweighted * E2u: 2nd component accessibility directional WHIM index / unweighted * E3u: 3rd component accessibility directional WHIM index / unweighted * Tu: T total size index / unweighted * Au: A total size index / unweighted * Vu: V total size index / unweighted * Ku: K global shape index / unweighted * Du: D total accessibility index / unweighted * L1m: 1st component size directional WHIM index / weighted by relative mass * L2m: 2nd component size directional WHIM index / weighted by relative mass * L3m: 3rd component size directional WHIM index / weighted by relative mass * P1m: 1st component shape directional WHIM index / weighted by relative mass * P2m: 2nd component shape directional WHIM index / weighted by relative mass * E1m: 1st component accessibility directional WHIM index / weighted by relative mass * E2m: 2nd component accessibility directional WHIM index / weighted by relative mass * E3m: 3rd component accessibility directional WHIM index / weighted by relative mass * Tm: T total size index / weighted by relative mass * Am: A total size index / weighted by relative mass * Vm: V total size index / weighted by relative mass * Km: K global shape index / weighted by relative mass * Dm: D total accessibility index / weighted by relative mass * L1v: 1st component size directional WHIM index / weighted by relative van der Waals volumes * L2v: 2nd component size directional WHIM index / weighted by relative van der Waals volumes * L3v: 3rd component size directional WHIM index / weighted by relative van der Waals volumes * P1v: 1st component shape directional WHIM index / weighted by relative van der Waals volumes * P2v: 2nd component shape directional WHIM index / weighted by relative van der Waals volumes * E1v: 1st component accessibility directional WHIM index / weighted by relative van der Waals volumes * E2v: 2nd component accessibility directional WHIM index / weighted by relative van der Waals volumes * E3v: 3rd component accessibility directional WHIM index / weighted by relative van der Waals volumes * Tv: T total size index / weighted by relative van der Waals volumes * Av: A total size index / weighted by relative van der Waals volumes * Vv: V total size index / weighted by relative van der Waals volumes * Kv: K global shape index / weighted by relative van der Waals volumes * Dv: D total accessibility index / weighted by relative van der Waals volumes * L1e: 1st component size directional WHIM index / weighted by relative Sanderson electronegativities * L2e: 2nd component size directional WHIM index / weighted by relative Sanderson electronegativities * L3e: 3rd component size directional WHIM index / weighted by relative Sanderson electronegativities * P1e: 1st component shape directional WHIM index / weighted by relative Sanderson electronegativities * P2e: 2nd component shape directional WHIM index / weighted by relative Sanderson electronegativities * E1e: 1st component accessibility directional WHIM index / weighted by relative Sanderson electronegativities * E2e: 2nd component accessibility directional WHIM index / weighted by relative Sanderson electronegativities * E3e: 3rd component accessibility directional WHIM index / weighted by relative Sanderson electronegativities * Te: T total size index / weighted by relative Sanderson electronegativities * Ae: A total size index / weighted by relative Sanderson electronegativities * Ve: V total size index / weighted by relative Sanderson electronegativities * Ke: K global shape index / weighted by relative Sanderson electronegativities * De: D total accessibility index / weighted by relative Sanderson electronegativities * L1p: 1st component size directional WHIM index / weighted by relative polarizabilities * L2p: 2nd component size directional WHIM index / weighted by relative polarizabilities * L3p: 3rd component size directional WHIM index / weighted by relative polarizabilities * P1p: 1st component shape directional WHIM index / weighted by relative polarizabilities * P2p: 2nd component shape directional WHIM index / weighted by relative polarizabilities * E1p: 1st component accessibility directional WHIM index / weighted by relative polarizabilities * E2p: 2nd component accessibility directional WHIM index / weighted by relative polarizabilities * E3p: 3rd component accessibility directional WHIM index / weighted by relative polarizabilities * Tp: T total size index / weighted by relative polarizabilities * Ap: A total size index / weighted by relative polarizabilities * Vp: V total size index / weighted by relative polarizabilities * Kp: K global shape index / weighted by relative polarizabilities * Dp: D total accessibility index / weighted by relative polarizabilities * L1i: 1st component size directional WHIM index / weighted by relative first ionization potential * L2i: 2nd component size directional WHIM index / weighted by relative first ionization potential * L3i: 3rd component size directional WHIM index / weighted by relative first ionization potential * P1i: 1st component shape directional WHIM index / weighted by relative first ionization potential * P2i: 2nd component shape directional WHIM index / weighted by relative first ionization potential * E1i: 1st component accessibility directional WHIM index / weighted by relative first ionization potential * E2i: 2nd component accessibility directional WHIM index / weighted by relative first ionization potential * E3i: 3rd component accessibility directional WHIM index / weighted by relative first ionization potential * Ti: T total size index / weighted by relative first ionization potential * Ai: A total size index / weighted by relative first ionization potential * Vi: V total size index / weighted by relative first ionization potential * Ki: K global shape index / weighted by relative first ionization potential * Di: D total accessibility index / weighted by relative first ionization potential * L1s: 1st component size directional WHIM index / weighted by relative I-state * L2s: 2nd component size directional WHIM index / weighted by relative I-state * L3s: 3rd component size directional WHIM index / weighted by relative I-state * P1s: 1st component shape directional WHIM index / weighted by relative I-state * P2s: 2nd component shape directional WHIM index / weighted by relative I-state * E1s: 1st component accessibility directional WHIM index / weighted by relative I-state * E2s: 2nd component accessibility directional WHIM index / weighted by relative I-state * E3s: 3rd component accessibility directional WHIM index / weighted by relative I-state * Ts: T total size index / weighted by relative I-state * As: A total size index / weighted by relative I-state * Vs: V total size index / weighted by relative I-state * Ks: K global shape index / weighted by relative I-state * Ds: D total accessibility index / weighted by relative I-state All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_WalkCount WalkCount PaDEL descriptor The following features are calculated: * MWC2: Molecular walk count of order 2 (ln(1+x) * MWC3: Molecular walk count of order 3 (ln(1+x) * MWC4: Molecular walk count of order 4 (ln(1+x) * MWC5: Molecular walk count of order 5 (ln(1+x) * MWC6: Molecular walk count of order 6 (ln(1+x) * MWC7: Molecular walk count of order 7 (ln(1+x) * MWC8: Molecular walk count of order 8 (ln(1+x) * MWC9: Molecular walk count of order 9 (ln(1+x) * MWC10: Molecular walk count of order 10 (ln(1+x) * TWC: Total walk count (up to order 10) * SRW2: Self-returning walk count of order 2 (ln(1+x) * SRW3: Self-returning walk count of order 3 (ln(1+x) * SRW4: Self-returning walk count of order 4 (ln(1+x) * SRW5: Self-returning walk count of order 5 (ln(1+x) * SRW6: Self-returning walk count of order 6 (ln(1+x) * SRW7: Self-returning walk count of order 7 (ln(1+x) * SRW8: Self-returning walk count of order 8 (ln(1+x) * SRW9: Self-returning walk count of order 9 (ln(1+x) * SRW10: Self-returning walk count of order 10 (ln(1+x) * TSRW: Total self-return walk count (up to order 10) (ln(1+x)) All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_Weight Weight PaDEL descriptor The following features are calculated: * MW: Molecular weight * AMW: Average molecular weight (Molecular weight / Total number of atoms) All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_WeightedPath WeightedPath PaDEL descriptor The following features are calculated: * WTPT-1: Molecular ID * WTPT-2: Molecular ID / number of atoms * WTPT-3: Sum of path lengths starting from heteroatoms * WTPT-4: Sum of path lengths starting from oxygens * WTPT-5: Sum of path lengths starting from nitrogens All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_WienerNumbers WienerNumbers PaDEL descriptor The following features are calculated: * WPATH: Weiner path number * WPOL: Weiner polarity number All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_XLogP XLogP PaDEL descriptor The following features are calculated: * XLogP: XLogP All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # padel_ZagrebIndex ZagrebIndex PaDEL descriptor The following features are calculated: * Zagreb: Sum of the squares of atom degree over all heavy atoms i All values are calculated with the [PaDEL_pywrapper Python package](https://github.com/OlivierBeq/PaDEL_pywrapper) based on [PaDEL-descriptor](https://doi.org/10.1002/jcc.21707). > Yap, Chun Wei. “PaDEL-Descriptor: An Open Source Software to Calculate > Molecular Descriptors and Fingerprints.” Journal of Computational Chemistry > 32, no. 7 (May 2011): 1466–74. https://doi.org/10.1002/jcc.21707. # qed QED feature calculator. Quantitative estimation of drug-likeness features calculated with the [RDKit cheminformatics library](https://www.rdkit.org/docs/source/rdkit.Chem.QED.html) > Bickerton, G. Richard, Gaia V. Paolini, Jérémy Besnard, Sorel Muresan, and > Andrew L. Hopkins. “Quantifying the Chemical Beauty of Drugs.” Nature > Chemistry 4, no. 2 (February 2012): 90–98. > https://doi.org/10.1038/nchem.1243. The following features are calculated: * ALERTS: The number of structural alerts. * ALOGP: The octanol-water partition coefficient. * AROM: The number of aromatic rings. * HBA: The number of hydrogen bond acceptors. * HBD: The number of hydrogen bond donors. * MW: The molecular weight. * PSA: Polar surface area. * ROTB: The number of rotatable bonds. # rdkdesc RDK descriptor feature calculator. Various chemical descriptors calculated with the RDKit cheminformatics library: https://www.rdkit.org/docs/source/rdkit.Chem.Descriptors.html The following features are calculated: * FpDensityMorgan1 * FpDensityMorgan2 * FpDensityMorgan3 * MaxAbsPartialCharge * MaxPartialCharge * MinAbsPartialCharge * MinPartialCharge * NumRadicalElectrons * NumValenceElectron # rdkfp RDK fingerprint feature calculator. RDK topological fingerprint calculated with the [RDKit cheminformatics library](https://www.rdkit.org/docs/source/rdkit.Chem.rdmolops.html#rdkit.Chem.rdmolops.RDKFingerprint Each feature is a single bit of the feature vector. ## Parameters * size (type: int; default: 2048): The fingerprint size. It should be 1024, 2048 or 4096. # secfp SECFP feature calculator. SMILES Extended Connectivity Fingerprint f MinHash Fingerprints (MHFP) / SMILES Extended Connectivity Fingerprints (SECFP) calculated with [RDKit cheminformatics library](https://rdkit.org/docs/source/rdkit.Chem.rdMHFPFingerprint.html). > Probst, Daniel, and Jean-Louis Reymond. “A Probabilistic Molecular > Fingerprint for Big Data Settings.” Journal of Cheminformatics 10, no. 1 > (December 18, 2018): 66. https://doi.org/10.1186/s13321-018-0321-8. Each feature is a single bit of the feature vector. ## Parameters * size (type: int; default: 2048): Description unavailable.