期刊
QSAR & COMBINATORIAL SCIENCE
卷 22, 期 1, 页码 113-128出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/qsar.200390001
关键词
3D geometry; CHEM-X; SYBYL; AM1; LDA; BLR; antibacterial activity
For a data set of 661 organic chemicals including many drug-like compounds, discrimination between antibacterial and non-antibacterial activity was modeled using hydrophobicity in terms of the logarithmic octanol/water partition coefficient (log K-ow) and AM1-level molecular descriptors encoding geometric, electrostatic, nucleophilic and electrophilic characteristics of the compounds. Linear discriminant analysis (LDA) and binary logistic regression (BLR) achieved an overall classification rate of around 90%, using two to three variables selected from log K-ow, charged-weighted negative surface area (PNSA-3), positive surface area of heavy atoms (PPSA-1Z), and maximum donor delocalizability (D-max(E)). Model validation was performed using complementary subsets for training and prediction as well as by training the total set with 50% of the activity data allocated wrongly in several arbitrarily selected ways. The discussion includes a comparative analysis of force-field and AM1 geometries as well as of the 3D variation of AM1-level molecular descriptors. Surprisingly, 3D geometry variations have only little impact on the discriminatory performance of the models.
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