Modeling Discrimination between Antibacterial and Non-Antibacterial Activity based on 3D Molecular Descriptors

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 Kow) 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 Kow, charged‐weighted negative surface area (PNSA‐3), positive surface area of heavy atoms (PPSA‐1Z), and maximum donor delocalizability (DEmax). 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.