FIG 3.

A machine learning approach identifies key molecular descriptors for Gram-negative activity of efflux-dependent actives. (A) Molecular descriptors for the 3,780 efflux-dependent actives and a random set of 3,780 inactive molecules (no growth inhibition in E. coli BW25113 ΔtolC) from the primary screen were used to train a random forest model to examine descriptors contributing to Gram-negative activity in efflux-compromised E. coli. (B) The area under the curve-receiver operating characteristic curve (AUC-ROC) plot for the random forest model is 0.808, showing a good distinction between efflux-dependent actives and inactive molecules. Sensitivity refers to the true-positive rate of the model, while specificity refers to its false-positive rate. (C) The top 10 molecular descriptors that reduce the model’s accuracy are shown, with clogD, Fsp³ (fraction of sp³ hybridized carbon atoms), and resonant structure count topping the list.