biostudies-literatureUnknown6(11)van Westen GJIn quite a few diseases, drug resistance due to target variability poses a serious problem in pharmacotherapy. This is certainly true for HIV, and hence, it is often unknown which drug is best to use or to develop against an individual HIV strain. In this work we applied 'proteochemometric' modeling of HIV Non-Nucleoside Reverse Transcriptase (NNRTI) inhibitors to support preclinical development by predicting compound performance on multiple mutants in the lead selection stage. Proteochemometric models are based on both small molecule and target properties and can thus capture multi-target activity relationships simultaneously, the targets in this case being a set of 14 HIV Reverse Transcriptase (RT) mutants. We validated our model by experimentally confirming model predictions for 317 untPloS onee27518https://www.ebi.ac.uk/biostudies/studies/S-EPMC3223189biostudies-literatureWhich compound to select in lead optimization? Prospectively validated proteochemometric models guide preclinical development.PMC3223189Vereycken Ivan Vlijmen HWPeeters AIjzerman APGeluykens PKwanten LWegner JKvan Westen GJBender AfalseWhich compound to select in lead optimization? Prospectively validated proteochemometric models guide preclinical development.In quite a few diseases, drug resistance due to target variability poses a serious problem in pharmacotherapy. This is certainly true for HIV, and hence, it is often unknown which drug is best to use or to develop against an individual HIV strain. In this work we applied 'proteochemometric' modeling of HIV Non-Nucleoside Reverse Transcriptase (NNRTI) inhibitors to support preclinical development by predicting compound performance on multiple mutants in the lead selection stage. Proteochemometric models are based on both small molecule and target properties and can thus capture multi-target activity relationships simultaneously, the targets in this case being a set of 14 HIV Reverse Transcriptase (RT) mutants. We validated our model by experimentally confirming model predictions for 317 unt2011-01-01T00:00:00Z20112026-04-15T11:08:11.863Z2026-04-07T13:56:00.65ZS-EPMC32231892213210710.1371/journal.pone.0027518