Three-dimensional quantitative structure-activity relationship of human immunodeficiency virus (I) protease inhibitors. 2. Predictive power using limited exploration of alternate binding modes.

NewPred, a semiautomated procedure to evaluate alternate binding modes and assist three dimensional quantitative structure-activity relationship (3D-QSAR) studies in predictive power evaluation is exemplified with a series of 30 human immunodeficiency virus 1 protease (HIV PR) inhibitors. Five comparative molecular field analysis (CoMFA) models (Waller, C. L.; et al. J. Med. Chem. 1993, 36, 4152-4160) based on 59 HIV-PR inhibitors were tested. The test set included 18 compounds (set A) having a different transition state isostere (TSI), hydroxyethylurea (Getman, D. P.; et al. J. Med. Chem. 1993, 36, 288-291), to investigate the binding mode in P1' and P2'. Twelve dihyroxyethylenes (set B) (Thaisrivongs, S.; et al. J. Med. Chem. 1993, 36, 941-952) were used to investigate binding in P2 and P3 as well as in P2' and P3'. Six other compounds with known or inferred binding structure (set C) were part of the test set, but not investigated with NewPred. Each compound was aligned in accordance to predefined alignment rules for the training set prior to the inclusion in the test set (except for set C). Using NewPred, geometrically different conformers for each compound were generated and individually relaxed in the HIV-PR binding site. Energy comparisons allowed selection of lowest energy structures to be included in the test set. Only in vacuo minimized conformers derived from low-energy complexes were used to determine the predictive power of the five models (predictive r2 varied from 0.1 to 0.7 when two chemical and statistical outliers were excluded). Our models correctly predict the poor inhibitor activity of 1(S)-amino-2(R)-hydroxyindan-containing peptides (set B), which is explained and interpreted from a 3D-QSAR perspective. The use of a new, flexibility-based, semiautomated method to explore alternate binding models for 3D-QSAR models is demonstrated.