which compound to select in lead optimization prospectively validated proteochemometric models guide preclinical development在铅化合物选择优化前瞻性验证proteochemometric模型指导临床前开发.pdfVIP

Which Compound to Select in Lead Optimization? Prospectively Validated Proteochemometric Models Guide Preclinical Development 1 ¨ 2 2 2 2 Gerard J. P. van Westen , Jorg K. Wegner , Peggy Geluykens , Leen Kwanten , Inge Vereycken , Anik 2 1 1,2 1,3 Peeters , Adriaan P. IJzerman , Herman W. T. van Vlijmen , Andreas Bender * 1 Division of Medicinal Chemistry, Leiden/Amsterdam Center for Drug Research, Leiden, The Netherlands, 2 Tibotec BVBA, Beerse, Belgium, 3 Unilever Centre for Molecular Science Informatics, University of Cambridge, Cambridge, United Kingdom Abstract 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 untested compound – mutant pairs, with a prediction error comparable with assay variability (RMSE 0.62). Furthermore, dependent on the similarity of a new mutant to the training set, we could predict wit