The Development of Target-Specific Pose Filter Ensembles To Boost Ligand Enrichment for Structure-Based Virtual Screening

Journal of Chemical Information and Modeling
Jie XiaXiang Simon Wang

Abstract

Structure-based virtual screening (SBVS) has become an indispensable technique for hit identification at the early stage of drug discovery. However, the accuracy of current scoring functions is not high enough to confer success to every target and thus remains to be improved. Previously, we had developed binary pose filters (PFs) using knowledge derived from the protein-ligand interface of a single X-ray structure of a specific target. This novel approach had been validated as an effective way to improve ligand enrichment. Continuing from it, in the present work we attempted to incorporate knowledge collected from diverse protein-ligand interfaces of multiple crystal structures of the same target to build PF ensembles (PFEs). Toward this end, we first constructed a comprehensive data set to meet the requirements of ensemble modeling and validation. This set contains 10 diverse targets, 118 well-prepared X-ray structures of protein-ligand complexes, and large benchmarking actives/decoys sets. Notably, we designed a unique workflow of two-layer classifiers based on the concept of ensemble learning and applied it to the construction of PFEs for all of the targets. Through extensive benchmarking studies, we demonstrated that (1) co...Continue Reading

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Citations

Feb 22, 2018·Journal of Enzyme Inhibition and Medicinal Chemistry·Jie XiaSong Wu
Feb 10, 2018·Journal of Chemical Information and Modeling·Hiromasa Kaneko
Mar 27, 2019·Journal of Chemical Information and Modeling·Wenze LiGuanHua Chen
Feb 13, 2020·Journal of Chemical Information and Modeling·Jie XiaXiang Simon Wang

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