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TS ‐Chemscore, a Target‐Specific Scoring Function, Significantly Improves the Performance of Scoring in Virtual Screening
Author(s) -
Wang WenJing,
Huang Qi,
Zou Jun,
Li LinLi,
Yang ShengYong
Publication year - 2015
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/cbdd.12470
Subject(s) - virtual screening , binding affinities , function (biology) , computer science , computational biology , correlation , range (aeronautics) , scoring system , machine learning , artificial intelligence , drug discovery , mathematics , bioinformatics , biology , medicine , biochemistry , genetics , receptor , geometry , materials science , composite material , surgery
Most of the scoring functions currently used in structure‐based drug design belong to ‘universal’ scoring functions, which often give a poor correlation between the calculated scores and experimental binding affinities. In this investigation, we proposed a simple strategy to construct target‐specific scoring functions based on known ‘universal’ scoring functions. This strategy was applied to Chemscore, a widely used empirical scoring function, which led to a new scoring function, termed TS ‐Chemscore. TS ‐Chemscore was validated on 14 protein targets, which cover a wide range of biological target categories. The results showed that TS ‐Chemscore significantly improved the correlation between the calculated scores and experimental binding affinities compared with the original Chemscore. TS ‐Chemscore was then applied in virtual screening to retrieve novel JAK 3 and YopH inhibitors. Top 30 compounds for each target were selected for experimental validation. Six active compounds for JAK 3 and four for YopH were obtained. These compounds were out of the lists of top 30 compounds sorted by Chemscore. Collectively, TS ‐Chemscore established in this study showed a better performance in virtual screening than its counterpart Chemscore.