J Chem Inf Model - Support vector regression scoring of receptor-ligand complexes for rank-ordering and virtual screening of chemical libraries.

Tópicos

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Resumo

The community structure-activity resource (CSAR) data sets are used to develop and test a support vector machine-based scoring function in regression mode (SVR). Two scoring functions (SVR-KB and SVR-EP) are derived with the objective of reproducing the trend of the experimental binding affinities provided within the two CSAR data sets. The features used to train SVR-KB are knowledge-based pairwise potentials, while SVR-EP is based on physicochemical properties. SVR-KB and SVR-EP were compared to seven other widely used scoring functions, including Glide, X-score, GoldScore, ChemScore, Vina, Dock, and PMF. Results showed that SVR-KB trained with features obtained from three-dimensional complexes of the PDBbind data set outperformed all other scoring functions, including best performing X-score, by nearly 0.1 using three correlation coefficients, namely Pearson, Spearman, and Kendall. It was interesting that higher performance in rank ordering did not translate into greater enrichment in virtual screening assessed using the 40 targets of the Directory of Useful Decoys (DUD). To remedy this situation, a variant of SVR-KB (SVR-KBD) was developed by following a target-specific tailoring strategy that we had previously employed to derive SVM-SP. SVR-KBD showed a much higher enrichment, outperforming all other scoring functions tested, and was comparable in performance to our previously derived scoring function SVM-SP.

Resumo Limpo

communiti structureact resourc csar data set use develop test support vector machinebas score function regress mode svr two score function svrkb svrep deriv object reproduc trend experiment bind affin provid within two csar data set featur use train svrkb knowledgebas pairwis potenti svrep base physicochem properti svrkb svrep compar seven wide use score function includ glide xscore goldscor chemscor vina dock pmf result show svrkb train featur obtain threedimension complex pdbbind data set outperform score function includ best perform xscore near use three correl coeffici name pearson spearman kendal interest higher perform rank order translat greater enrich virtual screen assess use target directori use decoy dud remedi situat variant svrkb svrkbd develop follow targetspecif tailor strategi previous employ deriv svmsp svrkbd show much higher enrich outperform score function test compar perform previous deriv score function svmsp

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