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Support Vector Regression Scoring of Receptor–Ligand Complexes for Rank-Ordering and Virtual Screening of Chemical Libraries

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† ‡ § ⊥ †Department of Biochemistry and Molecular Biology, ‡Center for Computational Biology and Bioinformatics, §Stark Neurosciences Research Institute, and ⊥Indiana University Cancer Center, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, United States
∥ Department of Chemistry and Chemical Biology, Indiana University−Purdue University, Indianapolis, Indiana, United States
E-mail: [email protected]. Telephone: (317) 274-8315.
Cite this: J. Chem. Inf. Model. 2011, 51, 9, 2132–2138
Publication Date (Web):July 5, 2011
https://doi.org/10.1021/ci200078f
Copyright © 2011 American Chemical Society
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Abstract

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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.

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