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Optimization of molecular docking scores with support vector rank regression

Authors

  • Wei Wang,

    1. State Key Laboratory of Plant Physiology and Biochemistry, Zhejiang University, Hangzhou 310058, People's Republic of China
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  • Wanlin He,

    1. State Key Laboratory of Plant Physiology and Biochemistry, Zhejiang University, Hangzhou 310058, People's Republic of China
    2. Institute of Biochemistry, Zhejiang University, Hangzhou 310058, People's Republic of China
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    • Wei Wang and Wanlin He contributed equally to this work.

  • Xi Zhou,

    1. State Key Laboratory of Plant Physiology and Biochemistry, Zhejiang University, Hangzhou 310058, People's Republic of China
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  • Xin Chen

    Corresponding author
    1. Institute of Biochemistry, Zhejiang University, Hangzhou 310058, People's Republic of China
    2. Department of Bioinformatics, Zhejiang University, Hangzhou 310058, People's Republic of China
    • State Key Laboratory of Plant Physiology and Biochemistry, Zhejiang University, Hangzhou 310058, People's Republic of China
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Correspondence to: Xin Chen, State Key Laboratory of Plant Physiology and Biochemistry, Zhejiang University, Hangzhou 310058, People's Republic of China. E-mail: xinchen@zju.edu.cn

Abstract

This work introduces the support vector rank regression (SVRR) algorithm for the optimization of molecular docking scores. Seven original docking scores reported by two docking software were integrated by the SVRR algorithm. The resulting SVRR scores showed an average of 12.1% improvement (59.5–66.7%) in binding conformation prediction tests to rank the correctly computed conformation in the first place, along with 16.7% RMSD improvement (2.5414 vs. 2.1162 Å) for the top ranked conformations. In compound library screening (LS) tests, an average of 46.3% improvement (18.2–26.6%) was also observed to rank the correct ligand in the first place. Furthermore, it was shown that SVRR scores trained with different example datasets, using different training strategies, all exhibited exceedingly consistent accuracies, suggesting that the SVRR algorithm is highly robust and generalizable. In contrast, using the same training datasets, traditional support vector classification and regression algorithms failed to improve comparably the accuracy of LS and conformation prediction. These results suggested that, with additional features to indicate the comparative fitness between computed binding conformations, the SVRR algorithm holds the potential to create a new category of more accurate integrative docking scores. Proteins 2013; 81:1386–1398. © 2013 Wiley Periodicals, Inc.

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