title: Quantitative Structure-activity Relationship (QSAR) Models for Docking Score Correction.
abstract: In order to improve docking score correction, we developed several structure-based quantitative structure activity relationship (QSAR) models by protein-drug docking simulations and applied these models to public affinity data. The prediction models used descriptor-based regression, and the compound descriptor was a set of docking scores against multiple (∼600) proteins including nontargets. The binding free energy that corresponded to the docking score was approximated by a weighted average of docking scores for multiple proteins, and we tried linear, weighted linear and polynomial regression models considering the compound similarities. In addition, we tried a combination of these regression models for individual data sets such as IC50, Ki, and %inhibition values. The cross-validation results showed that the weighted linear model was more accurate than the simple linear regression model. Thus, the QSAR approaches based on the affinity data of public databases should improve docking scores.
authors.affiliation: Molecular Profiling Research Center for Drug Discovery (molprof), National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26, Aomi, Koto-ku, Tokyo, 135-0064, Japan.
authors.forename: Yoshifumi
authors.initials: Y
authors.lastname: Fukunishi
authors.affiliation: Technology Research Association for Next-Generation Natural Products Chemistry, 2-3-26, Aomi, Koto-ku, Tokyo, 135-0064, Japan.
authors.forename: Satoshi
authors.initials: S
authors.lastname: Yamasaki
authors.affiliation: Technology Research Association for Next-Generation Natural Products Chemistry, 2-3-26, Aomi, Koto-ku, Tokyo, 135-0064, Japan.|Daiichi Sankyo RD Novare Co., Ltd., 1-16-13, Kita-Kasai, Edogawa-ku, Tokyo, 134-8630, Japan.
authors.forename: Isao
authors.initials: I
authors.lastname: Yasumatsu
authors.affiliation: Molecular Profiling Research Center for Drug Discovery (molprof), National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26, Aomi, Koto-ku, Tokyo, 135-0064, Japan.
authors.forename: Koh
authors.initials: K
authors.lastname: Takeuchi
authors.affiliation: Technology Research Association for Next-Generation Natural Products Chemistry, 2-3-26, Aomi, Koto-ku, Tokyo, 135-0064, Japan.|Hitachi Solutions East Japan, 12-1 Ekimaehoncho, Kawasaki-ku, Kanagawa, 210-0007, Japan.
authors.forename: Takashi
authors.initials: T
authors.lastname: Kurosawa
authors.affiliation: Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
authors.forename: Haruki
authors.initials: H
authors.lastname: Nakamura
mesh_terms: D001665:Binding Sites; D062105:Molecular Docking Simulation / Q000379:methods* / Q000592:standards; D011485:Protein Binding; D047428:Protein Kinase Inhibitors / Q000494:pharmacology; D011505:Protein-Tyrosine Kinases / Q000037:antagonists & inhibitors / Q000737:chemistry; D021281:Quantitative Structure-Activity Relationship
chemical_list: D047428:Protein Kinase Inhibitors; D011505:Protein-Tyrosine Kinases
keywords: Binding free energy; ChEMBL; Docking score; Protein-compound docking