Quantitative Structure-activity Relationship (QSAR) Models for Docking Score Correction

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• 作者：Yoshifumi Fukunishi ; Satoshi Yamasaki ; Isao Yasumatsu ; Koh Takeuchi ; Takashi Kurosawa and Haruki Nakamura
• 刊名：Molecular Informatics
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：36
• 期：1-2
• 全文大小：1069K
• ISSN：1868-1751

文摘

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, K_i, 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.