Premium
Quantitative Structure‐activity Relationship (QSAR) Models for Docking Score Correction
Author(s) -
Fukunishi Yoshifumi,
Yamasaki Satoshi,
Yasumatsu Isao,
Takeuchi Koh,
Kurosawa Takashi,
Nakamura Haruki
Publication year - 2017
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201600013
Subject(s) - docking (animal) , quantitative structure–activity relationship , linear regression , regression analysis , regression , linear model , artificial intelligence , computer science , mathematics , data mining , machine learning , statistics , medicine , nursing
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 IC 50 , 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.