期刊
MOLECULAR INFORMATICS
卷 36, 期 1-2, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/minf.201600013
关键词
Binding free energy; ChEMBL; Docking score; Protein-compound docking
类别
资金
- National Institute of Advanced Industrial Science and Technology (AIST)
- Japan Agency for Medical Research and Development (AMED)
- Ministry of Economy, Trade, and Industry (METI) of Japan.
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 (similar to 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.
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