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Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

文献类型：期刊论文

作者Ding, Xiaoyu1,2; Cui, Chen1,2; Wang, Dingyan1,2; Zhao, Jihui1,2; Zheng, Mingyue1,2; Luo, Xiaomin1,2; Jiang, Hualiang1,2,3; Chen, Kaixian1,2,3
刊名CURRENT PHARMACEUTICAL DESIGN
出版日期2020
卷号26期号:33页码:4195-4205
关键词Matched molecular pair matched molecular series bioactivity prediction SAR transfer application domain lead optimization
ISSN号1381-6128
DOI10.2174/1381612826666200427111309
通讯作者Zheng, Mingyue(myzheng@simm.ac.cn) ; Luo, Xiaomin(xmluo@simm.ac.cn)
英文摘要Background: Enhancing a compound's biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA BR, SR, and SR BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single ANTS pairs bioactivity prediction model showed the best performance (R-2= 0.828, MAE = 0.406, RMSE- 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R-2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R-2 = 0.842, MAE - 0.397 and RMSE - 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.
WOS关键词PLASMA-PROTEIN BINDING ; APPLICABILITY DOMAIN ; SAR TRANSFER ; QSAR ; REGRESSION ; MODELS
资助项目National Science & Technology Major Project Key New Drug Creation and Manufacturing Program of China[2018ZX09711002] ; National Natural Science Foundation of China[81573351] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA12020372] ; Science and Technology Commission of Shanghai Municipality[18431907100] ; Fudan-SIMM Joint Research Fund, China[FU-SIMM20174007]
WOS研究方向Pharmacology & Pharmacy
语种英语
WOS记录号WOS:000574650000011
出版者BENTHAM SCIENCE PUBL LTD
源URL[http://119.78.100.183/handle/2S10ELR8/291290]  
专题新药研究国家重点实验室
通讯作者Zheng, Mingyue; Luo, Xiaomin
作者单位1.Chinese Acad Sci, Drug Discovery & Design Ctr, Shanghai Inst Mat Med, State Key Lab Drug Res, Shanghai 201203, Peoples R China
2.Univ Chinese Acad Sci, Sch Pharm, Beijing 100049, Peoples R China
3.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 200031, Peoples R China
推荐引用方式
GB/T 7714		 Ding, Xiaoyu,Cui, Chen,Wang, Dingyan,et al. Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods[J]. CURRENT PHARMACEUTICAL DESIGN,2020,26(33):4195-4205.
APA Ding, Xiaoyu.,Cui, Chen.,Wang, Dingyan.,Zhao, Jihui.,Zheng, Mingyue.,...&Chen, Kaixian.(2020).Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods.CURRENT PHARMACEUTICAL DESIGN,26(33),4195-4205.
MLA Ding, Xiaoyu,et al."Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods".CURRENT PHARMACEUTICAL DESIGN 26.33(2020):4195-4205.

入库方式： OAI收割

来源：上海药物研究所

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