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Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods

文献类型：期刊论文

作者Wang, Dingyan1,2; Cui, Chen1,2; Ding, Xiaoyu1,2; Xiong, Zhaoping3; Zheng, Mingyue1; Luo, Xiaomin1; Jiang, Hualiang1,3; Chen, Kaixian1,3
刊名FRONTIERS IN PHARMACOLOGY
出版日期2019-08-22
卷号10页码:11
关键词virtual screening target-specific scoring function deep learning drug discovery DUD-E
ISSN号1663-9812
DOI10.3389/fphar.2019.00924
通讯作者Zheng, Mingyue(myzheng@simm.ac.cn) ; Luo, Xiaomin(xmluo@simm.ac.cn)
英文摘要Scoring functions play an important role in structure-based virtual screening. It has been widely accepted that target-specific scoring functions (TSSFs) may achieve better performance compared with universal scoring functions in actual drug research and development processes. A method that can effectively construct TSSFs will be of great value to drug design and discovery. In this work, we proposed a deep learning-based model named DeepScore to achieve this goal. DeepScore adopted the form of PMF scoring function to calculate protein-ligand binding affinity. However, different from PMF scoring function, in DeepScore, the score for each protein-ligand atom pair was calculated using a feedforward neural network. Our model significantly outperformed Glide Gscore on validation data set DUD-E. The average ROC-AUC on 102 targets was 0.98. We also combined Gscore and DeepScore together using a consensus method and put forward a consensus model named DeepScoreCS. The comparison results showed that DeepScore outperformed other machine learning-based TSSFs building methods. Furthermore, we presented a strategy to visualize the prediction of DeepScore. All of these results clearly demonstrated that DeepScore would be a useful model in constructing TSSFs and represented a novel way incorporating deep learning and drug design.
WOS关键词ENSEMBLE METHODS ; LIGAND ; DOCKING ; OPTIMIZATION
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[FU-SIMM20174007]
WOS研究方向Pharmacology & Pharmacy
语种英语
WOS记录号WOS:000482191400001
出版者FRONTIERS MEDIA SA
源URL[http://119.78.100.183/handle/2S10ELR8/288992]  
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, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, Shanghai, Peoples R China
2.Univ Chinese Acad Sci, Coll Pharm, Beijing, Peoples R China
3.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Dingyan,Cui, Chen,Ding, Xiaoyu,et al. Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods[J]. FRONTIERS IN PHARMACOLOGY,2019,10:11.
APA Wang, Dingyan.,Cui, Chen.,Ding, Xiaoyu.,Xiong, Zhaoping.,Zheng, Mingyue.,...&Chen, Kaixian.(2019).Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods.FRONTIERS IN PHARMACOLOGY,10,11.
MLA Wang, Dingyan,et al."Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods".FRONTIERS IN PHARMACOLOGY 10(2019):11.

入库方式： OAI收割

来源：上海药物研究所

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