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

doi:10.3389/fphar.2019.00924

	Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods
	Wang, Dingyan 1,2; Cui, Chen 1,2; Ding, Xiaoyu 1,2; Xiong, Zhaoping3 ; Zheng, Mingyue 1; Luo, Xiaomin 1; Jiang, Hualiang1,3 ; Chen, Kaixian1,3
	2019-08-22
发表期刊	FRONTIERS IN PHARMACOLOGY
ISSN	1663-9812
卷号	10
发表状态	已发表
DOI	10.3389/fphar.2019.00924
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.
关键词	virtual screening target-specific scoring function deep learning drug discovery DUD-E
URL	查看原文
收录类别	SCI ; SCIE
语种	英语
资助项目	Fudan-SIMM Joint Research Fund[FU-SIMM20174007]
WOS研究方向	Pharmacology & Pharmacy
WOS类目	Pharmacology & Pharmacy
WOS记录号	WOS:000482191400001
出版者	FRONTIERS MEDIA SA
WOS关键词	ENSEMBLE METHODS ; LIGAND ; DOCKING ; OPTIMIZATION
原始文献类型	Article
引用统计
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/66393
专题	生命科学与技术学院_博士生生命科学与技术学院_特聘教授组_陈凯先组免疫化学研究所_特聘教授组_蒋华良组
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.
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.
MLA	Wang, Dingyan,et al."Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods".FRONTIERS IN PHARMACOLOGY 10(2019).