Machine-Learning-Assisted Approach for Discovering Novel Inhibitors Targeting Bromodomain-Containing Protein 4

doi:10.1021/acs.jcim.7b00098

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	Machine-Learning-Assisted Approach for Discovering Novel Inhibitors Targeting Bromodomain-Containing Protein 4
	Xing, Jing 1,2,4; Lu, Wenchao 1,2; Liu, Rongfeng 3; Wang, Yulan 1,2; Xie, Yiqian 1,2; Zhang, Hao 1,2; Shi, Zhe 3; Jiang, Hao 1,2; Liu, Yu-Chih 3; Chen, Kaixian1
刊名	JOURNAL OF CHEMICAL INFORMATION AND MODELING
	2017-07
卷号	57 期号:7 页码:1677-1690
ISSN号	1549-9596
DOI	10.1021/acs.jcim.7b00098
文献子类	Article
英文摘要	Bromodomain-containing protein 4 (BRD4) is implicated in the pathogenesis of a number of different cancers, inflammatory diseases and heart failure. Much effort has been dedicated toward discovering novel scaffold BRD4 inhibitors (BRD4is) with different selectivity profiles and potential antiresistance properties. Structure-based drug design (SBDD) and virtual screening (VS) are the most frequently used approaches. Here, we demonstrate a novel, structure based VS approach that uses machine-learning algorithms trained on the priori structure and activity knowledge to predict the likelihood that a compound is a BRD4i based on its binding pattern with BRD4. In addition to positive experimental data, such as X-ray structures of BRD4-ligand complexes and BRD4 inhibitory potencies, negative data such as false positives (FPs) identified from our earlier ligand screening results were incorporated into our knowledge base. We used the resulting data to train a machine-learning model named BRD4LGR to predict the BRD4i-likeness of a compound. BRD4LGR achieved a 20-30% higher AUC-ROC than that of Glide using the same test set. When conducting in vitro experiments against a library of previously untested, commercially available organic compounds, the second round of VS using BRD4LGR generated 15 new BRD4is. Moreover, inverting the machine-learning model provided easy access to structure-activity relationship (SAR) interpretation for hit-to-lead optimization.
资助项目	Chinese Academy of Sciences[XDA12050201] ; National Key Research & Development Plan[2016YFC1201003] ; National Natural Science Foundation of China[21210003] ; National Natural Science Foundation of China[81230076] ; National Natural Science Foundation of China[81430084] ; National Natural Science Foundation of China[21472208] ; National Basic Research Program[2015CB910304] ; State Key Laboratory of Natural and Biomimetic Drugs[K20160201] ; State Key Laboratory of Toxicology and Medical Countermeasures, Academy of Military Medical Science[TMC201505]
WOS关键词	TRANSCRIPTIONAL PAUSE RELEASE ; ASSAY INTERFERENCE COMPOUNDS ; STRUCTURE-GUIDED DESIGN ; BET BROMODOMAINS ; DRUG DISCOVERY ; LIGAND INTERACTIONS ; SCORING FUNCTIONS ; DOCKING ; BRD4 ; BINDING
WOS研究方向	Pharmacology & Pharmacy ; Chemistry ; Computer Science
语种	英语
出版者	AMER CHEMICAL SOC
WOS记录号	WOS:000406435500015
内容类型	期刊论文
源URL	[http://119.78.100.183/handle/2S10ELR8/272583]
专题	药物发现与设计中心中科院受体结构与功能重点实验室新药研究国家重点实验室
通讯作者	Luo, Cheng; Zheng, Mingyue
作者单位	1.Chinese Acad Sci, Shanghai Inst Mat Med, State Key Lab Drug Res, Drug Discovery & Design Ctr, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China; 2.Univ Chinese Acad Sci, Dept Pharm, 19A Yuquan Rd, Beijing 100049, Peoples R China; 3.Shanghai ChemPartner Co LTD, 5 Bldg,998 Halei Rd, Shanghai 201203, Peoples R China 4.Peking Univ, State Key Lab Nat & Biomimet Drugs, Xue Yuan Rd 38, Beijing 100191, Peoples R China;
推荐引用方式 GB/T 7714	Xing, Jing,Lu, Wenchao,Liu, Rongfeng,et al. Machine-Learning-Assisted Approach for Discovering Novel Inhibitors Targeting Bromodomain-Containing Protein 4[J]. JOURNAL OF CHEMICAL INFORMATION AND MODELING,2017,57(7):1677-1690.
APA	Xing, Jing.,Lu, Wenchao.,Liu, Rongfeng.,Wang, Yulan.,Xie, Yiqian.,...&Zheng, Mingyue.(2017).Machine-Learning-Assisted Approach for Discovering Novel Inhibitors Targeting Bromodomain-Containing Protein 4.JOURNAL OF CHEMICAL INFORMATION AND MODELING,57(7),1677-1690.
MLA	Xing, Jing,et al."Machine-Learning-Assisted Approach for Discovering Novel Inhibitors Targeting Bromodomain-Containing Protein 4".JOURNAL OF CHEMICAL INFORMATION AND MODELING 57.7(2017):1677-1690.