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

文献类型：期刊论文

作者Xing, Jing1,2,4; Lu, Wenchao1,2; Liu, Rongfeng3; Wang, Yulan1,2; Xie, Yiqian1,2; Zhang, Hao1,2; Shi, Zhe3; Jiang, Hao1,2; Liu, Yu-Chih3; Chen, Kaixian1
刊名JOURNAL OF CHEMICAL INFORMATION AND MODELING
出版日期2017-07
卷号57期号:7页码:1677-1690
ISSN号1549-9596
DOI10.1021/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.
WOS关键词TRANSCRIPTIONAL PAUSE RELEASE ; ASSAY INTERFERENCE COMPOUNDS ; STRUCTURE-GUIDED DESIGN ; BET BROMODOMAINS ; DRUG DISCOVERY ; LIGAND INTERACTIONS ; SCORING FUNCTIONS ; DOCKING ; BRD4 ; BINDING
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研究方向Pharmacology & Pharmacy ; Chemistry ; Computer Science
语种英语
WOS记录号WOS:000406435500015
出版者AMER CHEMICAL SOC
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.

入库方式： OAI收割

来源：上海药物研究所

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