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Predicting Hepatotoxicity of Drug Metabolites Via an Ensemble Approach Based on Support Vector Machine

文献类型：期刊论文

作者Lu, Yin1; Liu, Lili1,2; Lu, Dong1,2,3; Cai, Yudong4; Zheng, Mingyue1; Luo, Xiaomin1,2; Jiang, Hualiang1,5; Chen, Kaixian1,5
刊名COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING
出版日期2017
卷号20期号:10页码:839-849
关键词DILI hepatotoxicity QSAR drug metabolites mRMR SVM
ISSN号1386-2073
DOI10.2174/1386207320666171121113255
文献子类Article
英文摘要Objective: Drug-induced liver injury (DILI) is a major cause of drug withdrawal. The chemical properties of the drug, especially drug metabolites, play key roles in DILI. Our goal is to construct a QSAR model to predict drug hepatotoxicity based on drug metabolites. Materials and Methods: 64 hepatotoxic drug metabolites and 3,339 non-hepatotoxic drug metabolites were gathered from MDL Metabolite Database. Considering the imbalance of the dataset, we randomly split the negative samples and combined each portion with all the positive samples to construct individually balanced datasets for constructing independent classifiers. Then, we adopted an ensemble approach to make prediction based on the results of all individual classifiers and applied the minimum Redundancy Maximum Relevance (mRMR) feature selection method to select the molecular descriptors. Eventually, for the drugs in the external test set, a Bayesian inference method was used to predict the hepatotoxicity of a drug based on its metabolites. Results: The model showed the average balanced accuracy=78.47%, sensitivity =74.17%, and specificity=82.77%. Five molecular descriptors characterizing molecular polarity, intramolecular bonding strength, and molecular frontier orbital energy were obtained. When predicting the hepatotoxicity of a drug based on all its metabolites, the sensitivity, specificity and balanced accuracy were 60.38%, 70.00% and 65.19%, respectively, indicating that this method is useful for identifying the hepatotoxicity of drugs. Conclusions: We developed an in silico model to predict hepatotoxicity of drug metabolites. Moreover, Bayesian inference was applied to predict the hepatotoxicity of a drug based on its metabolites which brought out valuable high sensitivity and specificity.
WOS关键词INDUCED LIVER-INJURY ; QUANTITATIVE STRUCTURE-ACTIVITY ; REACTIVE METABOLITES ; QSAR ; TOXICITY ; ACTIVATION
资助项目State Key Program of Basic Research of China[2015CB910304] ; National Natural Science Foundation of China[81430084] ; National Natural Science Foundation of China[81573351] ; State Key Laboratory of Natural and Biomimetic Drugs[00000000] ; Chinese Academy of Sciences[XDA12050201] ; National Key Research & Development Plan[2016YF1201003]
WOS研究方向Biochemistry & Molecular Biology ; Chemistry ; Pharmacology & Pharmacy
语种英语
WOS记录号WOS:000425051500003
出版者BENTHAM SCIENCE PUBL LTD
源URL[http://119.78.100.183/handle/2S10ELR8/275688]  
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Cai, Yudong; 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, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China;
2.Peking Univ, State Key Lab Nat & Biomimet Drugs, Beijing 100191, Peoples R China;
3.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China;
4.Shanghai Univ, Inst Syst Biol, Shanghai 200444, Peoples R China;
5.Shanghai Tech Univ, Sch Life Sci & Technol, Shanghai 200031, Peoples R China
推荐引用方式
GB/T 7714		 Lu, Yin,Liu, Lili,Lu, Dong,et al. Predicting Hepatotoxicity of Drug Metabolites Via an Ensemble Approach Based on Support Vector Machine[J]. COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING,2017,20(10):839-849.
APA Lu, Yin.,Liu, Lili.,Lu, Dong.,Cai, Yudong.,Zheng, Mingyue.,...&Chen, Kaixian.(2017).Predicting Hepatotoxicity of Drug Metabolites Via an Ensemble Approach Based on Support Vector Machine.COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING,20(10),839-849.
MLA Lu, Yin,et al."Predicting Hepatotoxicity of Drug Metabolites Via an Ensemble Approach Based on Support Vector Machine".COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING 20.10(2017):839-849.

入库方式： OAI收割

来源：上海药物研究所

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