文献类型：期刊文献

英文题名：In silico Prediction of Drug Induced Liver Toxicity Using Substructure Pattern Recognition Method

作者：Zhang, Chen[1];Cheng, Feixiong[1,2];Li, Weihua[1];Liu, Guixia[1];Lee, Philip W.[1];Tang, Yun[1]

机构：[1]E China Univ Sci & Technol, Sch Pharm, Shanghai Key Lab New Drug Design, 130 Meilong Rd, Shanghai 200237, Peoples R China;[2]Vanderbilt Univ, Sch Med, Dept Biomed Informat, Nashville, TN 37212 USA

年份：2016

卷号：35

期号：3-4

起止页码：136

外文期刊名：MOLECULAR INFORMATICS

收录：;EI(收录号：20232414244714);WOS:【SCI-EXPANDED(收录号:WOS:000374002000006)】；

基金：This work was supported by the National Natural Science Foundation of China (Grants 81373329, 81273438), the 863 Project (Grant 2012AA020308) and the Fundamental Research Funds for the Central Universities (Grant WY1113007).

语种：英文

外文关键词：Drug-induced liver injury; machine learning; substructure pattern recognition; structural alerts

摘要：Drug-induced liver injury (DILI) is a leading cause of acute liver failure in the US and less severe liver injury worldwide. It is also one of the major reasons of drug withdrawal from the market. Thus, DILI has become one of the most important concerns of drugs, and should be predicted in very early stage of drug discovery process. In this study, a comprehensive data set containing 1317 diverse compounds was collected from publications. Then, high accuracy classification models were built using five machine learning methods based on MACCS and FP4 fingerprints after evaluating by substructure pattern recognition method. The best model was built using SVM method together with FP4 fingerprint at the IG value threshold of 0.0005. Its overall predictive accuracies were 79.7% and 64.5% for the training and test sets, separately, which yielded overall accuracy of 75.0% for the external validation dataset, consisting of 88 compounds collected from a benchmark DILI database - the Liver Toxicity Knowledge Base. This model could be used for drug-induced liver toxicity prediction. Moreover, some key substructure patterns correlated with drug-induced liver toxicity were also identified as structural alerts.