文献类型：期刊文献

英文题名：Deep Learning Based Drug Metabolites Prediction

作者：Wang, Disha[1];Liu, Wenjun[2];Shen, Zihao[1];Jiang, Lei[1];Wang, Jie[1];Li, Shiliang[1];Li, Honglin[1]

机构：[1]East China Univ Sci & Technol, Shanghai Key Lab New Drug Design, State Key Lab Bioreactor Engn, Sch Pharm, Shanghai, Peoples R China;[2]Jiangzhong Pharmaceut Co Ltd, Dept Res & Dev, Nanchang, Jiangxi, Peoples R China

年份：2020

卷号：10

外文期刊名：FRONTIERS IN PHARMACOLOGY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000515383200001)】；

基金：This work was supported by the National Key Research and Development Program (2016YFA0502304 to HL); the National Natural Science Foundation of China (grant 81825020 to HL, 81803437 to SL); the National Science & Technology Major Project "Key New Drug Creation andManufacturing Program," China (Number: 2018ZX09711002); the Fundamental Research Funds for the Central Universities, Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase) under Grant No.U1501501. SL is also sponsored by Shanghai Sailing Program (No. 18YF1405100). HL is also sponsored by National Program for Special Supports of Eminent Professionals and National Program for Support of Top-notch Young Professionals.

语种：英文

外文关键词：deep learning; drug metabolism; metabolites prediction; reaction rules; SMARTS

摘要：Drug metabolism research plays a key role in the discovery and development of drugs. Based on the discovery of drug metabolites, new chemical entities can be identified and potential safety hazards caused by reactive or toxic metabolites can be minimized. Nowadays, computational methods are usually complementary tools for experiments. However, current metabolites prediction methods tend to have high false positive rates with low accuracy and are usually only used for specific enzyme systems. In order to overcome this difficulty, a method was developed in this paper by first establishing a database with broad coverage of SMARTS-coded metabolic reaction rule, and then extracting the molecular fingerprints of compounds to construct a classification model based on deep learning algorithms. The metabolic reaction rule database we built can supplement chemically reasonable negative reaction examples. Based on deep learning algorithms, the model could determine which reaction types are more likely to occur than the others. In the test set, our method can achieve the accuracy of 70% (Top-10), which is significantly higher than that of random guess and the rule-based method SyGMa. The results demonstrated that our method has a certain predictive ability and application value.