Deep Learning for Drug-Induced Liver Injury.

Abstract

Drug-induced liver injury (DILI) has been the single most frequent cause of safety-related drug marketing withdrawals for the past 50 years. Recently, deep learning (DL) has been successfully applied in many fields due to its exceptional and automatic learning ability. In this study, DILI prediction models were developed using DL architectures, and the best model trained on 475 drugs predicted an external validation set of 198 drugs with an accuracy of 86.9%, sensitivity of 82.5%, specificity of 92.9%, and area under the curve of 0.955, which is better than the performance of previously described DILI prediction models. Furthermore, with deep analysis, we also identified important molecular features that are related to DILI. Such DL models could improve the prediction of DILI risk in humans. The DL DILI prediction models are freely available at http://www.repharma.cn/DILIserver/DILI_home.php.

• Authors: Youjun Xu, Ziwei Dai, Fangjin Chen, Shuaishi Gao, Jianfeng Pei, Luhua Lai
• Journal: Journal of chemical information and modeling (J Chem Inf Model) Vol. 55 Issue 10 Pg. 2085-93 (Oct 26 2015) ISSN: 1549-960X [Electronic] United States
• PMID: 26437739 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)

Chemical References

• Glycine

Topics

• Algorithms
• Chemical and Drug Induced Liver Injury
• Glycine (chemistry)
• Humans
• Models, Biological
• ROC Curve
• Risk Factors
• Safety-Based Drug Withdrawals
• Software (standards)