Deep Learning for Drug-Induced Liver Injury

J Chem Inf Model. 2015 Oct 26;55(10):2085-93. doi: 10.1021/acs.jcim.5b00238. Epub 2015 Oct 13.


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

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

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


  • Glycine