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

Deep Learning for Drug-Induced Liver Injury.

Xu Y1, Dai Z1, Chen F1, Gao S1, Pei J1, Lai L1,2,3.

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Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University , Beijing 100871, China.
Beijing National Laboratory for Molecular Sciences, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University , Beijing 100871, China.
Peking-Tsinghua Center for Life Sciences, Peking University , Beijing 100871, China.


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

[Indexed for MEDLINE]

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