DeepDILI: Deep Learning-Powered Drug-Induced Liver Injury Prediction Using Model-Level Representation

Drug-induced liver injury (DILI)
is the most frequently reported
single cause of safety-related withdrawal of marketed drugs. It is
essential to identify drugs with DILI potential at the early stages
of drug development. In this study, we describe a deep learning-powered
DILI (DeepDILI) prediction model created by combining model-level
representation generated by conventional machine learning (ML) algorithms
with a deep learning framework based on Mold2 descriptors. We conducted
a comprehensive evaluation of the proposed DeepDILI model performance
by posing several critical questions: (1) Could the DILI potential
of newly approved drugs be predicted by accumulated knowledge of early
approved ones? (2) is model-level representation more informative
than molecule-based representation for DILI prediction? and (3) could
improved model explainability be established? For question 1, we developed
the DeepDILI model using drugs approved before 1997 to predict the
DILI potential of those approved thereafter. As a result, the DeepDILI
model outperformed the five conventional ML algorithms and two state-of-the-art
ensemble methods with a Matthews correlation coefficient (MCC) value
of 0.331. For question 2, we demonstrated that the DeepDILI model’s
performance was significantly improved (i.e., a MCC improvement of
25.86% in test set) compared with deep neural networks based on molecule-based
representation. For question 3, we found 21 chemical descriptors that
were enriched, suggesting a strong association with DILI outcome.
Furthermore, we found that the DeepDILI model has more discrimination
power to identify the DILI potential of drugs belonging to the World
Health Organization therapeutic category of ‘alimentary tract
and metabolism’. Moreover, the DeepDILI model based on Mold2
descriptors outperformed the ones with Mol2vec and MACCS descriptors.
Finally, the DeepDILI model was applied to the recent real-world problem
of predicting any DILI concern for potential COVID-19 treatments from
repositioning drug candidates. Altogether, this developed DeepDILI
model could serve as a promising tool for screening for DILI risk
of compounds in the preclinical setting, and the DeepDILI model is
publicly available through https://github.com/TingLi2016/DeepDILI.