Machine Learning-Based Approach for Predicting Drug-Induced Liver Injury of Chemical Compounds

Soyeon Lee, Sunyong Yoo

http://doi.org/10.5626/JOK.2023.50.9.777

Drug-induced liver injury (DILI) is one of the factors constraining the distribution of investigational products on the market. Therefore, DILI risk of compounds should be assessed in advance. Although in vivo and in vitro methods can be used to test drug safety, both methods are labor-intensive, time consuming and expensive. In this study, we suggested random forest, light gradient boosting machine, logistic regression models to overcome the above problems. These models used molecular structure and physicochemical features as input to predict the DILI as output. The optimal model was random forest, which performed well for evaluation metrics overall. The proposed model is expected to help drug development process by identifying potential DILI of drug candidates in advance.