Predicting Drug-Induced Liver Injury Using Ensemble Learning Methods and Molecular Fingerprints

Abstract

Drug-induced liver injury (Dili) is a major safety concern in the drug-development process, and various methods have been proposed to predict the hepatotoxicity of compounds during the early stages of drug trials. In this study, we developed an ensemble model using 3 machine learning algorithms and 12 molecular fingerprints from a dataset containing 1241 diverse compounds. The ensemble model achieved an average accuracy of 71.1±2.6%, sensitivity (SE) of 79.9±3.6%, specificity (SP) of 60.3±4.8%, and area under the receiver-operating characteristic curve (AUC) of 0.764±0.026 in 5-fold cross-validation and an accuracy of 84.3%, SE of 86.9%, SP of 75.4%, and AUC of 0.904 in an external validation dataset of 286 compounds collected from the Liver Toxicity Knowledge Base. Compared with previous methods, the ensemble model achieved relatively high accuracy and SE. We also identified several substructures related to Dili. In addition, we provide a web server offering access to our models (http://ccsipb.lnu.edu.cn/toxicity/HepatoPred-EL/).