Machine Learning to Predict Drug-Induced Liver Injury and Its Validation on Failed Drug Candidates in Development.

ABSTRACT: Drug-induced liver injury (DILI) poses a significant challenge for the pharmaceutical industry and regulatory bodies. Despite extensive toxicological research aimed at mitigating DILI risk, the effectiveness of these techniques in predicting DILI in humans remains limited. Consequently, researchers have explored novel approaches and procedures to enhance the accuracy of DILI risk prediction for drug candidates under development. In this study, we leveraged a large human dataset to develop machine learning models for assessing DILI risk. The performance of these prediction models was rigorously evaluated using a 10-fold cross-validation approach and an external test set. Notably, the random forest (RF) and multilayer perceptron (MLP) models emerged as the most effective in predicting DILI.

SUBMITTER: Mostafa F 

PROVIDER: S-EPMC11207878 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature