AI-Driven Integration of Transcriptomics, Quantum Mechanics, and Physiology for Predicting Drug-Induced Liver Injury in Data-Limited Scenarios.

Abstract

Drug-induced liver injury (DILI) is a significant concern with prescription medications and supplements. Accordingly, it is crucial to develop tools and approaches that can predict DILI likelihood of existing medications and supplements, as well as potential drug candidates under development. The complexity of liver injury mechanisms and the limited availability of DILI data hamper the development of robust predictive models. In order to overcome these challenges, this study investigated enriching machine learning/artificial intelligence (ML/AI) models that predict the risk of DILI using drug structural parameters along with rat liver transcriptomics data, quantum mechanics-derived features of the drug molecules, and metrics for interspecies variability of drug exposure. The enrichment of ML/AI models with such features dramatically improved ML/AI models' DILI predictive ability, even in a severely data-limited scenario. The approach used in the study, especially the incorporation of knowledge-based features to enrich AI models, holds tremendous promise for not only assessing safety and toxicity assessments of drug candidates but also in other aspects such as target engagement and efficacy of these candidates, early in the development phase.