Bridging data and drug development: Machine learning approaches for next-generation ADMET prediction.

Abstract

Absorption, distribution, metabolism, excretion, and toxicity (ADMET) evaluation is fundamental to determining drug candidate clinical success. Traditional experimental methods, although reliable, are resource-intensive, whereas conventional computational models lack robustness and generalizability. Recent machine learning (ML) advances have transformed ADMET prediction by deciphering complex structure-property relationships, providing scalable, efficient alternatives. In this paper, we systematically examine state-of-the-art methodologies, including graph neural networks, ensemble learning, and multitask frameworks, as well as emerging strategies for multimodal data integration and algorithmic optimization aimed at enhancing predictive accuracy and translational relevance. By mitigating late-stage attrition, supporting preclinical decision-making, and expediting the development of safer and more efficacious therapeutics, ML-driven ADMET prediction exemplifies the transformative role of artificial intelligence in reshaping modern drug discovery and development.