Harnessing the Power of AI: A Modern Review on the Prediction of ADMET Properties in Drug Discovery.
TL;DR
Artificial intelligence-driven ADMET prediction is becoming a vital tool in lowering attrition, speeding up candidate prioritization, and influencing the direction of rational drug development, despite persistent issues with data quality, regulatory acceptance, and synthetic viability.
Abstract
Drug discovery is frequently limited by high attrition rates, and poor absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles are a major cause of late-stage failure. Therefore, precise ADMET property prediction is necessary to develop safe and effective drug candidates. Traditional experimental assays and rule-based computational procedures are limited by their poor predictive power, cost, and time, despite providing valuable insights. Innovative strategies to deal with these issues have been introduced by developments in artificial intelligence (AI), such as machine learning (ML), deep learning (DL), graph neural networks (GNNs), generative models, and multi-task learning (MTL). AI techniques can better generalize scaffolds, capture interdependencies between pharmacokinetic and toxicological endpoints, and model complex nonlinear relationships by leveraging large, diverse datasets. Explainable AI (XAI) enhances transparency by detecting biological and structural characteristics that are relevant to predictions, even if integrated pipelines combine predictive modeling with molecular creation and optimization. AI-driven ADMET prediction is becoming a vital tool in lowering attrition, speeding up candidate prioritization, and influencing the direction of rational drug development, despite persistent issues with data quality, regulatory acceptance, and synthetic viability.