Explainable Artificial Intelligence (XAI) in Drug Discovery

Abstract

Artificial intelligence (AI) has transformed the drug discovery process, dramatically speeding up the identification of potential drug targets, the optimization of drug candidates, and the movement of compounds from the laboratory to the clinic. With the use of AI technologies, notably deep learning and machine learning methodologies, researchers have been able to analyze large datasets, find hidden biological patterns, and predict drug–target interactions with remarkable speed and precision. Yet, even with these advances, the intrinsic opacity of sophisticated AI models remains a fundamental barrier to broad use of AI in drug discovery. Specifically, deep learning systems tend to be “black boxes” that produce forecasts or recommendations but do not provide transparent explanations for how they arrived at those decisions. This lack of interpretability may impede scientific validation, regulatory approval and general confidence of researchers and doctors in AI-driven outcomes. This study gives a complete explanation of the main concepts of XAI, discusses important tools and methodologies now available and discusses a number of applications where XAI is being used to enhance results in drug development. It also discusses the challenges of implementing XAI, including technical, practical and regulatory hurdles, and considers possible future directions for research and development in this rapidly evolving area, with particular regard to its implications for pharmaceutical innovation.