Utilizing Artificial Intelligence and Machine Learning for Early Detection of Adverse Drug Reactions and Drug-Induced Toxicities

Abstract

Adverse drug reactions (ADRs) and drug-induced toxicity represent critical barriers in drug discovery and development, posing substantial risks to patient safety and contributing significantly to healthcare costs. Unlike more overt health threats such as infectious diseases, ADRs often remain underrecognized until late-stage clinical trials or post-market surveillance, making their early prediction vitally important. The emergence of artificial intelligence (AI) and machine learning (ML) has transformed the landscape of pharmacovigilance, offering innovative and powerful tools for the early identification of ADRs and toxicological risks. These computational techniques enable rapid, accurate, and large-scale prediction of adverse effects—sometimes even prior to the physical synthesis of a drug—thus improving efficiency and minimizing the likelihood of costly late-stage failures or withdrawals. This review comprehensively examines the current and emerging applications of AI and ML in the early detection of ADRs and drug-induced toxicity. It explores a range of methodologies including data mining, quantitative structure-activity relationship (QSAR) modeling, and deep learning, alongside curated databases, algorithms, and specialized software platforms used for toxicity prediction. By providing an integrated overview of existing strategies and future directions, this review underscores the transformative potential of AI and ML in enhancing drug safety and accelerating the development of safer therapeutics.