Drug-Induced Toxicity Prediction Using Data Mining Techniques: A Comprehensive Study

Abstract

Drug-induced toxicity represents one of the major developmental bottlenecks, leading to high attrition rates at clinical trial stages and post-marketing withdrawals of drugs. Classical in vivo and in vitro methods for toxicity assessment have numerous disadvantages, being above all expensive and low-throughput, raising ethical concerns, and demonstrating poor cross-species extrapolation. Consequently, innovative computational approaches became necessary. Data mining (DM) and Machine Learning (ML) have recently emerged as transformative paradigms in predicting drug toxicity that will enable rapid screening of chemical compounds and their early identification of adverse effects. This study extensively reviews data mining techniques in drug-induced toxicity prediction by exploring different toxicity endpoints, datasets, computational methodologies, and the challenges this area faces. A large promise for revolutionizing preclinical toxicity evaluation has been confirmed through the integration of QSAR modeling, ensemble learning methods, Deep Learning (DL) architectures, and multi-task learning frameworks. This analysis synthesizes recent advances in computational toxicology, analyzes the predictive performances across different machine learning algorithms, and discusses future directions of developing more accurate, interpretable, and clinically relevant toxicity prediction models.