Abstract

In silico toxicology models have emerged as pivotal tools in drug safety assessment, offering a sophisticated approach to predict and evaluate the potential toxic effects of pharmaceutical compounds. Recent advancements in computational methods, including machine learning, artificial intelligence, and bioinformatics, have significantly enhanced the predictive accuracy of these models. Modern Quantitative Structure-Activity Relationship (QSAR) models, pharmacophore modeling, and omics technologies now contribute to a more comprehensive understanding of drug toxicity. These innovations enable early-stage drug development screening, support personalized medicine, and align with regulatory requirements. Despite progress, challenges remain in data quality, model accuracy, and biological complexity. Future directions include integrating in silico models with emerging technologies to refine toxicity predictions and improve drug safety outcomes