Abstract

In silico toxicology has emerged as a powerful tool for revolutionizing risk assessment by leveraging computational modeling and machine learning techniques. This approach enables rapid and cost-effective prediction of chemical toxicity, reducing reliance on traditional animal testing. By integrating quantitative structure-activity relationships (QSAR), molecular docking, and artificial intelligence-driven algorithms, in silico models provide a more precise and mechanistic understanding of toxic effects. Furthermore, advancements in big data analytics and deep learning enhance the predictive accuracy and applicability of these models across various domains, including pharmaceuticals, environmental toxicology, and regulatory decision-making. As computational methods continue to evolve, in silico toxicology is set to become a cornerstone of modern toxicological assessments, ensuring safer chemical development and improved public health outcomes.