Angol
Artificial intelligence is redefining the future of pharmacology by enabling a shift from traditional trial-and-error approaches toward more predictive, data-driven models of drug discovery and development. As biomedical data grows in volume and complexity, advanced computational techniques are helping researchers uncover novel therapeutic targets, optimize candidate compounds, and improve the accuracy of safety assessments with unprecedented speed and precision. The increasing adoption of computational modeling and New Approach Methodologies is also accelerating efforts to reduce reliance on conventional testing practices while supporting more efficient and scientifically robust evaluation processes. By combining biological, chemical, clinical, and real-world data, AI-driven systems are generating deeper insights into disease mechanisms and treatment responses. These developments are creating new possibilities for faster innovation, improved patient outcomes, and a more intelligent and sustainable pharmaceutical ecosystem. AI-Driven Pharmacology, Toxicology, and Drug Safety: Emerging Methods, Regulatory Trends, and Applications explores how artificial intelligence is reshaping pharmacology, toxicology, and drug safety through more predictive and data-driven approaches to research and evaluation. The book examines emerging computational methods and their growing role in supporting innovation across the pharmaceutical sector while also considering the challenges associated with validation, transparency, and regulatory acceptance as AI becomes increasingly integrated into drug development and safety assessment. Covering topics such as animal testing alternatives, healthcare analytics, and risk assessment, this book is an indispensable academic resource for graduate and doctoral students, pharmacologists, toxicologists drug safety and pharmacovigilance professionals, regulatory scientists, pharmaceutical industry professionals, policymakers, and more.
