Integrating Artificial Intelligence with Virtual Screening: A New Era in Drug Discovery addresses the evolving landscape of drug discovery by combining traditional virtual screening with cutting-edge artificial intelligence technologies. This volume offers an interdisciplinary guide to AI-driven ligand and structure-based screening methods, including machine learning, deep learning, and generative models, thus enabling faster and more accurate identification of drug candidates. It covers mathematical models, computational workflows, and optimization strategies such as Pareto-based and physics-based approaches.

Sections also discuss challenges in navigating ultra-large chemical libraries and present practical case studies that demonstrate real-world applications. Edited by leading researchers, the book provides authoritative insights suited for graduate students, researchers, and industry professionals. Users will be empowered by the latest tools and knowledge to innovate drug discovery pipelines and support the development of precision medicines and foster advancements in pharmaceutical sciences.

• Presents comprehensive AI-driven virtual screening methods that enhance efficiency in drug discovery
• Explains the integration of machine learning, deep learning, and physics-based optimization techniques
• Covers ligand-based and structure-based virtual screening with practical case studies
• Highlights advancements in generative AI and active learning for molecular design
• Addresses challenges and solutions in screening ultra-large chemical libraries