Computer-Aided Drug Design in Modern Drug Discovery

ISBN 10: 0443451052 / ISBN 13: 9780443451058

Published by Elsevier Science, 2026

About this Item

Provides real-world case studies that demonstrate successful CADD applications in drug developmentSimplifies complex concepts, making CADD accessible for researchers at all experience levelsHighlights emerging trends and future dir.

Seller Inventory # 2726780722

Bibliographic Details

• Title: Computer-Aided Drug Design in Modern Drug Discovery
• Publisher: Elsevier Science
• Publication Date: 2026
• Language: English
• ISBN 10: 0443451052
• ISBN 13: 9780443451058
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Computer-Aided Drug Design in Modern Drug Discovery offers an in-depth exploration of CADD, featuring real-world case studies and practical examples that empower researchers to effectively harness this technology in drug discovery and development. Exploring the intricacies of CADD, the book comprises several key chapters, beginning with an introduction to its methods and evolution that is followed by a thorough examination of the integration of artificial intelligence in drug design. Subsequent chapters cover various CADD approaches, including ligand-based, structure-based, and fragment-based design, alongside AI-driven compound generation and drug screening.

The book also showcases a selection of marketed drugs developed with CADD or AI, such as Captopril, Tirofiban, and Crizotinib, providing invaluable insights into the successes and challenges faced in real-world applications. Finally, the discussion of emerging trends and future directions in CADD and AI highlights the ongoing evolution of these methodologies and their integration with traditional drug development processes.

• Provides real-world case studies that demonstrate successful CADD applications in drug development
• Simplifies complex concepts, making CADD accessible for researchers at all experience levels
• Highlights emerging trends and future directions in CADD and AI for informed decision-making
• Integrates theoretical knowledge with practical examples to enhance understanding and applications in drug discovery
• Encourages innovative approaches to drug design through comprehensive insights and expert contributions

About the Authors

Dr. Yinuo Wu is an associate Professor in Medicinal Chemistry. She did her undergraduate degree at the Sun Yat-sen University (2002-2006) and then carried out a PhD (2006-2011). After two years of post-doctoral at the Hong Kong polytechnic University, she started her academic career in 2013 at Sun Yat-sen University. Dr. Yinuo Wu is particularly interested in the development of anti-fibrosis drug candidates. In the last five years, Dr. Wu has more than 50 publications and several patents on this field.

Dr. Tianmiao Ou is a Professor in Medicinal Chemistry and Chemical Biology. She did her undergraduate degree at the China Pharmaceutical University (1998-2002) and then carried out a PhD under the supervision of Professor Lianquan Gu (2002-2007). She started her academic career in 2007 in the Sun Yat-sen University. She had traveled to UK as an academic visitor and worked in the group of Professor Shankar Balasubramanian at the University of Cambridge (2012-2013). The whole group is particularly interested in the role of non-canonical nucleic acid structures that control gene expression (e.g. G-quadruplexes, and RNA structures in the non-coding regions of mRNAs). Their goal is to find out small organic molecules that target such structures and alter the expression of certain genes of interest. Such small molecule gene regulators are valuable tools to study mechanisms in biology and will also open up new approaches for therapeutics and molecular medicine, particularly for diseases characterized by aberrant expression of certain genes. Under the support of various funding, Dr. Ou has more than 70 publications and several patents on this field.

Dr. Dongsheng Cao is a Professor at the Xiya School of Pharmaceutical Science at Central South University. He is mainly engaged in research on efficient chemical informatics and drug molecule design methods and applications based on artificial intelligence technology. Focusing on three key technical bottlenecks in drug development (discovery of bioactive molecules, prediction of drug properties and safety, and evaluation of selectivity/off-target effects/drug resistance), he conducts systematic and in-depth interdisciplinary methodological research on virtual screening methods based on structures/ligands, prediction of drug properties theory, molecular target recognition, and molecular intelligence generation and optimization. Prof. Cao has already published more than 270 publications with a maximum citation count of 1691 per paper.