Machine Learning and Deep Learning in Drug Design

Kunal Roy

ISBN 10: 1837070180 / ISBN 13: 9781837070183

Published by Royal Society of Chemistry, GB, 2026

About this Item

Machine learning (ML) and deep learning (DL) are reshaping the landscape of drug design. This comprehensive volume explores how these technologies are applied across the entire drug discovery pipeline-from target identification and protein structure prediction to virtual screening, pharmacokinetic modelling, and drug repurposing.Bridging cheminformatics, chemometrics, and computational science, the book offers practical case studies, emerging methodologies, and curated e-resources. Readers will discover how ML/DL techniques are used to predict drug-target interactions, optimize molecular properties, repurpose previously used drugs, and design multi-target therapeutics. Special topics include chemical language models, natural product-based drug discovery, and modelling drug-induced toxicities.With contributions from leading experts worldwide, this book is an essential resource for researchers, postgraduate students, and professionals in medicinal chemistry, pharmacology, and pharmaceutical sciences. It provides both foundational knowledge and advanced applications, equipping readers to harness AI for innovative and efficient drug development.

Seller Inventory # LU-9781837070183

Bibliographic Details

• Title: Machine Learning and Deep Learning in Drug Design
• Publisher: Royal Society of Chemistry, GB
• Publication Date: 2026
• Language: English
• ISBN 10: 1837070180
• ISBN 13: 9781837070183
• Binding: Hardback
• Condition: New
• Weight: 1329 grams

About this title

Synopsis

Machine learning (ML) and deep learning (DL) are reshaping the landscape of drug design. This comprehensive volume explores how these technologies are applied across the entire drug discovery pipeline―from target identification and protein structure prediction to virtual screening, pharmacokinetic modelling, and drug repurposing.

Bridging cheminformatics, chemometrics, and computational science, the book offers practical case studies, emerging methodologies, and curated e-resources. Readers will discover how ML/DL techniques are used to predict drug–target interactions, optimize molecular properties, repurpose previously used drugs, and design multi-target therapeutics. Special topics include chemical language models, natural product-based drug discovery, and modelling drug-induced toxicities.

With contributions from leading experts worldwide, this book is an essential resource for researchers, postgraduate students, and professionals in medicinal chemistry, pharmacology, and pharmaceutical sciences. It provides both foundational knowledge and advanced applications, equipping readers to harness AI for innovative and efficient drug development.