Full Description
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.
Contents
A Perspective on the Application of Machine Learning and Deep Learning in the Drug Discovery Pipeline
AI-driven Approaches for Target Discovery in Drug Design
Machine Learning in Protein Structure Prediction
Machine Learning Approaches to Chemical Space Exploration
Machine Learning in Structure-based Drug Design
Machine Learning in Molecular Dynamics Applications in Medicinal Chemistry
Machine Learning in Ligand-based Drug Design
Machine Learning in Virtual Screening of Databases
Machine Learning in Predicting the Physicochemical Properties of Drug Candidates
Machine Learning-augmented Rapid Screening and Scoring for an Effective Search for Lead Molecules in Computer-aided Drug Discovery
Machine Learning in Drug-Drug Interactions
Machine Learning in Natural Product-based Drug Discovery
Machine Learning in the Optimization of Pharmacokinetic Parameters
Machine Learning in Drug-induced Adverse Reaction Modeling: Case Studies of Drug-induced Cardiotoxicity Modeling
Machine Learning in Drug Repurposing
Machine Learning (ML) and Deep Learning (DL) Approaches in Multi-target Drug Design
Chemical Language Model Applications in Medicinal Chemistry
e-Resources Relevant to Machine Learning Tools for Medicinal Chemistry
Machine Learning-based Methods for Designing Protein-based Drugs
Machine Learning Applications in Vaccine Design
Leveraging Machine Learning for Network Pharmacology
