Next-Generation Computational Drug Discovery

This book presents the fundamental principles, contemporary methodologies, and practical applications that define modern computer-aided drug design. It explores the integration of Cryo-EM structures, AlphaFold predictions, and homology modeling to expand structural insight into biologically relevant targets. In addition, it covers molecular docking, molecular dynamics simulations, enhanced sampling approaches, and free energy calculations for robust hit-to-lead refinement. The book further examines in silico ADMET prediction, quantitative structure–activity relationships, machine learning–based modeling, and network pharmacology frameworks applied across therapeutic areas including oncology, infectious diseases, neurodegenerative disorders, and metabolic and cardiovascular conditions. Key Features: Explores foundational and advanced CADD techniques, including molecular docking, molecular dynamics simulations, in silico ADMET prediction, and network pharmacology Integrates Cryo-EM, AlphaFold, and homology modeling to enhance structure-based drug discovery Details machine learning applications for pharmacokinetic profiling, toxicity prediction, and lead prioritization Discusses computational workflows to multi-target drug design across major therapeutic domains Presents systems biology perspectives and translational case examples linking computational discovery to experimental validation This book is intended for researchers and students in pharmaceutical sciences, computational biology, bioinformatics, and related disciplines.