Foreword

The field of drug discovery has witnessed a paradigm shift in recent decades, evolving from labor-intensive empirical screening toward data-driven, computationally guided innovation. Among the forces driving this transformation, computer-aided drug design (CADD) stands as a cornerstone technology that bridges chemistry, biology, and informatics with unparalleled precision. The integration of computational methods into drug development has not only accelerated the discovery pipeline but also deepened our molecular understanding of disease mechanisms, target interactions, and therapeutic optimization.

This volume, Computer-Aided Drug Design: Principles and Computational Foundations, provides a comprehensive and methodical introduction to the concepts and methodologies that form the basis of modern CADD. At a time when computational approaches are becoming indispensable to pharmaceutical sciences, this book offers readers an opportunity to develop a strong conceptual framework in the discipline. The structure of this volume mirrors the foundational progression of the field itself. Beginning with the historical evolution of CADD, the chapters guide readers through classical and well-established methodologies, including quantitative structure–activity relationship (QSAR) analysis, molecular and quantum mechanics, energy minimization, homology modeling, molecular docking, and ADMET prediction. Together, these topics provide the theoretical and practical basis necessary for understanding how computational tools contribute to rational drug discovery.

What distinguishes this volume is its balanced presentation of theory and application. The contributors have successfully combined conceptual clarity with methodological depth, ensuring that readers not only appreciate the scientific principles involved but also understand their relevance within contemporary drug development workflows. The discussions are enriched through practical examples and case-based perspectives that reinforce the importance of these approaches in real-world settings.

The chapters included in this volume have been authored by experts from diverse academic and research backgrounds, reflecting the interdisciplinary nature of computational drug design. Their collective expertise has resulted in a scholarly resource that is equally valuable to students beginning their journey in CADD and to researchers seeking a consolidated reference on fundamental methodologies.

I commend the editors, Ajmer Singh Grewal, Viney Lather, Geeta Deswal, and Kumar Guarve, for their outstanding work and dedication in bringing this comprehensive and insightful edited book to fruition. It is a must-read for researchers, healthcare professionals, and students eager to stay abreast of the latest developments in the pharmaceutical and biomedical sciences.