Overview

Artificial Intelligence in Precision Drug Design, Volume One: Foundations and Core Techniques offers a comprehensive introduction to the transformative role of AI in modern drug discovery. The book lays the groundwork for understanding how machine learning, deep learning, and generative models are reshaping the development of precision therapeutics. It explores foundational topics such as AI-driven molecular screening, pharmacokinetics, ADMET modeling, toxicity prediction, and omics integration. In addition, sections address ethical, philosophical, and epistemological dimensions, ensuring a well-rounded perspective. Each chapter, authored by global experts, combines theoretical insights with real-world case studies to bridge the gap between AI and life sciences. Designed for graduate students, researchers, and professionals in bioinformatics, biomedical sciences, and pharmaceutical R&D, this volume equips readers with essential knowledge and tools to navigate the evolving landscape of AI in drug design. It empowers interdisciplinary learners to apply cutting-edge AI techniques to real-world biomedical challenges.

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9780443444159

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Author Information

Dr. Khalid Raza is working as an Associate Professor at the Department of Computer Science, Jamia Millia Islamia, New Delhi. Earlier he worked as an “ICCR Chair Professor” at Ain Shams University, Cairo, Egypt. He has many years of teaching & research experiments in the field of Translational Bioinformatics and Computational Intelligence. He has contributed over 120 research articles in reputed Journals and Edited Books. Dr. Raza has authored/edited dozens of books published by reputed publishers. Dr. Raza is an Academic Editor of PeerJ Computer Science International Journal, and Guest Editor of the Journal Natural Product Communications. He has an active collaboration with the scientists from leading institutions of India and abroad. Recently, Dr. Raza has been featured in the list of Top 2% Scientists released by Stanford University (USA) in collaboration with Elsevier. His research interest lies in Machine Learning and its applications in Bioinformatics and Health-informatics.

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