Bioinformatics, Computational Chemistry, and AI in Drug Innovation: Advances and Applications (EPUB)

by Sushil Kumar Kashaw (ed.), Anshuman Dixit (ed.), Shivangi Agarwal (ed.), Priyanshu Nema (ed.)

This book comprehensively examines the applications of bioinformatics, computational chemistry, and artificial intelligence (AI) in the field of drug discovery and innovation. It provides foundational insights into bioinformatics and its applications, from cutting‑edge advancements in computational molecular phylogeny, structural bioinformatics, and metabolic computing to biological databases and data mining. In addition, this book covers critical aspects of data storage, retrieval, database structure, and annotation, providing a robust foundation for understanding the complexities in biological data management. It discusses computational protein modeling, molecular docking, pharmacophore modeling, quantitative structure–activity relationships, and innovative approaches for drug target identification. The chapters explore protein–ligand interactions, molecular dynamics simulations, density function theory, and the strategic endeavor of drug repurposing. Toward the end, this book discusses quality by design (QbD) in drug formulation, the transformative influence of AI and machine learning (ML) on drug discovery, computational tools for estimating pharmacokinetic parameters, and the conceptual underpinnings of computational genomics. This book will be useful for students, professionals, and researchers in the fields of pharmaceutical sciences, life sciences, pharmaceuticals, biotechnology, and computational biology.
Key Features:

Discusses applications of bioinformatics, computational chemistry, and AI in drug discovery and innovation

Covers essential aspects of data storage, retrieval, database structure, and annotation for managing complex biological data

Reviews molecular docking, pharmacophore modeling, and quantitative structure–activity relationships in drug designing

Explores the transformative influence of AI and ML in drug discovery

Emphasizes QbD principles, highlighting the importance of precision and quality in drug formulation design