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

個数：

予約

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

Kashaw, Sushil Kumar (EDT)/ Dixit, Anshuman (EDT)/ Agarwal, Shivangi (EDT)

ウェブストア価格 ¥29,173（本体¥26,521）
CRC Press（2026/01発売）
外貨定価 US$ 140.00
ポイント 265pt

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製本 Hardcover:ハードカバー版／ページ数 264 p.
言語 ENG
商品コード 9781032733012

Full Description

This book comprehensively examines the applications of bioinformatics, computational chemistry and artificial intelligence 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. Additionally, the book covers critical aspects of data storage, retrieval, database structure, and annotation, providing a robust foundation for understanding the complexities of 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. Towards the end, the book discusses Quality by Design (QbD) in drug formulation, artificial intelligence, and machine learning's transformative influence on drug discovery, computational tools for estimating pharmacokinetic parameters, and the conceptual underpinnings of computational genomics. This book will is useful for students, professionals and researchers in the fields of pharmaceutical Sciences, life sciences, pharmaceuticals, biotechnology and computational biology.

Key feature

· Discuses applications of bioinformatics, computational chemistry, and artificial intelligence 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 for drug designing.

· Explores the transformative influence of artificial intelligence and machine learning in drug discovery.

· Emphasizes Quality by Design (QbD) principles, highlighting the importance of precision and quality in drug formulation design.

1 An Overview of Bioinformatics and Its Application 2 Structural Bioinformatics 3 Metabolic Computing 4 Biological Databases and Data mining 5 Data Storage and Retrieval, Database Structure and Annotation 6 Computational Protein Modelling, Refinement, Validation and its Application 7 Molecular Docking Guided Drug Design & Discovery 8 Pharmacophore Modeling: A Cornerstone of Modern Drug Design 9 Quantitative Structure Activity Relationship in Drug Design 10 Computational Approaches for Drug Target Identification 11 Protein-Ligand Interaction and Molecular Dynamic Simulation 12 Density Function theory and its applications 13 Drug Repurposing 14 Biodiversity, Ecological and Immuno-Informatics 15 Quality by Design in Drug Formulation Design 16 In Silico Designing of Vaccines: Methods, Tools, and Their Limitations 17 Artificial Intelligence and Machine Learning in Drug Discovery 18 Pharmacokinetic parameters estimation by computational tools19 Computational Genomics concept and its utilization