Unlock the Power of Cheminformatics, QSAR, and Machine Learning in Novel Drug Development

Authored by Kunal Roy, this comprehensive guide delves into the realm of structure-based, ligand-based, and machine learning tools that are revolutionizing drug design. The book presents an integrated approach to chemometrics, cheminformatics, and machine learning methods, providing a solid foundation for understanding the complexities of computational drug design.

Foundational Knowledge: Establishing the Basics

The first part of the book sets the stage by exploring recent trends in computational modeling of drugs. Additionally, it covers essential topics such as QSAR in medicinal chemistry, structure-based methods, chemoinformatics and chemometric approaches, and machine learning methods in drug design.

Applications and Case Studies: Putting Theory into Practice

The second part of the book focuses on methods and case studies, including:

• Molecular descriptors and molecular similarity
• Structure-based screening and homology modeling in protein structure predictions
• Molecular docking and stability of drug receptor interactions
• Deep learning and support vector machine in drug design

Special Topics: Exploring the Frontiers of Computational Drug Design

The third part of the book is dedicated to special topics, featuring dedicated chapters on:

• Design of green pharmaceuticals
• Computational toxicology

Tools and Resources: Unlocking the Power of Computational Drug Design

The final part of the book presents an extensive collection of available tools and databases, including:

• QSAR databases
• Free tools and databases in ligand and structure-based drug design
• Machine learning resources for drug design
• Web servers for identifying drug candidates

By presenting chemometrics, cheminformatics, and machine learning methods under a single reference, this book provides a unique and valuable resource for researchers, scientists, and students working in the field of novel drug development.

Book Details:

Publisher: Elsevier Science

Published: May 23, 2023

Language: English

ISBN: 9780443186387, 9780443186394