Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development

Cheminformatics, QSAR, and Machine Learning Applications for Novel Drug Development is a book that provides an integrated presentation of chemometrics, cheminformatics, and machine learning methods for drug design. It covers recent trends in computational modeling, methods and case studies, special topics, and available tools and databases. The book emphasizes the importance of these methods in discovering new drugs and improving drug design.

Format: Paperback / softback
Length: 768 pages
Publication date: 25 May 2023
Publisher: Elsevier Science Publishing Co Inc

Cheminformatics, Quantitative Structure-Activity Relationships (QSAR), and Machine Learning Applications for Novel Drug Development is a comprehensive guide that explores the diverse range of structure-based, ligand-based, and machine learning tools currently employed in drug design. This book serves as a valuable resource for researchers, scientists, and practitioners in the pharmaceutical industry, providing a comprehensive presentation of chemometrics, cheminformatics, and machine learning methods.

The first part of the content focuses on establishing the foundations of the area. Here, recent trends in computational modeling of drugs are presented, encompassing topics such as QSAR in medicinal chemistry, structure-based methods, chemoinformatics and chemometric approaches, and machine learning methods in drug design. This section provides a solid foundation for the subsequent chapters, enabling readers to gain a deep understanding of the computational tools and techniques employed in drug discovery.

The second part of the book delves into methods and case studies, encompassing a wide range of topics. Molecular descriptors, molecular similarity, structure-based based screening, homology modeling in protein structure predictions, molecular docking, stability of drug receptor interactions, deep learning, and support vector machine in drug design are all explored in detail. These methods and case studies provide practical insights into the application of computational tools in drug design, enabling readers to apply these techniques to their own research and development efforts.

The third part of the book is dedicated to special topics, including dedicated chapters on topics ranging from de design of green pharmaceuticals to computational toxicology. These chapters offer in-depth discussions on emerging trends and challenges in drug design, providing valuable insights into the future of this field.

The final part of the book is dedicated to presenting the available tools and databases, including QSAR databases, free tools and databases in ligand and structure-based drug design, and machine learning resources for drug design. This section provides a comprehensive overview of the resources available to researchers and practitioners, enabling them to access the latest information and tools in drug design.

The final chapters of the book discuss different web servers used for identification of various drug candidates. These web servers serve as valuable resources for researchers, enabling them to access a wide range of data and information related to drug discovery.

In conclusion, Cheminformatics, QSAR, and Machine Learning Applications for Novel Drug Development is a comprehensive and authoritative guide that provides a comprehensive presentation of chemometrics, cheminformatics, and machine learning methods in drug design. This book is an essential resource for researchers, scientists, and practitioners in the pharmaceutical industry, enabling them to stay up-to-date with the latest trends and techniques in drug discovery.

Weight: 1566g
Dimension: 192 x 237 x 116 (mm)
ISBN-13: 9780443186387