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Elsevier Machine Learning in Drug Development Part 1 Volume 64

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Description

Machine Learning in Drug Development: Part One, Volume 64 in the Annual Reports on Medicinal Chemistry series, presents cutting-edge advances in applying artificial intelligence and machine learning to pharmaceutical research. This comprehensive volume explores AI applications across multiple drug discovery domains, including small molecule and nucleic acid research, protein degrader design and optimisation, and antiviral drug development. Chapters cover critical topics such as lead identification, biology validation, disease association analysis, and drug repurposing strategies enhanced by machine learning approaches. Readers will discover how transforming modern drug discovery with machine learning technologies accelerates research timelines and improves compound selection. Published by Academic Press, this authoritative reference combines expert insights into practical AI implementation in medicinal chemistry, making it essential for researchers, scientists, and professionals advancing pharmaceutical innovation through computational intelligence.

Key Features

Volume 64 in the Annual Reports on Medicinal Chemistry series delivers the latest machine learning breakthroughs in pharmaceutical research and drug discovery applications.

Chapters cover artificial intelligence in small molecule and nucleic acid research, providing comprehensive review of computational techniques in modern chemistry.

Explores AI-aided drug development for protein degraders, including design strategies, lead identification, optimisation, and biology validation methods.

Includes disease-association analysis and drug repurposing chapters demonstrating how machine learning identifies new therapeutic applications for existing compounds.

Addresses artificial intelligence in antiviral drug development, showcasing progress and practical implementations in infectious disease research.

Published by Academic Press, a trusted academic publisher delivering peer-reviewed scientific content for researchers and professionals worldwide.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
24 October 2025
Listed Since
12 March 2025

Barcode

No barcode data available

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