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Image of - Artificial Intelligence and Machine Learning in Drug Design and Development | Hardcover – A Must-Have for Scientists, Researchers & Pharma Experts
Artificial Intelligence and Machine Learning in Drug Design and Development | Hardcover – A Must-Have for Scientists, Researchers & Pharma Experts

Artificial Intelligence and Machine Learning in Drug Design and Development | Hardcover – A Must-Have for Scientists, Researchers & Pharma Experts

by Khanna A.

Artificial Intelligence and Machine Learning in Drug Design and Development is a comprehensive hardcover guide that explores the transformative role of AI and ML in pharmaceutical research. Covering topics like molecular modeling, drug optimization, and AI-driven drug repurposing, this book provides expert insights and real-world case studies, making it an essential resource for researchers, scientists, and industry professionals in the pharmaceutical and biotech fields.

Highlights

  • binding-icon

    9781394234165

    ISBN

  • binding-icon

    Khanna A.

    Author

  • binding-icon

    672

    Pages

  • binding-icon

    1360 gm

    Weight

  • langauage-icon

    English

    Language

  • date-icon

    2024

    Year

  • edition-icon

    N/A

    Edition

  • binding-icon

    Hardcover

    Binding

₹18470

₹21734

Discover how Artificial Intelligence (AI) and Machine Learning (ML) are transforming modern drug discovery and development.

This comprehensive hardcover volume explores the latest applications of AI and ML across the pharmaceutical pipeline — from molecular modeling and docking to compound selection, target identification, and drug optimization.

The book brings together leading experts in AI, machine learning, and pharmaceutical sciences to present real-world case studies, advanced techniques, and a forward-looking perspective on how data-driven innovation is reshaping healthcare.

Topics include:

  • AI in drug repurposing and predictive safety.
  • Improving productivity in pharmaceutical R&D.
  • Using big data and algorithms to optimize drug delivery.
  • Real-life case studies and expert insights across 19 chapters.

Audience:

Ideal for researchers, scientists, industry professionals, engineers, and IT experts interested in the intersection of technology and life sciences.

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