Description

​This book presents drug repurposing strategies to combat infectious diseases and cancer. It discusses key experimental and in silico approaches for modern drug repositioning, including signature matching, molecular docking, genome-wide associated studies, and network-based approaches aided by artificial intelligence. Further, the book presents various computational and experimental strategies for better understanding disease mechanisms and identify repurposed drug candidates for personalized pharmacotherapy. It also explores the databases for drug repositioning, summarizes the approaches taken for drug repositioning, and highlights and compares their characteristics and challenges. Towards the end, the book discusses challenges and limitations encountered in computational drug repositioning.

Drug Repurposing for Emerging Infectious Diseases and Cancer

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£179.99

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Hardback by Ranbir Chander Sobti , Sunil K. Lal

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​This book presents drug repurposing strategies to combat infectious diseases and cancer. It discusses key experimental and in silico approaches... Read more

    Publisher: Springer Verlag, Singapore
    Publication Date: 08/02/2023
    ISBN13: 9789811953989, 978-9811953989
    ISBN10: 9811953988

    Number of Pages: 654

    Non Fiction , Education

    Description

    ​This book presents drug repurposing strategies to combat infectious diseases and cancer. It discusses key experimental and in silico approaches for modern drug repositioning, including signature matching, molecular docking, genome-wide associated studies, and network-based approaches aided by artificial intelligence. Further, the book presents various computational and experimental strategies for better understanding disease mechanisms and identify repurposed drug candidates for personalized pharmacotherapy. It also explores the databases for drug repositioning, summarizes the approaches taken for drug repositioning, and highlights and compares their characteristics and challenges. Towards the end, the book discusses challenges and limitations encountered in computational drug repositioning.

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