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Opportunities and challenges in phenotypic drug discovery: An industry perspective

Nature Reviews Drug Discovery, ISSN: 1474-1784, Vol: 16, Issue: 8, Page: 531-543
2017
  • 675
    Citations
  • 0
    Usage
  • 1,312
    Captures
  • 6
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    675
    • Citation Indexes
      671
    • Patent Family Citations
      2
      • Patent Families
        2
    • Policy Citations
      2
      • Policy Citation
        2
  • Captures
    1,312
  • Mentions
    6
    • References
      3
      • Wikipedia
        3
    • News Mentions
      2
      • News
        2
    • Blog Mentions
      1
      • Blog
        1

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Nature Reviews Drug Discovery contents August 2017 Volume 16 Number 8 pp 513-586

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Most Recent News

Biological sciences / Biomolecules & Biomedicine, "Artificial intelligence driven innovations in biochemistry: A review of emerging research frontiers."

Introduction Research combining artificial intelligence (AI) and biochemistry has rapidly progressed over the past decade, transforming the field. Initial AI applications were primarily in bioinformatics,

Review Description

Phenotypic drug discovery (PDD) approaches do not rely on knowledge of the identity of a specific drug target or a hypothesis about its role in disease, in contrast to the target-based strategies that have been widely used in the pharmaceutical industry in the past three decades. However, in recent years, there has been a resurgence in interest in PDD approaches based on their potential to address the incompletely understood complexity of diseases and their promise of delivering first-in-class drugs, as well as major advances in the tools for cell-based phenotypic screening. Nevertheless, PDD approaches also have considerable challenges, such as hit validation and target deconvolution. This article focuses on the lessons learned by researchers engaged in PDD in the pharmaceutical industry and considers the impact of 'omics' knowledge in defining a cellular disease phenotype in the era of precision medicine, introducing the concept of a chain of translatability. We particularly aim to identify features and areas in which PDD can best deliver value to drug discovery portfolios and can contribute to the identification and the development of novel medicines, and to illustrate the challenges and uncertainties that are associated with PDD in order to help set realistic expectations with regard to its benefits and costs.

Bibliographic Details

Moffat, John G; Vincent, Fabien; Lee, Jonathan A; Eder, Jörg; Prunotto, Marco

Springer Science and Business Media LLC

Pharmacology, Toxicology and Pharmaceutics

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