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Using Machine Learning To Inform Decisions in Drug Discovery: An Industry Perspective

Using Machine Learning To Inform Decisions in Drug Discovery: An Industry Perspective

  • Darren V. S. Green*
    Darren V. S. Green
    Department of Molecular Design, GlaxoSmithKline, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, United Kingdom
    *E-mail: [email protected]
DOI: 10.1021/bk-2019-1326.ch005
    Publication Date (Web):November 20, 2019
    Copyright © 2019 American Chemical Society.
    Machine Learning in Chemistry: Data-Driven Algorithms, Learning Systems, and Predictions
    Chapter 5pp 81-101
    ACS Symposium SeriesVol. 1326
    ISBN13: 9780841235052eISBN: 9780841235045

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    Abstract

    Modern machine-learning techniques have powered a wave of creative approaches that aim to solve or improve long-standing productivity and attrition problems in drug discovery. While industrial practitioners are keen to embrace new technology, it is important for the community to understand the need to produce actionable decisions for scientists in the field and the implications o for how methods and models conceived, built, validated and their benefits quantified.

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