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Practical considerations for active machine learning in drug discovery.

Publication ,  Journal Article
Reker, D
Published in: Drug discovery today. Technologies
December 2019

Active machine learning enables the automated selection of the most valuable next experiments to improve predictive modelling and hasten active retrieval in drug discovery. Although a long established theoretical concept and introduced to drug discovery approximately 15 years ago, the deployment of active learning technology in the discovery pipelines across academia and industry remains slow. With the recent re-discovered enthusiasm for artificial intelligence as well as improved flexibility of laboratory automation, active learning is expected to surge and become a key technology for molecular optimizations. This review recapitulates key findings from previous active learning studies to highlight the challenges and opportunities of applying adaptive machine learning to drug discovery. Specifically, considerations regarding implementation, infrastructural integration, and expected benefits are discussed. By focusing on these practical aspects of active learning, this review aims at providing insights for scientists planning to implement active learning workflows in their discovery pipelines.

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Published In

Drug discovery today. Technologies

DOI

EISSN

1740-6749

ISSN

1740-6749

Publication Date

December 2019

Volume

32-33

Start / End Page

73 / 79

Related Subject Headings

  • Supervised Machine Learning
  • Pharmacology & Pharmacy
  • Humans
  • Drug Discovery
 

Citation

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ICMJE
MLA
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Reker, D. (2019). Practical considerations for active machine learning in drug discovery. Drug Discovery Today. Technologies, 3233, 73–79. https://doi.org/10.1016/j.ddtec.2020.06.001
Reker, Daniel. “Practical considerations for active machine learning in drug discovery.Drug Discovery Today. Technologies 32–33 (December 2019): 73–79. https://doi.org/10.1016/j.ddtec.2020.06.001.
Reker D. Practical considerations for active machine learning in drug discovery. Drug discovery today Technologies. 2019 Dec;32–33:73–9.
Reker, Daniel. “Practical considerations for active machine learning in drug discovery.Drug Discovery Today. Technologies, vol. 32–33, Dec. 2019, pp. 73–79. Epmc, doi:10.1016/j.ddtec.2020.06.001.
Reker D. Practical considerations for active machine learning in drug discovery. Drug discovery today Technologies. 2019 Dec;32–33:73–79.
Journal cover image

Published In

Drug discovery today. Technologies

DOI

EISSN

1740-6749

ISSN

1740-6749

Publication Date

December 2019

Volume

32-33

Start / End Page

73 / 79

Related Subject Headings

  • Supervised Machine Learning
  • Pharmacology & Pharmacy
  • Humans
  • Drug Discovery