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Computational drug design

Taking a deep dive with active learning for drug discovery

Nature Computational Science volume 4, pages 727–728 (2024)Cite this article

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Active machine learning is employed in academia and industry to support drug discovery. A recent study unravels the factors that influence a deep learning models’ ability to guide iterative discovery.

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Fig. 1: Active learning cycle and evaluated parameters to determine best practices for active deep learning in drug discovery.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Duke University, Durham, NC, USA

    Zachary Fralish & Daniel Reker

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  1. Zachary Fralish
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  2. Daniel Reker
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Correspondence to Daniel Reker.

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Competing interests

D.R. acts as a consultant to the pharmaceutical and biotechnology industry, as a mentor for Start2, and on the scientific advisory board of Areteia Therapeutics. Z.F. has no competing interests.

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Fralish, Z., Reker, D. Taking a deep dive with active learning for drug discovery. Nat Comput Sci 4, 727–728 (2024). https://doi.org/10.1038/s43588-024-00704-6

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