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Drug design

Harnessing deep learning to build optimized ligands

Nature Computational Science volume 4, pages 809–810 (2024)Cite this article

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A recent study proposes DeepBlock, a deep learning-based approach for generating ligands with targeted properties, such as low toxicity and high affinity with the given target. This approach outperforms existing methods in the field while maintaining synthetic accessibility and drug-likeness.

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Fig. 1: The DeepBlock framework and individual parts.

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Author notes

  1. These authors contributed equally: Orestis A. Ntintas, Theodoros Daglis.

Authors and Affiliations

  1. Molecular Carcinogenesis Group, Department of Histology and Embryology, Medical School, National and Kapodistrian University of Athens, Athens, Greece

    Orestis A. Ntintas, Theodoros Daglis & Vassilis G. Gorgoulis

  2. Intelligencia Inc, New York, NY, USA

    Orestis A. Ntintas & Vassilis G. Gorgoulis

  3. School of Product Engineering & Management, Technical University of Crete, Chania, Greece

    Theodoros Daglis

  4. School of Applied Mathematical and Physical Sciences, National Technical University of Athens, Athens, Greece

    Theodoros Daglis

  5. Ninewells Hospital and Medical School, University of Dundee, Dundee, UK

    Vassilis G. Gorgoulis

  6. Biomedical Research Foundation, Academy of Athens, Athens, Greece

    Vassilis G. Gorgoulis

  7. Faculty Institute for Cancer Sciences, Manchester Academic Health Sciences Centre, University of Manchester, Manchester, UK

    Vassilis G. Gorgoulis

Authors
  1. Orestis A. Ntintas
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  2. Theodoros Daglis
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  3. Vassilis G. Gorgoulis
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Corresponding author

Correspondence to Vassilis G. Gorgoulis.

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The authors declare no competing interests.

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Ntintas, O.A., Daglis, T. & Gorgoulis, V.G. Harnessing deep learning to build optimized ligands. Nat Comput Sci 4, 809–810 (2024). https://doi.org/10.1038/s43588-024-00725-1

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  • DOI: https://doi.org/10.1038/s43588-024-00725-1

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