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Leveraging molecular structure and bioactivity with chemical language models for de novo drug design

Author

Listed:
  • Michael Moret

    (ETH Zurich, Department of Chemistry and Applied Biosciences)

  • Irene Pachon Angona

    (ETH Zurich, Department of Chemistry and Applied Biosciences)

  • Leandro Cotos

    (ETH Zurich, Department of Chemistry and Applied Biosciences)

  • Shen Yan

    (University of Zurich, University Children’s Hospital, Children’s Research Center, Pediatric Molecular Neuro-Oncology Research)

  • Kenneth Atz

    (ETH Zurich, Department of Chemistry and Applied Biosciences)

  • Cyrill Brunner

    (ETH Zurich, Department of Chemistry and Applied Biosciences)

  • Martin Baumgartner

    (University of Zurich, University Children’s Hospital, Children’s Research Center, Pediatric Molecular Neuro-Oncology Research)

  • Francesca Grisoni

    (ETH Zurich, Department of Chemistry and Applied Biosciences
    Eindhoven University of Technology, Institute for Complex Molecular Systems and Eindhoven Artificial Intelligence Systems Institute, Department of Biomedical Engineering
    Center for 393 Living Technologies, Alliance TU/e, WUR, UU, UMC 394 Utrecht)

  • Gisbert Schneider

    (ETH Zurich, Department of Chemistry and Applied Biosciences
    ETH Singapore SEC Ltd, 1 CREATE Way, #06-01 CREATE Tower)

Abstract

Generative chemical language models (CLMs) can be used for de novo molecular structure generation by learning from a textual representation of molecules. Here, we show that hybrid CLMs can additionally leverage the bioactivity information available for the training compounds. To computationally design ligands of phosphoinositide 3-kinase gamma (PI3Kγ), a collection of virtual molecules was created with a generative CLM. This virtual compound library was refined using a CLM-based classifier for bioactivity prediction. This second hybrid CLM was pretrained with patented molecular structures and fine-tuned with known PI3Kγ ligands. Several of the computer-generated molecular designs were commercially available, enabling fast prescreening and preliminary experimental validation. A new PI3Kγ ligand with sub-micromolar activity was identified, highlighting the method’s scaffold-hopping potential. Chemical synthesis and biochemical testing of two of the top-ranked de novo designed molecules and their derivatives corroborated the model’s ability to generate PI3Kγ ligands with medium to low nanomolar activity for hit-to-lead expansion. The most potent compounds led to pronounced inhibition of PI3K-dependent Akt phosphorylation in a medulloblastoma cell model, demonstrating efficacy of PI3Kγ ligands in PI3K/Akt pathway repression in human tumor cells. The results positively advocate hybrid CLMs for virtual compound screening and activity-focused molecular design.

Suggested Citation

  • Michael Moret & Irene Pachon Angona & Leandro Cotos & Shen Yan & Kenneth Atz & Cyrill Brunner & Martin Baumgartner & Francesca Grisoni & Gisbert Schneider, 2023. "Leveraging molecular structure and bioactivity with chemical language models for de novo drug design," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-022-35692-6
    DOI: 10.1038/s41467-022-35692-6
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    References listed on IDEAS

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    1. Megan M. Kaneda & Karen S. Messer & Natacha Ralainirina & Hongying Li & Christopher J. Leem & Sara Gorjestani & Gyunghwi Woo & Abraham V. Nguyen & Camila C. Figueiredo & Philippe Foubert & Michael C. , 2016. "PI3Kγ is a molecular switch that controls immune suppression," Nature, Nature, vol. 539(7629), pages 437-442, November.
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    Cited by:

    1. Laura Isigkeit & Tim Hörmann & Espen Schallmayer & Katharina Scholz & Felix F. Lillich & Johanna H. M. Ehrler & Benedikt Hufnagel & Jasmin Büchner & Julian A. Marschner & Jörg Pabel & Ewgenij Proschak, 2024. "Automated design of multi-target ligands by generative deep learning," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    2. Kenneth Atz & Leandro Cotos & Clemens Isert & Maria Håkansson & Dorota Focht & Mattis Hilleke & David F. Nippa & Michael Iff & Jann Ledergerber & Carl C. G. Schiebroek & Valentina Romeo & Jan A. Hiss , 2024. "Prospective de novo drug design with deep interactome learning," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    3. Alessio Fallani & Leonardo Medrano Sandonas & Alexandre Tkatchenko, 2024. "Inverse mapping of quantum properties to structures for chemical space of small organic molecules," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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