IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-50469-9.html
   My bibliography  Save this article

Chemical language modeling with structured state space sequence models

Author

Listed:
  • Rıza Özçelik

    (Institute for Complex Molecular Systems and Department of Biomedical Engineering, Eindhoven University of Technology
    Alliance TU/e, WUR, UU, UMC Utrecht)

  • Sarah Ruiter

    (Institute for Complex Molecular Systems and Department of Biomedical Engineering, Eindhoven University of Technology)

  • Emanuele Criscuolo

    (Institute for Complex Molecular Systems and Department of Biomedical Engineering, Eindhoven University of Technology)

  • Francesca Grisoni

    (Institute for Complex Molecular Systems and Department of Biomedical Engineering, Eindhoven University of Technology
    Alliance TU/e, WUR, UU, UMC Utrecht)

Abstract

Generative deep learning is reshaping drug design. Chemical language models (CLMs) – which generate molecules in the form of molecular strings – bear particular promise for this endeavor. Here, we introduce a recent deep learning architecture, termed Structured State Space Sequence (S4) model, into de novo drug design. In addition to its unprecedented performance in various fields, S4 has shown remarkable capabilities to learn the global properties of sequences. This aspect is intriguing in chemical language modeling, where complex molecular properties like bioactivity can ‘emerge’ from separated portions in the molecular string. This observation gives rise to the following question: Can S4 advance chemical language modeling for de novo design? To provide an answer, we systematically benchmark S4 with state-of-the-art CLMs on an array of drug discovery tasks, such as the identification of bioactive compounds, and the design of drug-like molecules and natural products. S4 shows a superior capacity to learn complex molecular properties, while at the same time exploring diverse scaffolds. Finally, when applied prospectively to kinase inhibition, S4 designs eight of out ten molecules that are predicted as highly active by molecular dynamics simulations. Taken together, these findings advocate for the introduction of S4 into chemical language modeling – uncovering its untapped potential in the molecular sciences.

Suggested Citation

  • Rıza Özçelik & Sarah Ruiter & Emanuele Criscuolo & Francesca Grisoni, 2024. "Chemical language modeling with structured state space sequence models," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50469-9
    DOI: 10.1038/s41467-024-50469-9
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-50469-9
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-50469-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Daniel Flam-Shepherd & Kevin Zhu & Alán Aspuru-Guzik, 2022. "Language models can learn complex molecular distributions," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Juan-Ni Wu & Tong Wang & Yue Chen & Li-Juan Tang & Hai-Long Wu & Ru-Qin Yu, 2024. "t-SMILES: a fragment-based molecular representation framework for de novo ligand design," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50469-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.