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Exhaustive local chemical space exploration using a transformer model

Author

Listed:
  • Alessandro Tibo

    (R&D, AstraZeneca)

  • Jiazhen He

    (R&D, AstraZeneca)

  • Jon Paul Janet

    (R&D, AstraZeneca)

  • Eva Nittinger

    (BioPharmaceuticals R&D AstraZeneca)

  • Ola Engkvist

    (R&D, AstraZeneca
    Computer Science and Engineering, Chalmers)

Abstract

How many near-neighbors does a molecule have? This fundamental question in chemistry is crucial for molecular optimization problems under the similarity principle assumption. Generative models can sample molecules from a vast chemical space but lack explicit knowledge about molecular similarity. Therefore, these models need guidance from reinforcement learning to sample a relevant similar chemical space. However, they still miss a mechanism to measure the coverage of a specific region of the chemical space. To overcome these limitations, a source-target molecular transformer model, regularized via a similarity kernel function, is proposed. Trained on a largest dataset of ≥200 billion molecular pairs, the model enforces a direct relationship between generating a target molecule and its similarity to a source molecule. Results indicate that the regularization term significantly improves the correlation between generation probability and molecular similarity, enabling exhaustive exploration of molecule near-neighborhoods.

Suggested Citation

  • Alessandro Tibo & Jiazhen He & Jon Paul Janet & Eva Nittinger & Ola Engkvist, 2024. "Exhaustive local chemical space exploration using a transformer model," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51672-4
    DOI: 10.1038/s41467-024-51672-4
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    References listed on IDEAS

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    1. Alessandro Tibo & Jiazhen He & Jon Paul Janet & Eva Nittinger & Ola Engkvist, 2024. "Exhaustive local chemical space exploration using a transformer model," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. O. Anatole von Lilienfeld & Kieron Burke, 2020. "Retrospective on a decade of machine learning for chemical discovery," Nature Communications, Nature, vol. 11(1), pages 1-4, December.
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    1. Alessandro Tibo & Jiazhen He & Jon Paul Janet & Eva Nittinger & Ola Engkvist, 2024. "Exhaustive local chemical space exploration using a transformer model," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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