Exhaustive local chemical space exploration using a transformer model

Alessandro Tibo (), Jiazhen He, Jon Paul Janet, Eva Nittinger and Ola Engkvist
Additional contact information
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

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-51672-4

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