Exhaustive local chemical space exploration using a transformer model
Journal article, 2024

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.

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Author

Alessandro Tibo

AstraZeneca R&D

Jiazhen He

AstraZeneca R&D

Jon Paul Janet

AstraZeneca R&D

Eva Nittinger

AstraZeneca R&D

Ola Engkvist

AstraZeneca AB

Chalmers, Computer Science and Engineering (Chalmers)

Nature Communications

2041-1723 (ISSN) 20411723 (eISSN)

Vol. 15 1 7315

Subject Categories

Chemical Sciences

DOI

10.1038/s41467-024-51672-4

PubMed

39183239

Related datasets

PubChem and ChEMBL-series processed dataset used in Exhaustive local chemical space exploration using a transformer model [dataset]

DOI: 10.5281/zenodo.12818281

More information

Latest update

9/26/2024