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CHEMOINFORMATICS

Transformers for future medicinal chemists

Chemical reactions can be grouped into classes, but determining what class a specific reaction belongs to is not trivial on a large-scale. A new study demonstrates data-driven automatic classification of chemical reactions with methods borrowed from natural language processing.

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Fig. 1: An interactive reaction atlas visualizing clusters of the chemical reactions with the aim of making them more interpretable.

References

  1. Boström, J., Brown, D. G., Young, R. & Keserü, G. M. Nat. Rev. Drug Discov. 17, 709–727 (2018).

    Article  Google Scholar 

  2. Schwaller, P. et al. Nat. Mach. Intell. https://doi.org/10.1038/s42256-020-00284-w (2020).

  3. NameRXN (Nextmove Software, accessed 22 December 2020); http://www.nextmovesoftware.com/namerxn.html

  4. Kraut, H. et al. J. Chem. Inf. Model. 53, 2884–2895 (2013).

    Article  Google Scholar 

  5. SMIRKS (Daylight, accessed 22 December 2020); https://www.daylight.com/dayhtml/doc/theory/theory.smirks.html

  6. Vaswani, A. et al. in Advances in Neural Information Processing Systems 5998–6008 (NeurIPS, 2017).

  7. Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. in Proc. Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies Vol. 1 4171–4186 (2019).

  8. Probst, D. & Reymond, J.-L. J. Cheminform. 12, 12 (2020).

    Article  Google Scholar 

  9. The Interactive TMAP (GitHub, accessed 22 December 2020); https://rxn4chemistry.github.io/rxnfp//tmaps/tmap_ft_10k.html

  10. Jorner, K., Brinck, T., Norrby, P.-O. & Buttar, D. Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies. Chem. Sci. https://doi.org/10.1039/D0SC04896H (2021).

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Correspondence to Jonas Boström.

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Boström, J. Transformers for future medicinal chemists. Nat Mach Intell 3, 102–103 (2021). https://doi.org/10.1038/s42256-021-00299-x

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