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Holistic models of reaction selectivity

Computational models that predict the selectivity of reactions are typically accurate for only a specific reaction type and a narrow range of reaction components. A more general model has now been reported.
  1. Per-Ola Norrby

    1. Per-Ola Norrby is in Data Science & Modelling, Pharmaceutical Sciences, R&D, AstraZeneca Gothenburg, 43183 Mölndal, Sweden.

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Selectivity is a linchpin of chemical synthesis — if a synthetic reaction is not selective, it cannot give a good yield of the desired product, and will require tedious purification processes. Chemists have therefore long sought ways of predicting the selectivity of chemical reactions. Computational models can be constructed, but their development is laborious, and they are usually specific to a particular reaction type. Writing in Nature, Reid and Sigman1 now show that a selectivity model can be built in a semi-automated way and generalized over a range of reactions.

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Nature 571, 332-333 (2019)

doi: https://doi.org/10.1038/d41586-019-02148-9

References

  1. Reid, J. P. & Sigman, M. S. Nature 571, 343–348 (2019).

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  2. Brown, J. M. & Deeth, R. J. Angew. Chem. Int. Ed. 48, 4476–4479 (2009).

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  3. Reid, J. P. & Sigman, M. S. Nature Rev. Chem. 2, 290–305 (2018).

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