Nature Communications (Feb 2022)

Biocatalysed synthesis planning using data-driven learning

  • Daniel Probst,
  • Matteo Manica,
  • Yves Gaetan Nana Teukam,
  • Alessandro Castrogiovanni,
  • Federico Paratore,
  • Teodoro Laino

DOI
https://doi.org/10.1038/s41467-022-28536-w
Journal volume & issue
Vol. 13, no. 1
pp. 1 – 11

Abstract

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As of now, only rule-based systems support retrosynthetic planning using biocatalysis, while initial data-driven approaches are limited to forward predictions. Here, the authors extend the data-driven forward reaction as well as retrosynthetic pathway prediction models based on the Molecular Transformer architecture to biocatalysis.