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Substructure-based neural machine translation for retrosynthetic prediction.

Umit V Ucak1, Taek Kang2, Junsu Ko3

  • 1Division of Chemistry and Biochemistry, Department of Chemistry, Kangwon National University, Chuncheon, South Korea.

Journal of Cheminformatics
|January 12, 2021
PubMed
Summary

This study introduces a novel template-free neural machine translation model for retrosynthesis planning. The approach uses molecular fragments for better prediction accuracy and avoids invalid chemical string generation.

Keywords:
AttentionNeural machine translationRetrosynthesis planningSeq-to-seq

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Area of Science:

  • Computational Chemistry
  • Artificial Intelligence in Chemistry

Background:

  • Neural machine translation (NMT) shows promise for retrosynthesis planning.
  • Existing NMT models for retrosynthesis often translate SMILES strings, facing limitations.

Purpose of the Study:

  • To develop a template-free NMT model for retrosynthesis planning.
  • To improve the accuracy and robustness of predicting synthetic pathways.

Main Methods:

  • Recasting retrosynthesis as a language translation problem using a template-free sequence-to-sequence model.
  • Training the model end-to-end in a data-driven manner.
  • Representing chemical reactions using molecular fragments instead of SMILES strings.

Main Results:

  • The new approach achieves higher prediction accuracy than current state-of-the-art methods.
  • Predicts highly similar reactant molecules with 57.7% accuracy.
  • Resolves issues of generating invalid SMILES strings and offers more robust predictions.

Conclusions:

  • The molecular fragment-based NMT approach significantly enhances retrosynthesis planning.
  • This data-driven method offers a more reliable and accurate computational tool for chemists.