RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions

Xiaorui Wang, Yuquan Li, Jiezhong Qiu, Guangyong Chen, Huanxiang Liu, Benben Liao, Chang Yu Hsieh, Xiaojun Yao

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)

Abstract

Retrosynthesis prediction is a crucial task for organic synthesis. In this work, we propose a single-step template-free and Transformer-based method dubbed RetroPrime, integrating chemists’ retrosynthetic strategy of (1) decomposing a molecule into synthons then (2) generating reactants by attaching leaving groups. These two stages are accomplished with versatile Transformer models, respectively. RetroPrime achieves the Top-1 accuracy of 64.8% and 51.4%, when the reaction type is known and unknown, respectively, in the USPTO-50 K dataset. And the Top-1 accuracy is close to the state-of-the-art transformer-based method in the large dataset USPTO-full. It is known that outputs of the Transformer-based retrosynthesis model tend to suffer from insufficient diversity and high chemical implausibility. These problems may limit the potential of Transformer-based methods in real practice, yet few works address both issues simultaneously. RetroPrime is designed to tackle these challenges.

Original languageEnglish
Article number129845
JournalChemical Engineering Journal
Volume420
DOIs
Publication statusPublished - 15 Sept 2021
Externally publishedYes

Keywords

  • Deep Learning
  • Natural Language Processing
  • Template-free Single-Step Retrosynthesis

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