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EnzRetro: Enzymatic Retrosynthetic Planning With Site-Specific Reaction Edits Based on Sequence Generative

Yahui Cao1,2, Haoshu Chen1, Tao Zhang1,3

  • 1School of Electrical and Information Engineering Tianjin University Tianjin China.

Exploration (Beijing, China)
|April 22, 2026
PubMed
Summary

EnzRetro is a novel framework for enzymatic retrosynthesis, integrating pathway synthesis and enzyme identification. It uses site-specific reaction edits (SSREdits) to improve computational efficiency and accuracy in green chemistry applications.

Keywords:
biosynthesisenzymatic retrosynthesisenzyme identificationlarge language model

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Area of Science:

  • Computational chemistry
  • Green chemistry
  • Biotechnology

Background:

  • Enzymatic biosynthesis is vital for green chemistry but faces challenges in integrating enzyme identification with pathway synthesis.
  • Current computational tools lack efficiency due to enzyme specificity and complex substrate interactions.

Purpose of the Study:

  • To develop EnzRetro, a novel framework for enzymatic retrosynthesis, bridging pathway planning and enzymatic engineering.
  • To create an end-to-end solution for enzyme identification and reaction pathway synthesis.

Main Methods:

  • Introduced site-specific reaction edits (SSREdits) for dynamic representation of structural transformations at enzyme active sites.
  • Developed three pretraining tasks and fine-tuned SSREdits generation and EC generation models.
  • Integrated retrosynthesis and enzyme identification into a one-pot learning framework.

Main Results:

  • Achieved 56.1% Top-1 accuracy for retrosynthesis on USPTO-50k and 97.7% for enzyme identification on ECREACT.
  • Validated EnzRetro's accuracy through three enzymatic pathways.
  • Developed a web platform demonstrating diverse enzymatic pathways for putrescine biosynthesis.

Conclusions:

  • EnzRetro enhances computational efficiency and interpretability by combining retrosynthesis and enzyme identification.
  • The framework successfully bridges the gap between pathway synthesis and enzyme identification.
  • EnzRetro significantly outperforms state-of-the-art baselines in enzymatic retrosynthesis planning.