Machine learning-enabled retrobiosynthesis of molecules

Tianhao Yu, Aashutosh Girish Boob, Michael J. Volk, Xuan Liu, Haiyang Cui, Huimin Zhao

Research output: Contribution to journalReview articlepeer-review


Retrobiosynthesis provides an effective and sustainable approach to producing functional molecules. The past few decades have witnessed a rapid expansion of biosynthetic approaches. With the recent advances in data-driven sciences, machine learning (ML) is enriching the retrobiosynthesis design toolbox and being applied to each step of the synthesis design workflow, including retrosynthesis planning, enzyme identification and engineering, and pathway optimization. The ability to learn from existing knowledge, recognize complex patterns and generalize to the unknown has made ML a promising solution to biological problems. In this Review, we summarize the recent progress in the development of ML models for assisting with molecular synthesis. We highlight the key advantages of ML-based biosynthesis design methods and discuss the challenges and outlook for the further development of ML-based approaches. [Figure not available: see fulltext.]

Original languageEnglish (US)
Pages (from-to)137-151
Number of pages15
JournalNature Catalysis
Issue number2
StatePublished - Feb 2023

ASJC Scopus subject areas

  • Catalysis
  • Bioengineering
  • Biochemistry
  • Process Chemistry and Technology


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