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  • Review Article
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Machine learning-enabled retrobiosynthesis of molecules

Nature Catalysis volume 6, pages 137–151 (2023)Cite this article

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Abstract

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.

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Fig. 1: An overview of an ML-enabled retrobiosynthesis workflow.
Fig. 2: A conceptualized pathway design workflow based on a retrobiosynthesis tool.
Fig. 3: Multi-task transfer learning for retrobiosynthesis.
Fig. 4: Similarity- and ML-based EC number prediction tools.
Fig. 5: An overview of the MLDE workflow.
Fig. 6: Overview of the ML approaches for designing novel enzymes.
Fig. 7: Reaction network optimization in vivo and in vitro.

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Acknowledgements

This work was supported by the Molecule Maker Lab Institute, an AI Research Institutes programme supported by the US National Science Foundation under grant no. 2019897 (H.Z.). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect those of the NSF.

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Authors and Affiliations

  1. Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA

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

  2. Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA

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

  3. NSF Molecule Maker Lab Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Tianhao Yu, Xuan Liu, Haiyang Cui & Huimin Zhao

  4. DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Aashutosh Girish Boob, Michael J. Volk & Huimin Zhao

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Yu, T., Boob, A.G., Volk, M.J. et al. Machine learning-enabled retrobiosynthesis of molecules. Nat Catal 6, 137–151 (2023). https://doi.org/10.1038/s41929-022-00909-w

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