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Article dans une revue

Reinforcement learning for bioretrosynthesis

Abstract : Metabolic engineering aims to produce chemicals of interest from living organisms, to advance towards greener chemistry. Despite efforts, the research and development process is still long and costly and efficient computational design tools are required to explore the chemical biosynthetic space. Here, we propose to explore the bio-retrosynthesis space using an Artificial Intelligence based approach relying on the Monte Carlo Tree Search reinforcement learning method, guided by chemical similarity. We implement this method in RetroPath RL, an open-source and modular command line tool. We validate it on a golden dataset of 20 manually curated experimental pathways as well as on a larger dataset of 152 successful metabolic engineering projects. Moreover, we provide a novel feature, that suggests potential media supplements to complement the enzymatic synthesis plan.
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Article dans une revue
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Déposant : Jean-Loup Faulon <>
Soumis le : jeudi 23 juillet 2020 - 11:02:14
Dernière modification le : mercredi 19 août 2020 - 13:28:02



Mathilde Koch, Thomas Duigou, Jean-Loup Faulon. Reinforcement learning for bioretrosynthesis. ACS Synthetic Biology, American Chemical Society, 2019, 9 (1), pp.157-168. ⟨10.1021/acssynbio.9b00447⟩. ⟨hal-02905157⟩



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