Reinforcement learning for bioretrosynthesis - ANR - Agence nationale de la recherche Accéder directement au contenu
Article Dans Une Revue ACS Synthetic Biology Année : 2019

Reinforcement learning for bioretrosynthesis

Résumé

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.

Dates et versions

hal-02905157 , version 1 (23-07-2020)

Identifiants

Citer

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

Altmetric

Partager

Gmail Facebook X LinkedIn More