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Poster De Conférence Année : 2024

Artificial Intelligence Methods and Models for Retro-Biosynthesis

Résumé

Retrosynthesis aims to efficiently plan the synthesis of desirable chemicals by strategically breaking down molecules into readily available building block compounds. Having a long history in chemistry, retro-biosynthesis has also been used in the fields of biocatalysis and synthetic biology. Artificial intelligence (AI) is driving us towards new frontiers in synthesis planning and the exploration of chemical spaces, arriving at an opportune moment for promoting bioproduction that would better align with green chemistry, enhancing environmental practices. In this review, we summarize the recent advancements in the application of AI methods and models for retrosynthetic and retro-biosynthetic pathway design. These techniques can be based either on reaction templates or generative models and require scoring functions and planning strategies to navigate through the retrosynthetic graph of possibilities. We finally discuss limitations and promising research directions in this field.
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Dates et versions

hal-04603192 , version 1 (06-06-2024)

Identifiants

  • HAL Id : hal-04603192 , version 1

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Guillaume Gricourt, Philippe Meyer, Thomas Duigou, Jean-Loup Faulon. Artificial Intelligence Methods and Models for Retro-Biosynthesis. AI Methods and Models for (bio)catalysis and Synthetic Biology, Jun 2024, Montpellier, France. ⟨hal-04603192⟩
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