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Artificial Intelligence Methods and Models for Retro-Biosynthesis: A Scoping Review.

Guillaume Gricourt1, Philippe Meyer1, Thomas Duigou1

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Summary
This summary is machine-generated.

Artificial intelligence (AI) advances retrosynthesis and retro-biosynthesis for chemical synthesis planning. These AI methods accelerate the discovery of greener chemical production pathways.

Keywords:
artificial intelligenceretro-biosynthesisretrosynthesis

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Area of Science:

  • Chemistry
  • Biotechnology
  • Computer Science

Background:

  • Retrosynthesis strategically breaks down molecules into building blocks for efficient synthesis.
  • Retro-biosynthesis applies similar principles in biocatalysis and synthetic biology.
  • Artificial intelligence (AI) offers new frontiers for chemical synthesis and exploration.

Purpose of the Study:

  • To review recent advancements in AI for retrosynthetic and retro-biosynthetic pathway design.
  • To highlight AI's role in promoting green chemistry and bioproduction.
  • To discuss current limitations and future research directions.

Main Methods:

  • AI methods based on reaction templates or generative models are employed.
  • Scoring functions and planning strategies are utilized to navigate complex synthesis pathways.
  • The review summarizes applications of these AI techniques in pathway design.

Main Results:

  • AI significantly enhances the planning of chemical synthesis and exploration of chemical spaces.
  • AI-driven approaches facilitate bioproduction aligned with green chemistry principles.
  • Recent advancements show promise in designing complex retrosynthetic and retro-biosynthetic pathways.

Conclusions:

  • AI is revolutionizing chemical synthesis planning, particularly in retrosynthesis and retro-biosynthesis.
  • The integration of AI promotes environmentally friendly chemical practices and bioproduction.
  • Further research is needed to address limitations and unlock the full potential of AI in this field.