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Toward Lab-Ready AI Synthesis Plans with Protection Strategies and Route Scoring.

Annie M Westerlund1, Lukas M Sigmund1, Marco V Mijangos2

  • 1Molecular AI, Discovery Sciences, R&D, AstraZeneca Gothenburg, Pepparedsleden 1, 43183 Mölndal, Sweden.

Journal of Chemical Information and Modeling
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

AI tools for molecule synthesis planning now feature automated protection strategies. This innovation addresses selectivity issues in reaction trees, improving efficiency for chemists and accelerating drug discovery.

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Area of Science:

  • Chemical synthesis
  • Computational chemistry
  • Machine learning in chemistry

Background:

  • AI-driven synthesis planning tools aid novel molecule creation.
  • These tools often generate reaction trees with selectivity issues due to competing reactive sites.
  • Manual postprocessing for protection strategies hinders full automation and efficiency.

Purpose of the Study:

  • To develop automated routines for identifying competing sites and formulating context-aware protection strategies.
  • To enhance AI-driven synthesis planning by addressing selectivity issues and improving route quality.
  • To accelerate industrial drug discovery workflows through improved synthesis planning.

Main Methods:

  • Leveraging machine learning and encoded human chemical knowledge (rules and data).
  • Considering cross-functional-group competition, protecting group selection, and reaction tree structure.
  • Implementing orthogonal and multistep protection strategies.
  • Proposing a competing sites score for analyzing and reranking reaction trees.

Main Results:

  • Substantial reduction in selectivity issues in AiZynthFinder-generated reaction trees.
  • Improved quality of synthesis routes provided to users.
  • Modest increase in computation time per target molecule.
  • Demonstrated effectiveness of context-aware protecting group strategies.

Conclusions:

  • Automated selectivity control via context-aware protecting group strategies enhances AI-driven synthesis planning.
  • The framework significantly improves the efficiency and quality of AI-generated synthesis routes.
  • This approach facilitates the acceleration of industrial drug discovery processes.