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Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge.

Shu-Wen Li1, Li-Cheng Xu1, Cheng Zhang2

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This study introduces a knowledge-based graph model for predicting chemical reaction outcomes. The model accurately predicts reaction yield and stereoselectivity, offering insights for synthetic chemistry development.

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Area of Science:

  • Computational Chemistry
  • Organic Synthesis
  • Machine Learning

Background:

  • Predicting chemical reactivity and selectivity is crucial for efficient synthetic development.
  • Current predictive models struggle with high-dimensional molecular data, limiting extrapolative ability and interpretability.
  • Bridging the gap between chemical domain knowledge and advanced molecular graph models is essential.

Purpose of the Study:

  • To develop a knowledge-based graph model for predicting synthetic transformation performance.
  • To embed digitalized steric and electronic information into a molecular graph model.
  • To enable learning of synergistic effects between reaction components.

Main Methods:

  • Developed a knowledge-based graph model incorporating digitalized steric and electronic properties.
  • Integrated a molecular interaction module to capture synergistic influences of reaction components.
  • Validated model predictions with scaffold-based data splitting and experimental verification.

Main Results:

  • Achieved excellent predictions for reaction yield and stereoselectivity.
  • Demonstrated strong extrapolative ability through rigorous validation methods.
  • Provided atomic-level interpretation of steric and electronic influences on synthetic outcomes.

Conclusions:

  • The knowledge-based graph model offers an accurate, extrapolative, and interpretable approach for reaction performance prediction.
  • Embedding chemical knowledge into reaction modeling is vital for synthetic applications.
  • The model serves as a guide for molecular engineering to achieve desired synthetic functions.