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Reaction Templates: Bridging Synthesis Knowledge and Artificial Intelligence.

Shuan Chen1, Juhwan Noh2, Jidon Jang3

  • 1Department of Chemical and Biological Engineering, and Institute of Chemical Process, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, South Korea.

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This study integrates chemical synthesis knowledge into deep learning models using reaction templates, improving AI

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Area of Science:

  • * Computational chemistry and cheminformatics.
  • * Artificial intelligence in chemical synthesis.

Background:

  • * Traditional chemical synthesis planning relied on expert systems, which were difficult to scale and adapt.
  • * Recent deep learning models show promise but often lack integration with established chemical knowledge, hindering interpretability.

Purpose of the Study:

  • * To bridge the gap between deep learning and chemical intuition by incorporating reaction templates.
  • * To develop AI models that leverage known synthesis rules for improved performance and interpretability.

Main Methods:

  • * Utilizing reaction templates encoded in SMARTS (SMILES arbitrary target specification) format for Python-based AI models.
  • * Automating the extraction of reaction templates from large chemical reaction databases.
  • * Developing specialized AI models: LocalRetro for retrosynthesis, LocalTransform for reaction outcome prediction, and LocalMapper for atom-to-atom mapping.

Main Results:

  • * Demonstrated the effectiveness of SMARTS-based reaction templates in AI models for molecule optimization.
  • * Automated template extraction reduced manual effort and potential errors in knowledge encoding.
  • * Developed AI models (LocalRetro, LocalTransform, LocalMapper) that align with chemists' intuition and improve task-specific performance.

Conclusions:

  • * Integrating known synthesis knowledge via reaction templates enhances deep learning model performance and interpretability.
  • * This approach shows potential for advancing AI in chemical synthesis, including reaction mechanism prediction and inorganic synthesis.