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Reagent prediction with a molecular transformer improves reaction data quality.

Mikhail Andronov1,2, Varvara Voinarovska3, Natalia Andronova4

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This study introduces a novel reagent prediction model for automated synthesis planning. The Molecular Transformer enhances generative chemistry by accurately predicting reaction conditions, improving product prediction accuracy.

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Area of Science:

  • Computational chemistry
  • Organic synthesis
  • Artificial intelligence in chemistry

Background:

  • Automated synthesis planning is crucial for generative chemistry.
  • Current software often lacks reaction condition recommendations, relying on human expertise.
  • Reagent prediction for arbitrary reactions has been a significant gap in cheminformatics.

Purpose of the Study:

  • To develop a computational model for predicting reaction conditions, specifically reagents.
  • To improve the accuracy and efficiency of computer-aided synthesis planning.
  • To enhance generative chemistry workflows through AI-driven condition recommendations.

Main Methods:

  • Utilized the Molecular Transformer, a state-of-the-art deep learning model.
  • Trained the model on the United States Patent and Trademark Office (USPTO) dataset.
  • Tested the model's generalization capabilities on the Reaxys database.

Main Results:

  • The Molecular Transformer demonstrated strong out-of-distribution generalization capabilities.
  • The developed reagent prediction model improved the quality of predicted reaction products.
  • The model enabled product prediction models to outperform existing benchmarks.

Conclusions:

  • AI-driven reagent prediction is a viable approach to enhance automated synthesis planning.
  • This work addresses a critical gap in cheminformatics, paving the way for more intelligent generative chemistry.
  • The developed model offers a significant advancement in reaction product prediction accuracy.