Molecule-Edit Templates for Efficient and Accurate Retrosynthesis Prediction

• URL: http://arxiv.org/abs/2310.07313v1
• Date: Wed, 11 Oct 2023 09:00:02 GMT
• Title: Molecule-Edit Templates for Efficient and Accurate Retrosynthesis Prediction
• Authors: Miko{\l}aj Sacha, Micha{\l} Sadowski, Piotr Kozakowski, Ruard van Workum, Stanis{\l}aw Jastrz\k{e}bski
• Abstract summary: We introduce METRO, a machine-learning model that predicts reactions using minimal templates. We achieve state-of-the-art results on standard benchmarks.
• Score: 0.16070833439280313
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrosynthesis involves determining a sequence of reactions to synthesize complex molecules from simpler precursors. As this poses a challenge in organic chemistry, machine learning has offered solutions, particularly for predicting possible reaction substrates for a given target molecule. These solutions mainly fall into template-based and template-free categories. The former is efficient but relies on a vast set of predefined reaction patterns, while the latter, though more flexible, can be computationally intensive and less interpretable. To address these issues, we introduce METRO (Molecule-Edit Templates for RetrOsynthesis), a machine-learning model that predicts reactions using minimal templates - simplified reaction patterns capturing only essential molecular changes - reducing computational overhead and achieving state-of-the-art results on standard benchmarks.

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