@inproceedings{
sacha2023moleculeedit,
title={Molecule-edit templates for efficient and accurate retrosynthesis prediction},
author={Miko{\l}aj Sacha and Micha{\l} Sadowski and Piotr Kozakowski and Ruard van Workum and Stanislaw Jastrzebski},
booktitle={NeurIPS 2023 AI for Science Workshop},
year={2023},
url={https://openreview.net/forum?id=2H1oU6VD4H}
}
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.
Molecule-edit templates for efficient and accurate retrosynthesis prediction
Mikołaj Sacha Michał Sadowski Piotr Kozakowski Ruard van Workum Stanislaw Kamil Jastrzebski