Molecule-Edit Templates for Efficient and Accurate Retrosynthesis
  Prediction
Mikołaj Sacha Michał Sadowski Piotr Kozakowski Ruard van Workum Stanisław Jastrzębski
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.