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Finding synthesis routes for molecules of interest is an essential step inthe discovery of new drugs and materials. To find such routes,computer-assisted synthesis planning (CASP) methods are employed which rely ona model of chemical reactivity. In this study, we model single-stepretrosynthesis in a template-based approach using modern Hopfield networks(MHNs). We adapt MHNs to associate different modalities, reaction templates andmolecules, which allows the model to leverage structural information aboutreaction templates. This approach significantly improves the performance oftemplate relevance prediction, especially for templates with few or zerotraining examples. With inference speed several times faster than that ofbaseline methods, we improve predictive performance for top-k exact matchaccuracy for $\mathrm{k}\geq5$ in the retrosynthesis benchmark USPTO-50k.

Modern Hopfield Networks for Few- and Zero-Shot Reaction Template Prediction