Retrosynthesis Prediction with Local Template Retrieval
This work addresses retrosynthesis prediction for drug discovery, representing an incremental improvement over existing template-based methods.
The paper tackled retrosynthesis prediction for drug discovery by introducing RetroKNN, a local template retrieval method combined with neural networks, achieving improvements of 7.1% in top-1 accuracy on the USPTO-50K dataset and 12.0% on the USPTO-MIT dataset.
Retrosynthesis, which predicts the reactants of a given target molecule, is an essential task for drug discovery. In recent years, the machine learing based retrosynthesis methods have achieved promising results. In this work, we introduce RetroKNN, a local reaction template retrieval method to further boost the performance of template-based systems with non-parametric retrieval. We first build an atom-template store and a bond-template store that contain the local templates in the training data, then retrieve from these templates with a k-nearest-neighbor (KNN) search during inference. The retrieved templates are combined with neural network predictions as the final output. Furthermore, we propose a lightweight adapter to adjust the weights when combing neural network and KNN predictions conditioned on the hidden representation and the retrieved templates. We conduct comprehensive experiments on two widely used benchmarks, the USPTO-50K and USPTO-MIT. Especially for the top-1 accuracy, we improved 7.1% on the USPTO-50K dataset and 12.0% on the USPTO-MIT dataset. These results demonstrate the effectiveness of our method.