Retrosynthesis Prediction with Local Template Retrieval
Shufang Xie Rui Yan Junliang Guo Yingce Xia Lijun Wu Tao Qin
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.