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.

本研究提出一种 RetroKNN 方法， 该方法是一种基于机器学习的本地反应模板检索方法， 结合了神经网络预测和最近邻检索， 可以用于分子逆合成， 在 USPTO 数据集上取得了较好的结果。