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。

使用本地模板检索的逆合成预测

Retrosynthesis Prediction with Local Template Retrieval

Retrosynthetic planning problem is to analyze a complex molecule and give a
synthetic route using simple building blocks. The huge number of chemical
reactions leads to a combinatorial explosion of possibilities, and even the
experienced chemists could not select the most promising transformations. The
current approaches rely on human-defined or machine-trained score functions
which have limited chemical knowledge or use expensive estimation methods such
as rollout to guide the search. In this paper, we propose {\tt MCTS}, a novel
MCTS-based retrosynthetic planning approach, to deal with retrosynthetic
planning problem. Instead of exploiting rollout, we build an Experience
Guidance Network to learn knowledge from synthetic experiences during the
search. Experiments on benchmark USPTO datasets show that, our {\tt MCTS} gains
significant improvement over state-of-the-art approaches both in efficiency and
effectiveness.

本文提出了一种名为 MCTS 的基于模拟退火算法的反合成规划方法，使用 Experience Guidance Network 从化学合成经验中学习知识，实现有效处理拥有巨大可能性的化学反应，实验结果证明在效率和功效上均优于现有研究成果。

经验引导的蒙特卡罗树搜索在反合成规划中的应用

Retrosynthetic Planning with Experience-Guided Monte Carlo Tree Search

Retrosynthetic planning is a critical task in organic chemistry which
identifies a series of reactions that can lead to the synthesis of a target
product. The vast number of possible chemical transformations makes the size of
the search space very big, and retrosynthetic planning is challenging even for
experienced chemists. However, existing methods either require expensive return
estimation by rollout with high variance, or optimize for search speed rather
than the quality. In this paper, we propose Retro*, a neural-based A*-like
algorithm that finds high-quality synthetic routes efficiently. It maintains
the search as an AND-OR tree, and learns a neural search bias with off-policy
data. Then guided by this neural network, it performs best-first search
efficiently during new planning episodes. Experiments on benchmark USPTO
datasets show that, our proposed method outperforms existing state-of-the-art
with respect to both the success rate and solution quality, while being more
efficient at the same time.

本文提出了一种基于神经网络的 A * 算法 Retro * 来高效地进行合成路径规划，在美国专利和商标局数据集上实验表明，我们的方法不仅成功率和解决方案质量均优于现有技术，而且效率更高。