Retrosynthesis planning poses a formidable challenge in the organic chemical
industry, particularly in pharmaceuticals. Single-step retrosynthesis
prediction, a crucial step in the planning process, has witnessed a surge in
interest in recent years due to advancements in AI for science. Various deep
learning-based methods have been proposed for this task in recent years,
incorporating diverse levels of additional chemical knowledge dependency. This
paper introduces UAlign, a template-free graph-to-sequence pipeline for
retrosynthesis prediction. By combining graph neural networks and Transformers,
our method can more effectively leverage the inherent graph structure of
molecules. Based on the fact that the majority of molecule structures remain
unchanged during a chemical reaction, we propose a simple yet effective SMILES
alignment technique to facilitate the reuse of unchanged structures for
reactant generation. Extensive experiments show that our method substantially
outperforms state-of-the-art template-free and semi-template-based approaches.
Importantly, Our template-free method achieves effectiveness comparable to, or
even surpasses, established powerful template-based methods. Scientific
contribution: We present a novel graph-to-sequence template-free retrosynthesis
prediction pipeline that overcomes the limitations of Transformer-based methods
in molecular representation learning and insufficient utilization of chemical
information. We propose an unsupervised learning mechanism for establishing
product-atom correspondence with reactant SMILES tokens, achieving even better
results than supervised SMILES alignment methods. Extensive experiments
demonstrate that UAlign significantly outperforms state-of-the-art
template-free methods and rivals or surpasses template-based approaches, with
up to 5\% (top-5) and 5.4\% (top-10) increased accuracy over the strongest
baseline.

本文介绍了 UAlign，一种无模板的图序列化逆合成预测方法，通过结合图神经网络和 Transformer，更有效地利用了分子的内在图结构。基于化学反应过程中大部分分子结构保持不变的事实，我们提出了一种简单而有效的 SMILES 对齐技术，以促进未改变结构在反应物生成中的重复利用。大量实验表明，我们的方法明显优于最先进的无模板和半模板方法。重要的是，我们的无模板方法在效果上可与甚至超过已建立的强大的模板方法相媲美。

UAlign：无模板反合成预测的极限推动与无监督 SMILES 对齐

UAlign: Pushing the Limit of Template-free Retrosynthesis Prediction  with Unsupervised SMILES Alignment

Retrosynthesis planning is a fundamental challenge in chemistry which aims at
designing reaction pathways from commercially available starting materials to a
target molecule. Each step in multi-step retrosynthesis planning requires
accurate prediction of possible precursor molecules given the target molecule
and confidence estimates to guide heuristic search algorithms. We model
single-step retrosynthesis planning as a distribution learning problem in a
discrete state space. First, we introduce the Markov Bridge Model, a generative
framework aimed to approximate the dependency between two intractable discrete
distributions accessible via a finite sample of coupled data points. Our
framework is based on the concept of a Markov bridge, a Markov process pinned
at its endpoints. Unlike diffusion-based methods, our Markov Bridge Model does
not need a tractable noise distribution as a sampling proxy and directly
operates on the input product molecules as samples from the intractable prior
distribution. We then address the retrosynthesis planning problem with our
novel framework and introduce RetroBridge, a template-free retrosynthesis
modeling approach that achieves state-of-the-art results on standard evaluation
benchmarks.

化学中的逆合成规划是一项基本挑战，旨在从商业可获取的起始物质设计反应路径以达到目标分子。本文介绍了马尔可夫桥模型和 RetroBridge 这一无模板逆合成建模方法，它们在标准评估基准上取得了最先进的结果。