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. 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.

中文翻译：

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Ualign：通过无监督的 SMILES 比对突破无模板逆合成预测的极限


逆合成规划对有机化工行业，特别是制药行业提出了巨大的挑战。单步逆合成预测是规划过程中的关键一步，近年来，由于科学人工智能的进步，人们对单步逆合成预测的兴趣激增。近年来，针对此任务提出了各种基于深度学习的方法，其中结合了不同级别的附加化学知识依赖性。本文介绍了 UAlign，一种用于逆合成预测的无模板图到序列管道。通过结合图神经网络和 Transformer，我们的方法可以更有效地利用分子固有的图结构。基于大多数分子结构在化学反应过程中保持不变的事实，我们提出了一种简单而有效的 SMILES 对齐技术，以促进未变化的结构在反应物生成中的重复利用。大量的实验表明，我们的方法大大优于最先进的无模板和半基于模板的方法。重要的是，我们的无模板方法的有效性可与甚至超越已建立的强大的基于模板的方法相媲美。我们提出了一种新颖的图到序列无模板逆合成预测管道，克服了基于 Transformer 的方法在分子表示学习和化学信息利用不足方面的局限性。我们提出了一种无监督学习机制，用于与反应物 SMILES 令牌建立产品原子对应关系，取得比监督 SMILES 对齐方法更好的结果。 大量实验表明，UAlign 的性能显着优于最先进的无模板方法，并且可以与基于模板的方法相媲美或超越，其准确​​率比最强方法提高了 5%（前 5 名）和 5.4%（前 10 名）基线。