标题： 基于虚拟反应数据增强的逆合成预测 显示英文标题

标题： Retrosynthesis prediction enhanced by in-silico reaction data augmentation

摘要： 最近机器学习（ML）的进展通过帮助化学家更高效地设计实验，加快了逆合成研究。然而，所有基于ML的方法都需要大量配对的训练数据（即化学反应：产物-反应物对），而获取这些数据成本很高。此外，公司视反应数据为宝贵资产，并限制研究人员的访问。由于这些数据驱动的性质，这些问题阻碍了更强大逆合成模型的创建。作为回应，我们利用易于获取的非配对数据（即产物-反应物对的一个组成部分）来生成模拟配对数据，以促进模型训练。具体来说，我们提出了RetroWISE，这是一种自增强框架，它使用从真实配对数据中推断出的基础模型，利用非配对数据进行模拟反应生成和增强，最终得到一个更优的模型。在三个基准数据集上，RetroWISE在与最先进模型的对比中表现最佳（例如，在USPTO-50K测试数据集上top-1准确率提高了8.6%）。此外，它持续提高了罕见转化的预测准确性。这些结果表明，Retro-WISE通过模拟反应克服了训练瓶颈，从而为更有效的基于ML的逆合成模型铺平了道路。 显示英文摘要

摘要： Recent advances in machine learning (ML) have expedited retrosynthesis research by assisting chemists to design experiments more efficiently. However, all ML-based methods consume substantial amounts of paired training data (i.e., chemical reaction: product-reactant(s) pair), which is costly to obtain. Moreover, companies view reaction data as a valuable asset and restrict the accessibility to researchers. These issues prevent the creation of more powerful retrosynthesis models due to their data-driven nature. As a response, we exploit easy-to-access unpaired data (i.e., one component of product-reactant(s) pair) for generating in-silico paired data to facilitate model training. Specifically, we present RetroWISE, a self-boosting framework that employs a base model inferred from real paired data to perform in-silico reaction generation and augmentation using unpaired data, ultimately leading to a superior model. On three benchmark datasets, RetroWISE achieves the best overall performance against state-of-the-art models (e.g., +8.6% top-1 accuracy on the USPTO-50K test dataset). Moreover, it consistently improves the prediction accuracy of rare transformations. These results show that Retro- WISE overcomes the training bottleneck by in-silico reactions, thereby paving the way toward more effective ML-based retrosynthesis models.