Retrosynthesis, the strategy of devising laboratory pathways for small molecules by working backwards from the target compound, remains a rate limiting step in multi-step synthesis of complex molecules, particularly in drug discovery. Enhancing retrosynthetic efficacy requires overcoming the vast complexity of chemical space, the limited known interconversions between molecules, and the challenges posed by limited experimental datasets. In this study, we introduce generative machine learning methods for retrosynthetic planning that generate reaction templates. Our approach features three key innovations. First, the models generate complete reactions, known as templates, instead of reactants or synthons. Through this abstraction, novel chemical transforms resembling those in the training dataset can be generated. Second, the approach optionally allows users to select the specific bond or bonds to be changed in the proposed reaction, enabling human interaction to influence the synthetic approach. Third, one of our models, based on the conditional kernel-elastic autoencoder (CKAE) architecture, employs a latent space to measure the similarity between generated and known reactions, providing insights into their chemical viability. Together, these features establish a coherent framework for retrosynthetic planning, as validated by our experimental work. We demonstrate the application of our machine learning methodology to design a synthetic pathway for a simple yet challenging small molecule of pharmaceutical interest. The pathway was experimentally proven viable through a 3-step process, which compares favorably to previous 5-9 step approaches. This improvement demonstrates the utility and robustness of the generative machine learning approaches described herein and highlights their potential to address a broad spectrum of challenges in chemical synthesis.

中文翻译：

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用于逆向综合规划的特定地点模板生成方法

逆合成是通过从目标化合物向后进行工作来设计小分子实验室途径的策略，仍然是复杂分子多步合成中的限速步骤，特别是在药物发现中。提高逆合成功效需要克服化学空间的巨大复杂性、分子之间已知的有限相互转化以及有限的实验数据集带来的挑战。在这项研究中，我们介绍了用于生成反应模板的逆合成规划的生成机器学习方法。我们的方法具有三个关键创新。首先，模型生成完整的反应，称为模板，而不是反应物或合成子。通过这种抽象，可以生成类似于训练数据集中的化学变换。其次，该方法可选择允许用户选择在提议的反应中要改变的特定键或键，从而使人机交互能够影响合成方法。第三，我们的模型之一基于条件核弹性自动编码器（CKAE）架构，采用潜在空间来测量生成的反应和已知反应之间的相似性，从而深入了解其化学可行性。这些特征共同为逆向综合规划建立了一个连贯的框架，正如我们的实验工作所验证的那样。我们展示了如何应用机器学习方法来设计简单但具有挑战性的药物小分子的合成途径。该途径通过 3 步过程经实验证明是可行的，与之前的 5-9 步方法相比具有优势。这一改进证明了本文所述的生成机器学习方法的实用性和鲁棒性，并强调了它们解决化学合成中广泛挑战的潜力。