Retrosynthesis, the strategy of devising laboratory pathways by working backwards from the target compound, is crucial yet challenging. Enhancing retrosynthetic efficiency requires overcoming the vast complexity of chemical space, the limited known interconversions between molecules, and the challenges posed by limited experimental datasets. This study introduces generative machine learning methods for retrosynthetic planning. The approach features three innovations: generating reaction templates instead of reactants or synthons to create novel chemical transformations, allowing user selection of specific bonds to change for human-influenced synthesis, and employing a conditional kernel-elastic autoencoder (CKAE) to measure the similarity between generated and known reactions for chemical viability insights. These features form a coherent retrosynthetic framework, validated experimentally by designing a 3-step synthetic pathway for a challenging small molecule, demonstrating a significant improvement over previous 5-9 step approaches. This work highlights the utility and robustness of generative machine learning in addressing complex challenges in chemical synthesis.

逆合成是一种通过从目标化合物逆向工作来设计实验室途径的策略，至关重要但具有挑战性。提高逆合成效率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实验数据集带来的挑战。本研究介绍了用于逆向综合规划的生成机器学习方法。该方法具有三项创新：生成反应模板而不是反应物或合成子来创建新颖的化学转化，允许用户选择特定的键来改变人类影响的合成，以及采用条件核弹性自动编码器（CKAE）来测量之间的相似性生成和已知的反应以获取化学可行性见解。这些特征形成了一个连贯的逆合成框架，通过为具有挑战性的小分子设计 3 步合成途径进行实验验证，证明比之前的 5-9 步方法有显着改进。这项工作强调了生成机器学习在解决化学合成中的复杂挑战方面的实用性和稳健性。