Data Science-Enabled Palladium-Catalyzed Enantioselective Aryl-Carbonylation of Sulfonimidamides

New methods for the general asymmetric synthesis of sulfonimidamides
are of great interest due to their applications in medicinal chemistry,
agrochemical discovery, and academic research. We report a palladium-catalyzed
cross-coupling method for the enantioselective aryl-carbonylation
of sulfonimidamides. Using data science techniques, a virtual library
of calculated bisphosphine ligand descriptors was used to guide reaction
optimization by effectively sampling the catalyst chemical space.
The optimized conditions identified using this approach provided the
desired product in excellent yield and enantioselectivity. As the
next step, a data science-driven strategy was also used to explore
a diverse set of aryl and heteroaryl iodides, providing key information
about the scope and limitations of the method. Furthermore, we tested
a range of racemic sulfonimidamides for compatibility of this coupling
partner. The developed method offers a general and efficient strategy
for accessing enantioenriched sulfonimidamides, which should facilitate
their application in industrial and academic settings.

针对磺酰亚胺酰胺（sulfonimidamides）的通用不对称合成新方法，因其在药物化学、农药研发及学术研究中的应用价值而备受关注。本文报道一种钯催化的磺酰亚胺酰胺对映选择性芳基羰基化交叉偶联方法。本研究借助数据科学技术，构建经计算得到的双膦配体描述符虚拟库，通过有效遍历催化剂化学空间来指导反应优化。通过该策略筛选得到的最优反应条件，可使目标产物获得优异的收率与对映选择性。后续研究采用数据科学驱动的策略，对一系列多样化的芳基及杂芳基碘代物进行探索，明确了该方法的适用范围与局限性。此外，我们还测试了多种外消旋磺酰亚胺酰胺作为偶联底物的兼容性。本方法为制备对映体富集的磺酰亚胺酰胺提供了通用且高效的途径，将有助于推动其在工业与学术研究场景中的应用。