数据科学驱动的最佳条件的发现和初级硫胺的Chan-Lam合的条件选择模型
Shivaani S Gandhi1,2, Giselle Z Brown2, Santeri Aikonen3
1Department of Chemistry, Princeton University, Princeton, New Jersey, 08544, United States.
ACS catalysis
|November 3, 2025
概括
这项研究引入了一种数据驱动的方法,以优化用于制药合成的初级硫胺的兰合. 通过将机器学习与高通量选相结合,研究人员在N-arylation反应中实现了广泛的应用性和高选择性.
科学领域:
- 有机化学 有机化学
- 药用化学 医学化学
- 数据科学数据科学数据科学
背景情况:
- 由于其有利的特性,二级N-烯硫胺胺在药品中至关重要.
- 主要硫胺的直接N-arylation是一种关键的合成策略,但在过渡金属催化交叉合反应 (如Chan-Lam (CL) 合) 中面临挑战.
- CL合通常是基质依赖的,容易过度合,限制其一般使用.
研究的目的:
- 为了解决Chan-Lam合的局限性,用于初级硫胺的N-arylation.
- 使用数据科学和高通量实验,开发广泛适用的和高度选择性的反应条件.
- 为优化硫胺N-arylation创建一个预测模型.
主要方法:
- 采用无监督学习来系统地选择各种初级硫胺,以尽量减少训练数据中的偏差.
- 利用高吞吐量实验来生成一个大数据集,包括 3,904 个反应.
- 开发了一种回归模型来预测反应结果并确定硫胺的关键特征.
主要成果:
- 确定了对亚利法和 (异芳) 芳原胺的CL合的广泛适用和高度选择性条件.
- 成功地将复杂的有机合体伙伴与初级硫胺结合在一起.
- 创建了一个回归模型,能够识别基于硫胺结构的高收益条件并预测反应成功.
结论:
- 数据驱动的工作流显著提高了兰N-arylation对硫胺的范围和可预测性.
- 这种方法为合成有价值的制药支架提供了一个强大的工具.
- 开发的模型有助于理解硫胺联反应中的结构-活性关系.
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