Hybrid Machine Learning Approach to Predict the Site Selectivity of Iridium-Catalyzed Arene Borylation,Journal of the American Chemical Society

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Hybrid Machine Learning Approach to Predict the Site Selectivity of Iridium-Catalyzed Arene Borylation
Journal of the American Chemical Society ( IF 14.4 ) Pub Date : 2023-07-31 , DOI: 10.1021/jacs.3c04986
Eike Caldeweyher ₁ , Masha Elkin ₂ , Golsa Gheibi ₂ , Magnus Johansson _{3,

4} , Christian Sköld ₅ , Per-Ola Norrby ₁ , John F Hartwig ₂

Affiliation

The borylation of aryl and heteroaryl C–H bonds is valuable for the site-selective functionalization of C–H bonds in complex molecules. Iridium catalysts ligated by bipyridine ligands catalyze the borylation of the C–H bond that is most acidic and least sterically hindered in an arene, but predicting the site of borylation in molecules containing multiple arenes is difficult. To address this challenge, we report a hybrid computational model that predicts the Site of Borylation (SoBo) in complex molecules. The SoBo model combines density functional theory, semiempirical quantum mechanics, cheminformatics, linear regression, and machine learning to predict site selectivity and to extrapolate these predictions to new chemical space. Experimental validation of SoBo showed that the model predicts the major site of borylation of pharmaceutical intermediates with higher accuracy than prior machine-learning models or human experts, demonstrating that SoBo will be useful to guide experiments for the borylation of specific C(sp²)–H bonds during pharmaceutical development.

中文翻译：

混合机器学习方法预测铱催化芳烃硼化的位点选择性

芳基和杂芳基 C-H 键的硼基化对于复杂分子中 C-H 键的位点选择性官能化很有价值。由联吡啶配体连接的铱催化剂可催化芳烃中酸性最强且空间位阻最小的 C-H 键的硼基化，但预测含有多个芳烃的分子中的硼基化位点很困难。为了应对这一挑战，我们报告了一种混合计算模型，可以预测复杂分子中的硼化位点（SoBo）。SoBo 模型结合了密度泛函理论、半经验量子力学、化学信息学、线性回归和机器学习来预测位点选择性并将这些预测外推到新的化学空间。^{SoBo 的实验验证表明，该模型比之前的机器学习模型或人类专家更准确地预测药物中间体硼化的主要位点，这表明 SoBo 将有助于指导特定 C(sp 2} )–硼化的实验。药物开发过程中的 H 键。

更新日期：2023-07-31

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