Deconvoluting Nonlinear Catalyst-Substrate Effects in the Intramolecular Dirhodium-Catalyzed C-H Insertion of Donor/Donor Carbenes Using Data Science Tools.
Deconvoluting Nonlinear Catalyst-Substrate Effects in the Intramolecular Dirhodium-Catalyzed C-H Insertion of Donor/Donor Carbenes Using Data Science Tools.
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利用数据科学工具解析分子内铑催化的供体/供体卡宾的C - H插入反应中的非线性催化剂 - 底物效应
DOI：
10.1021/acscatal.3c04256
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发表时间：
2024-01-05
期刊：
影响因子：
12.9
通讯作者：
Shaw, Jared T.
中科院分区：
文献类型：
--
作者：
Souza, Lucas W.;Miller, Beck R.;Cammarota, Ryan C.;Lo, Anna;Lopez, Ixchel;Shiue, Yuan-Shin;Bergstrom, Benjamin D.;Dishman, Sarah N.;Fettinger, James C.;Sigman, Matthew S.;Shaw, Jared T.
催化剂和底物之间的相互作用可能是高度复杂和动态的，通常使预测或理解这些过程的模型的开发复杂化。以二铑（II）催化的供体/供体卡宾C-H插入2-烷氧基二苯甲酮底物中形成苯并二氢呋喃为模型体系，探索非线性方法来实现对机理的理解.我们发现，应用传统的多元线性回归（MLR）方法，将DFT衍生的催化剂和底物的描述符关联起来，会导致模型性能不佳。这启发了通过应用确定独立筛选和稀疏化算子（SISSO）算法将非线性描述符关系引入建模。基于SISSO生成的描述符，确定了一个高性能的MLR模型，可以很好地预测外部验证点。机械解释的辅助功能关系，使用化学空间图，决策树和线性描述符的解构。底物被发现有很强的依赖于空间效应，以确定其先天环化选择性偏好。然后可以将催化剂反应性位点特征与产物特征匹配，以在底物规定的范围内调节或超越所得的非对映选择性。本案例研究提出了一种方法，用于理解复杂的相互作用，经常遇到的催化，使用非线性建模方法和线性去卷积模式识别。
Interactions between catalysts and substrates can be highly complex and dynamic, often complicating the development of models to either predict or understand such processes. A dirhodium(II)-catalyzed C–H insertion of donor/donor carbenes into 2-alkoxybenzophenone substrates to form benzodihydrofurans was selected as a model system to explore nonlinear methods to achieve a mechanistic understanding. We found that the application of traditional methods of multivariate linear regression (MLR) correlating DFT-derived descriptors of catalysts and substrates leads to poorly performing models. This inspired the introduction of nonlinear descriptor relationships into modeling by applying the sure independence screening and sparsifying operator (SISSO) algorithm. Based on SISSO-generated descriptors, a high-performing MLR model was identified that predicts external validation points well. Mechanistic interpretation was aided by the deconstruction of feature relationships using chemical space maps, decision trees, and linear descriptors. Substrates were found to have a strong dependence on steric effects for determining their innate cyclization selectivity preferences. Catalyst reactive site features can then be matched to product features to tune or override the resultant diastereoselectivity within the substrate-dictated ranges. This case study presents a method for understanding complex interactions often encountered in catalysis by using nonlinear modeling methods and linear deconvolution by pattern recognition.
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影响因子：
3.4
作者：
Farina, Vittorio
通讯作者：
Farina, Vittorio
影响因子：
15
作者：
DeAngelis, Andrew;Dmitrenko, Olga;Fox, Joseph M.
通讯作者：
Fox, Joseph M.
影响因子：
64.8
作者：
Reid, Jolene P.;Sigman, Matthew S.
通讯作者：
Sigman, Matthew S.
DOI：
10.1038/s41570-019-0099-x
发表时间：
2019-06
期刊：
Nature reviews. Chemistry
影响因子：
--
作者：
Davies HML;Liao K
通讯作者：
Liao K
影响因子：
4.3
作者：
Lamb, Kellan N.;Squitieri, Richard A.;Shaw, Jared T.
通讯作者：
Shaw, Jared T.