Iterative Supervised Principal Component Analysis Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis.
Iterative Supervised Principal Component Analysis Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis.
复制标题
DOI：
10.1021/acscatal.0c03939
复制
发表时间：
2020-11-20
期刊：
影响因子：
12.9
通讯作者：
Tonks IA
中科院分区：
文献类型：
--
作者：
See XY;Wen X;Wheeler TA;Klein CK;Goodpaster JD;Reiner BR;Tonks IA
在更广泛的化学界中，催化剂的合理设计仍然是一项具有挑战性的奋进，因为有无数的变量可以影响关键的成键事件。为任何反应设计选择性催化剂都需要一种有效的策略来发现预测性的结构-活性关系。在这里，我们描述了使用迭代监督主成分分析（ISPCA）从头催化剂设计。通过Ti催化的苯基丙炔和偶氮苯的[2 + 2 + 1]环加成反应，区域选择性合成了2，5-二甲基-1，3，4-三苯基-1H-吡咯（C）。初始反应条件导致所有可能的吡咯区域异构体的非选择性混合物。ISPCA是在催化剂的训练集上进行的，并且它们的性能针对来自前三个主成分的分数进行回归。来自该PCA空间的组分负载和k均值聚类用于通知新测试催化剂的设计。一个前瞻性的测试集的选择性预测在硅片上使用ISPCA模型，最佳的候选人进行合成和实验测试。迭代该数据驱动的预测建模工作流程，并且仅在三代之后，通过引入2，6_二甲基-4-（吡咯烷-1-基）吡啶作为配体，催化选择性从0.5（产物的统计混合物）提高到超过11（> 90%C）。催化剂选择性的起源进行了探讨，通过检查ISPCA可变负载结合DFT建模，揭示了配体的不稳定性起着重要的作用的选择性。一个并行的催化剂搜索使用多元线性回归（MLR），一种流行的方法在催化信息学，也进行了比较，这些策略在一个假设的催化剂侦察活动。ISPCA似乎是更强大的和预测比MLR时，稀疏的训练集使用的是在早期搜索的最佳催化剂的可用数据的代表。成功开发的高选择性催化剂，而不诉诸长时间，随机筛选过程中表现出的内在力量的ISPCA从头催化剂设计，并应激励ISPCA在反应开发中的普遍使用。
The rational design of catalysts remains a challenging endeavor within the broader chemical community owing to the myriad variables that can affect key bond-forming events. Designing selective catalysts for any reaction requires an efficient strategy for discovering predictive structure–activity relationships. Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H-pyrrole (C) via a Ti-catalyzed formal [2 + 2 +1] cycloaddition of phenylpropyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space and k-means clustering were used to inform the design of new test catalysts. The selectivity of a prospective test set was predicted in silico using the ISPCA model, and optimal candidates were synthesized and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (>90% C) by incorporating 2,6-dimethyl-4-(pyrrolidin-1-yl)pyridine as a ligand. The origin of catalyst selectivity was probed by examining ISPCA variable loadings in combination with DFT modeling, revealing that ligand lability plays an important role in selectivity. A parallel catalyst search using multivariate linear regression (MLR), a popular approach in catalysis informatics, was also conducted in order to compare these strategies in a hypothetical catalyst scouting campaign. ISPCA appears to be more robust and predictive than MLR when sparse training sets are used that are representative of the data available during the early search for an optimal catalyst. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development.
登录
查看更多内容
影响因子：
15
作者：
Davis-Gilbert ZW;Yao LJ;Tonks IA
通讯作者：
Tonks IA
在化学空间中丢失？支持有机金属催化的地图。
影响因子：
--
作者：
Fey N
通讯作者：
Fey N
影响因子：
37.8
作者：
Burai Patrascu, Mihai;Pottel, Joshua;Moitessier, Nicolas
通讯作者：
Moitessier, Nicolas
影响因子：
5.4
作者：
Bailly C
通讯作者：
Bailly C
在[2 + 2 + 1]吡咯合成中从炔烃和偶氮苯中进行二催化的氧化氮转移的机理。
影响因子：
15
作者：
Davis-Gilbert ZW;Wen X;Goodpaster JD;Tonks IA
通讯作者：
Tonks IA