Impact of Phosphine Featurization Methods in Process Development
Impact of Phosphine Featurization Methods in Process Development
复制标题
磷化氢特征化方法对工艺开发的影响
DOI：
10.1021/acs.oprd.1c00357
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发表时间：
2022
影响因子：
3.4
通讯作者：
Steves, Janelle E.
中科院分区：
文献类型：
--
作者：
Crawford, Jennifer M.;Gensch, Tobias;Sigman, Matthew S.;Elward, Jennifer M.;Steves, Janelle E.
现代高通量实验（HTE）使人们能够快速探索大范围的化学反应空间，从而加速制药工艺中关键合成步骤的开发。然而，反应参数的维度、使用最少起始材料的期望以及彻底分析反应结果的需要仍然需要明智地选择以何种顺序进行哪些实验。因此，开发量化试剂多样性和全面分析HTE反应结果的能力至关重要。解决该目标的方法将结合关键反应组分的联合收割机计算特征化与使用多元线性回归建模来关联反应性能输出。在这方面，我们描述了一个过程，建立一个计算功能化平台的单齿膦配体和考虑其实施GSK。我们表明，计算方法的选择有影响的膦描述符值，配体的选择实验，反应结果的线性回归模型的发展。
Modern high-throughput experimentation (HTE) has enabled the rapid exploration of large expanses of chemical reaction space to accelerate the development of key synthetic steps in pharmaceutical processes. However, the dimensionality of reaction parameters, the desire to use minimal starting material, and the need to thoroughly analyze reaction outcomes still require the judicious selection of which experiments to perform in which order. Therefore, the development of a capability to quantify reagent diversity and analyze reaction outcomes in HTE holistically is paramount. A method to address this goal would combine computational featurization of key reaction components with the use of multivariate linear regression modeling to correlate the reaction performance outputs. In this context, we describe a process of establishing a computational featurization platform for monodentate phosphine ligands and considerations for its implementation at GSK. We demonstrate that the choice of computational method has an impact on phosphine descriptor values, ligand selection for experiments, and the development of linear regression models of reaction outcomes.
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影响因子：
5.9
作者：
Christensen, Melodie;Yunker, Lars P. E.;Adedeji, Folarin;Hase, Florian;Roch, Loic M.;Gensch, Tobias;dos Passos Gomes, Gabriel;Zepel, Tara;Sigman, Matthew S.;Aspuru-Guzik, Alan;Hein, Jason E.
通讯作者：
Hein, Jason E.
DOI：
10.1126/science.aat2299
发表时间：
2018-11-09
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Zhao S;Gensch T;Murray B;Niemeyer ZL;Sigman MS;Biscoe MR
通讯作者：
Biscoe MR
影响因子：
15
作者：
Barder, Timothy E.;Buchwald, Stephen L.
通讯作者：
Buchwald, Stephen L.
影响因子：
15
作者：
T. Gensch;G. dosPassosGomes;Pascal Friederich;E. Peters;T. Gaudin;R. Pollice;K. Jorner;AkshatKumar Nigam;M. LindnerD'Addario;M. Sigman;Alán Aspuru
通讯作者：
Alán Aspuru
影响因子：
2.8
作者：
Falivene, Laura;Credendino, Raffaele;Cavallo, Luigi
通讯作者：
Cavallo, Luigi