Leveraging Regio- and Stereoselective C(sp<sup>3</sup>)-H Functionalization of Silyl Ethers to Train a Logistic Regression Classification Model for Predicting Site-Selectivity Bias.

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作者： Yannick T,Boni ^[1] ; Ryan C,Cammarota ^[2] ; Kuangbiao,Liao ^[1] ; Matthew S,Sigman ^[2] ; Huw M L,Davies ^[1]

作者单位： Department of Chemistry, Emory University, 1515 Dickey Drive, Atlanta, Georgia 30322, United States. ^[1] Department of Chemistry, University of Utah, 315 South 1400 East, Salt Lake City, Utah 84112, United States. ^[2]

主题词催化作用(Catalysis);醚类(Ethers);Logistic模型(Logistic Models)

DOI 10.1021/jacs.2c04383

PMID 35977100

发布时间 2024-09-29

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Journal of the American Chemical Society

2022年144卷34期

15549-15561页

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Leveraging Regio- and Stereoselective C(sp<sup>3</sup>)-H Functionalization of Silyl Ethers to Train a Logistic Regression Classification Model for Predicting Site-Selectivity Bias.

Journal of the American Chemical Society

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Leveraging Regio- and Stereoselective C(sp<sup>3</sup>)-H Functionalization of Silyl Ethers to Train a Logistic Regression Classification Model for Predicting Site-Selectivity Bias.

Journal of the American Chemical Society

相似文献

Journal of the American Chemical Society

相关期刊

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