Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1126/science.aau5631 |
Prediction of higher-selectivity catalysts by computer-driven worlflow and machine learning | |
Zahrt, Andrew F.; Henle, Jeremy J.; Rose, Brennan T.; Wang, Yang; Darrow, William T.; Denmarkt, Scott E. | |
2019-01-18 | |
发表期刊 | SCIENCE
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ISSN | 0036-8075 |
EISSN | 1095-9203 |
出版年 | 2019 |
卷号 | 363期号:6424页码:247-+ |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Catalyst design in asymmetric reaction development has traditionally been driven by empiricism, wherein experimentalists attempt to qualitatively recognize structural patterns to improve selectivity. Machine learning algorithms and chemoinformatics can potentially accelerate this process by recognizing otherwise inscrutable patterns in large datasets. Herein we report a computationally guided workflow for chiral catalyst selection using chemoinformatics at every stage of development. Robust molecular descriptors that are agnostic to the catalyst scaffold allow for selection of a universal training set on the basis of steric and electronic properties. This set can be used to train machine learning methods to make highly accurate predictive models over a broad range of selectivity space. Using support vector machines and deep feed-forward neural networks, we demonstrate accurate predictive modeling in the chiral phosphoric acid-catalyzed thiol addition to N-acylimines. |
领域 | 地球科学 ; 气候变化 ; 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000456140700027 |
WOS关键词 | QUATERNARY AMMONIUM-IONS ; DIELS-ALDER REACTION ; NEURAL-NETWORKS ; DESIGN ; BINOL ; ACIDS ; ENANTIOSELECTIVITY ; PERFORMANCE ; COMPLEXES ; LIGANDS |
WOS类目 | Multidisciplinary Sciences |
WOS研究方向 | Science & Technology - Other Topics |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/200557 |
专题 | 地球科学 资源环境科学 气候变化 |
作者单位 | Univ Illinois, Dept Chem, Roger Adams Lab, Urbana, IL 61801 USA |
推荐引用方式 GB/T 7714 | Zahrt, Andrew F.,Henle, Jeremy J.,Rose, Brennan T.,et al. Prediction of higher-selectivity catalysts by computer-driven worlflow and machine learning[J]. SCIENCE,2019,363(6424):247-+. |
APA | Zahrt, Andrew F.,Henle, Jeremy J.,Rose, Brennan T.,Wang, Yang,Darrow, William T.,&Denmarkt, Scott E..(2019).Prediction of higher-selectivity catalysts by computer-driven worlflow and machine learning.SCIENCE,363(6424),247-+. |
MLA | Zahrt, Andrew F.,et al."Prediction of higher-selectivity catalysts by computer-driven worlflow and machine learning".SCIENCE 363.6424(2019):247-+. |
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