High-throughput discovery of chemical structure-polarity relationships combining automation and machine-learning techniques

Xu, Hao1,2,3; Lin, Jinglong1; Liu, Qianyi1; Chen, Yuntian4; Zhang, Jianning1; Yang, Yang5; Young, Michael C.6; Xu, Yan7; Zhang, Dongxiao8,9; Mo, Fanyang1

2022-12-08

10.1016/j.chempr.2022.08.008

Chem   影响因子和分区

2451-9294

As an essential attribute of organic compounds, polarity has a profound influence on many molecular properties. Thin-layer chromatography ( TLC) represents a commonly used technique for empirical polarity estimations. Current TLC techniques need repetitive attempts to obtain suitable development conditions and have low reproducibility due to a low degree of standardization. Herein, we describe an automated system to conduct TLC analysis automatically, facilitating high-throughput collection of a large quantity of experimental data under standardized conditions. Using this dataset, machine-learning (ML) methods are employed to construct surrogate models correlating organic compound structures and their polarity reflected by retardation factor (R-f). The trained ML models are able to predict the R-f value curve of organic compounds in different solvent combinations with high accuracy, thus providing general guidelines for the selection of purification conditions and expediting the generation and analysis of quality TLC data.

SCI

英语

通讯

National Natural Science Foundation of China["22071004","21933001","22150013"]

Chemistry

Chemistry, Multidisciplinary

WOS:000919705600001

CELL PRESS

Web of Science

1.Peking Univ, Sch Mat Sci & Engn, Beijing 100871, Peoples R China
2.Peking Univ, Coll Engn, BIC ESAT, ERE, Beijing 100871, Peoples R China
3.Peking Univ, Coll Engn, SKLTCS, Beijing 100871, Peoples R China
4.Yongriver Inst Technol, Eastern Inst Adv Study, Ningbo 315200, Peoples R China
5.Univ Calif Santa Barbara, Dept Chem & Biochem, Santa Barbara, CA 93106 USA
6.Univ Toledo, Sch Green Chem & Engn, Dept Chem & Biochem, 2801 W Bancroft St, Toledo, OH 43606 USA
7.WuXi AppTec Co Ltd, Chem Serv Unit, Shanghai 200131, Peoples R China
8.Peng Cheng Lab, Dept Math & Theories, Shenzhen 518000, Peoples R China
9.Southern Univ Sci & Technol, Natl Ctr Appl Math Shenzhen NCAMS, Shenzhen 518000, Peoples R China

Xu, Hao,Lin, Jinglong,Liu, Qianyi,et al. High-throughput discovery of chemical structure-polarity relationships combining automation and machine-learning techniques[J]. Chem,2022,8(12).

Xu, Hao.,Lin, Jinglong.,Liu, Qianyi.,Chen, Yuntian.,Zhang, Jianning.,...&Mo, Fanyang.(2022).High-throughput discovery of chemical structure-polarity relationships combining automation and machine-learning techniques.Chem,8(12).

Xu, Hao,et al."High-throughput discovery of chemical structure-polarity relationships combining automation and machine-learning techniques".Chem 8.12(2022).