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

极性(国际关系) 自动化 计算机科学 人工智能 标准化 薄层色谱法 吞吐量 机器学习 化学 色谱法 生物系统 工程类 操作系统 生物 机械工程 电信 无线 细胞 生物化学
作者
Hao Xu,Jinglong Lin,Qianyi Liu,Yuntian Chen,Jianning Zhang,Yang Yang,Michael C. Young,Yan Xu,Dongxiao Zhang,Fanyang Mo
出处
期刊:Chem [Elsevier BV]
卷期号:8 (12): 3202-3214 被引量:9
标识
DOI:10.1016/j.chempr.2022.08.008
摘要

•An automated platform is invented to conduct high-throughput TLC analysis •4,944 standardized Rf values from 387 compounds under 17 solvent conditions •A machine-learning model facilitates Rf prediction and chromatographic separation •Higher topological polar surface area (TPSA) contributes to smaller Rf values 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 (Rf). The trained ML models are able to predict the Rf 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. 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 (Rf). The trained ML models are able to predict the Rf 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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
jayjiao完成签到,获得积分10
1秒前
1秒前
搜集达人应助留白采纳,获得10
1秒前
斯文莺发布了新的文献求助10
1秒前
whl完成签到 ,获得积分10
2秒前
余俊兰发布了新的文献求助10
2秒前
3秒前
少夫人完成签到,获得积分10
4秒前
zwj003完成签到,获得积分0
5秒前
英俊的铭应助Yun采纳,获得10
6秒前
6秒前
lalaland完成签到,获得积分10
6秒前
7秒前
7秒前
鹿立轩完成签到,获得积分10
9秒前
10秒前
Shawn完成签到,获得积分10
10秒前
小蘑菇应助哈哈哈采纳,获得10
10秒前
11秒前
12秒前
李爱国应助恋雅颖月采纳,获得10
12秒前
13秒前
留白发布了新的文献求助10
13秒前
fdkufghkd完成签到,获得积分10
16秒前
17秒前
17秒前
懵懂的幻桃完成签到 ,获得积分10
17秒前
flyfish完成签到,获得积分10
18秒前
18秒前
上官若男应助斯文莺采纳,获得30
19秒前
20秒前
20秒前
21秒前
Yun发布了新的文献求助10
21秒前
21秒前
21秒前
kyra发布了新的文献求助10
22秒前
xiaoze发布了新的文献求助10
22秒前
23秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988786
求助须知:如何正确求助?哪些是违规求助? 3531116
关于积分的说明 11252493
捐赠科研通 3269766
什么是DOI,文献DOI怎么找? 1804771
邀请新用户注册赠送积分活动 881870
科研通“疑难数据库(出版商)”最低求助积分说明 809021