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

•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.