Royal Society of Chemistry
The integration of automated synthesis and machine learning (ML) is transforming analytical chemistry by enabling data-driven approaches to method development. Chromatographic column selection, a critical yet time-consuming step in separation science, stands to benefit substantially from such advances. Here, we report a workflow that combines automated synthesis of a structurally diverse amide library with fragment descriptor-based ML for retention time prediction in supercritical fluid chromatography (SFC). Retention data were systematically acquired on the recently developed DCpak® PBT column, providing one of the first structured datasets for this stationary phase. Benchmarking revealed that fragment-count descriptors (ChyLine and CircuS) substantially outperformed conventional molecular fingerprints, delivering higher predictive accuracy and more interpretable relationships between substructures and retention behavior. External validation underscored the role of chemical space coverage, while visualization techniques such as ColorAtom analysis offered mechanistic insight into model decisions. By uniting automated synthesis with chemoinformatics-driven ML, this study demonstrates a scalable approach to generating high-quality training data and predictive models for chromatography. Beyond retention prediction, the framework exemplifies how data-centric strategies can accelerate column characterization, reduce reliance on trial-and-error experimentation, and advance the development of autonomous, high-throughput analytical workflows.
For details:
Automated synthesis and fragment descriptor-based machine learning for retention time prediction in supercritical fluid chromatography
Sitanan Sartyoungkul ab, Balasubramaniyan Sakthivel a, Pavel Sidorov a, Yuuya Nagata abc
a) Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, Hokkaido 001-0021, Japan
b) JST, ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Sapporo, Hokkaido 060-0810, Japan
c) Autonomous Polymer Design and Discovery Group, Research Center for Macromolecules and Biomaterials, National Institute for Materials Science (NIMS), Tsukuba, Ibaraki 305-0047, Japan
Royal Society of Chemistry
https://pubs.rsc.org/en/content/articlelanding/2026/dd/d5dd00437c
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