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説明:

This study integrates automated synthesis with machine learning to enable data-driven chromatographic method development. A diverse amide library was synthesized and analyzed on the DCpak PBT column, generating a curated retention-time dataset for supercritical fluid chromatography. Fragment count descriptors such as ChyLine and CircuS outperformed traditional fingerprints, offering higher accuracy and interpretable structure–retention relationships. Visualization methods provided mechanistic insight into key molecular features. Overall, the workflow demonstrates a scalable strategy for producing high-quality data and predictive models, highlighting how automation and chemoinformatics can accelerate column characterization and support autonomous analytical workflows.

権利情報:

• cc-by-3.0
  

キーワード: Automated synthesis, Fragment descriptor, Machine learning, Retention time prediction, Supercritical fluid chromatography

刊行年月日: 2025-11-26

出版者: Royal Society of Chemistry (RSC)

掲載誌:

• Digital Discovery (ISSN: 2635098X)

研究助成金:

• Exploratory Research for Advanced Technology JPMJER1903
• Ministry of Education, Culture, Sports, Science and Technology
• Japan Society for the Promotion of Science JP23H03810
• Japan Society for the Promotion of Science JP23H03807
• Japan Society for the Promotion of Science JP23H03806

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1039/d5dd00437c

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更新時刻: 2025-12-24 15:05:55 +0900

MDRでの公開時刻: 2025-12-24 16:20:29 +0900