# Automated synthesis and fragment descriptor-based machine learning for retention time prediction in supercritical fluid chromatography

https://mdr.nims.go.jp/datasets/ed143ff5-a867-415f-8b9a-3351fcf6a62b

## File

- [D5DD00437C.pdf](https://mdr.nims.go.jp/filesets/b12564d8-1f99-4c10-aaf7-3d76170307dd/download) ([Detail](https://mdr.nims.go.jp/filesets/b12564d8-1f99-4c10-aaf7-3d76170307dd.md))

## Id

ed143ff5-a867-415f-8b9a-3351fcf6a62b

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-12-24T05:02:55.994499Z

## Updated at

2025-12-24T06:05:55.106228Z

## Published at

2025-12-24T07:20:29.922094Z

## Doi



## First published url

https://doi.org/10.1039/d5dd00437c

## Date published

2025-11-26

## Recorded date published



## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Automated synthesis and fragment descriptor-based machine learning for retention
    time prediction in supercritical fluid chromatography
  title_type: original
  lang: en

## Description

- description: 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.
  description_type: abstract
  lang: und

## Creator

- name: Sitanan Sartyoungkul
  role: author
- name: Balasubramaniyan Sakthivel
  role: author
- name: Pavel Sidorov
  role: author
  orcid: https://orcid.org/0000-0001-6462-702X
- name: Yuuya Nagata
  role: author
  orcid: https://orcid.org/0000-0001-5926-5845

## Contact agent



## Publisher

organization: Royal Society of Chemistry (RSC)

## Managing organization



## Keyword

- subject: Automated synthesis
  schema: not_defined
- subject: Fragment descriptor
  schema: not_defined
- subject: Machine learning
  schema: not_defined
- subject: Retention time prediction
  schema: not_defined
- subject: Supercritical fluid chromatography
  schema: not_defined

## Rights

- identifier: cc-by-3.0

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## Embargo



## Journal

- title: Digital Discovery
  issn: 2635098X

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## Funding

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

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## Fileset

- id: b12564d8-1f99-4c10-aaf7-3d76170307dd
  filename: D5DD00437C.pdf
  content_type: application/pdf
  size: 2019725
  md5: 7a9f08a7dd55a8b767c019cdfa5efe94

## Thumbnail

fileset_id: b12564d8-1f99-4c10-aaf7-3d76170307dd
filename: D5DD00437C.pdf