Automated Synthesis and Fragment Descriptor-Based Machine Learning for Retention Time Prediction in Supercritical Fluid Chromatography

Sitanan Sartyoungkul; Balasubramaniyan Sakthivel; Yuuya Nagata; Pavel Sidorov

doi:10.1039/d5dd00437c

January 1, 2025Digital DiscoveryOpen Access

Automated Synthesis and Fragment Descriptor-Based Machine Learning for Retention Time Prediction in Supercritical Fluid Chromatography

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Key Points

Machine learning enhances retention time prediction accuracy in supercritical fluid chromatography, with significant implications for analytical methods.
Retention time prediction methods leverage automated synthesis, streamlining the chromatographic column selection process.
Analysis integrates machine learning techniques to optimize method development, aiming for increased efficiency in analytical chemistry applications.
Automated synthesis in chromatography may lead to significant advancements in data-driven method development, highlighting the need for further exploration.

Implication

Integration of automated synthesis and machine learning improves retention time predictions in chromatography, suggesting more efficient method development.

Abstract

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...

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Cite This Study

Sartyoungkul et al. (2025) studied this question.

synapsesocial.com/papers/692b94261d383f2b2a378486 https://doi.org/10.1039/d5dd00437c

View Full Paper

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Automated Synthesis and Fragment Descriptor-Based Machine Learning for Retention Time Prediction in Supercritical Fluid Chromatography

Key Points

Implication

Abstract

Discussion

Cite This Study

Automated Synthesis and Fragment Descriptor-Based Machine Learning for Retention Time Prediction in Supercritical Fluid Chromatography

Key Points

Implication

Abstract

Discussion

Cite This Study

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