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Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network

Author

Listed:
  • Hao Xu

    (Peking University
    Peking University)

  • Jinglong Lin

    (Peking University)

  • Dongxiao Zhang

    (Eastern Institute of Technology
    Peng Cheng Laboratory)

  • Fanyang Mo

    (Peking University
    Peking University Shenzhen Graduate School)

Abstract

The enantioseparation of chiral molecules is a crucial and challenging task in the field of experimental chemistry, often requiring extensive trial and error with different experimental settings. To overcome this challenge, here we show a research framework that employs machine learning techniques to predict retention times of enantiomers and facilitate chromatographic enantioseparation. A documentary dataset of chiral molecular retention times in high-performance liquid chromatography (CMRT dataset) is established to handle the challenge of data acquisition. A quantile geometry-enhanced graph neural network is proposed to learn the molecular structure-retention time relationship, which shows a satisfactory predictive ability for enantiomers. The domain knowledge of chromatography is incorporated into the machine learning model to achieve multi-column prediction, which paves the way for chromatographic enantioseparation prediction by calculating the separation probability. The proposed research framework works well in retention time prediction and chromatographic enantioseparation facilitation, which sheds light on the application of machine learning techniques to the experimental scene and improves the efficiency of experimenters to speed up scientific discovery.

Suggested Citation

  • Hao Xu & Jinglong Lin & Dongxiao Zhang & Fanyang Mo, 2023. "Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38853-3
    DOI: 10.1038/s41467-023-38853-3
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    References listed on IDEAS

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    3. Alexandre Tkatchenko, 2020. "Machine learning for chemical discovery," Nature Communications, Nature, vol. 11(1), pages 1-4, December.
    4. Benjamin Burger & Phillip M. Maffettone & Vladimir V. Gusev & Catherine M. Aitchison & Yang Bai & Xiaoyan Wang & Xiaobo Li & Ben M. Alston & Buyi Li & Rob Clowes & Nicola Rankin & Brandon Harris & Rei, 2020. "A mobile robotic chemist," Nature, Nature, vol. 583(7815), pages 237-241, July.
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