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Developing a machine learning model for accurate nucleoside hydrogels prediction based on descriptors

Author

Listed:
  • Weiqi Li

    (Sichuan University)

  • Yinghui Wen

    (Sichuan University)

  • Kaichao Wang

    (Sichuan University)

  • Zihan Ding

    (Sichuan University)

  • Lingfeng Wang

    (Sichuan University)

  • Qianming Chen

    (Sichuan University)

  • Liang Xie

    (Sichuan University)

  • Hao Xu

    (Sichuan University)

  • Hang Zhao

    (Sichuan University)

Abstract

Supramolecular hydrogels derived from nucleosides have been gaining significant attention in the biomedical field due to their unique properties and excellent biocompatibility. However, a major challenge in this field is that there is no model for predicting whether nucleoside derivative will form a hydrogel. Here, we successfully develop a machine learning model to predict the hydrogel-forming ability of nucleoside derivatives. The optimal model with a 71% (95% Confidence Interval, 0.69−0.73) accuracy is established based on a dataset of 71 reported nucleoside derivatives. 24 molecules are selected via the optimal model external application and the hydrogel-forming ability is experimentally verified. Among these, two rarely reported cation-independent nucleoside hydrogels are found. Based on their self-assemble mechanisms, the cation-independent hydrogel is found to have potential applications in rapid visual detection of Ag+ and cysteine. Here, we show the machine learning model may provide a tool to predict nucleoside derivatives with hydrogel-forming ability.

Suggested Citation

  • Weiqi Li & Yinghui Wen & Kaichao Wang & Zihan Ding & Lingfeng Wang & Qianming Chen & Liang Xie & Hao Xu & Hang Zhao, 2024. "Developing a machine learning model for accurate nucleoside hydrogels prediction based on descriptors," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46866-9
    DOI: 10.1038/s41467-024-46866-9
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    References listed on IDEAS

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    1. Nathan Ratledge & Gabe Cadamuro & Brandon Cuesta & Matthieu Stigler & Marshall Burke, 2022. "Using machine learning to assess the livelihood impact of electricity access," Nature, Nature, vol. 611(7936), pages 491-495, November.
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