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Factor-wise disentangled contrastive learning for cross-domain few-shot molecular property prediction

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Acknowledgements

This research was sponsored in part by the National Key Research and Development Program of China (No. 2023YFB3307500), the Science and Technology Innovation Project of Hunan Province (No. 2023RC4014), and the National Natural Science Foundation of China (NSFC) (Grant Nos. 62076146, 62021002, U20A6003, 6212780016).

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Authors and Affiliations

  1. Beijing National Research Center for Information Science and Technology, Key Laboratory for Information System Security (Ministry of Education), and School of Software, Tsinghua University, Beijing, 100084, China

    Zhibin Ni, Chenghao Zhang, Hai Wan & Xibin Zhao

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  1. Zhibin Ni
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  2. Chenghao Zhang
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  3. Hai Wan
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  4. Xibin Zhao
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Correspondence to Xibin Zhao.

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Competing interests The authors declare that they have no competing interests or financial conflicts to disclose.

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Ni, Z., Zhang, C., Wan, H. et al. Factor-wise disentangled contrastive learning for cross-domain few-shot molecular property prediction. Front. Comput. Sci. 19, 198916 (2025). https://doi.org/10.1007/s11704-024-40791-2

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  • DOI: https://doi.org/10.1007/s11704-024-40791-2