MSGRL: A Motif-Driven Self-Supervised Graph Representation Learning Framework for Interpretable Molecular Property Prediction
- DOI
- 10.3390/molecules31173008
- Published
- 2026-08-27
- Container
- Molecules
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/molecules31173008,
title = {MSGRL: A Motif-Driven Self-Supervised Graph Representation Learning Framework for Interpretable Molecular Property Prediction},
author = {You Wu and Yuxin Jiang and Xiaoyun Qi and Haitao Fu and Qiyu Tang and Wen Wang and Cheng Zeng and Guosheng Zhu},
year = {2026},
journal = {Molecules},
doi = {10.3390/molecules31173008},
url = {https://doi.org/10.3390/molecules31173008}
}RIS
TY - JOUR TI - MSGRL: A Motif-Driven Self-Supervised Graph Representation Learning Framework for Interpretable Molecular Property Prediction AU - You Wu AU - Yuxin Jiang AU - Xiaoyun Qi AU - Haitao Fu AU - Qiyu Tang AU - Wen Wang AU - Cheng Zeng AU - Guosheng Zhu PY - 2026 JO - Molecules DO - 10.3390/molecules31173008 UR - https://doi.org/10.3390/molecules31173008 ER -
APA
Wu, Y., Jiang, Y., Qi, X., Fu, H., Tang, Q., Wang, W., Zeng, C., & Zhu, G. (2026). MSGRL: A Motif-Driven Self-Supervised Graph Representation Learning Framework for Interpretable Molecular Property Prediction. Molecules. https://doi.org/10.3390/molecules31173008
Source records
- crossref · retrieved 2026-09-25T05:16:14.742Z