GDMol: Generative Double‐Masking Self‐Supervised Learning for Molecular Property Prediction
In summary, this research offers novel insights on improving molecular property prediction tasks, and paves the way for further research on the application of generative learning and self-supervised learning in the field of chemistry.
Authors
- Chengcheng Xu (ORCID: https://orcid.org/0000-0001-6332-051X)
- Yadong Chen (ORCID: https://orcid.org/0000-0002-2189-9356)
- Yingxu Liu (ORCID: https://orcid.org/0009-0004-7645-7012)
- Haichun Liu (ORCID: https://orcid.org/0000-0003-0164-3502)
- Qing Fan (ORCID: https://orcid.org/0000-0001-7985-4153)
- Yang Liu (ORCID: https://orcid.org/0000-0003-4729-4395)
- Yu Wang (ORCID: https://orcid.org/0000-0001-6108-5157)
- Yanmin Zhang (ORCID: https://orcid.org/0000-0002-4075-7556)
- Yu Xie (ORCID: https://orcid.org/0000-0002-3852-1536)
- Xiangzhen Ning
Institutions
- China Pharmaceutical University (CN)
Publication Details
- Journal
- Molecular Informatics
- Published
- 2024-10-24
- DOI
- https://doi.org/10.1002/minf.202400146
- Citations
- 4
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 1.45
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Jiangsu Province