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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2510.16780 |
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| _version_ | 1866909864031158272 |
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| author | Wu, Chang Liu, Zhiyuan Shu, Wen Wang, Liang Luo, Yanchen Lei, Wenqiang Bian, Yatao Fang, Junfeng Wang, Xiang |
| author_facet | Wu, Chang Liu, Zhiyuan Shu, Wen Wang, Liang Luo, Yanchen Lei, Wenqiang Bian, Yatao Fang, Junfeng Wang, Xiang |
| contents | Masked graph modeling (MGM) is a promising approach for molecular representation learning (MRL).However, extending the success of re-mask decoding from 2D to 3D MGM is non-trivial, primarily due to two conflicting challenges: avoiding 2D structure leakage to the decoder, while still providing sufficient 2D context for reconstructing re-masked atoms. To address these challenges, we propose 3D-GSRD: a 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding. The core innovation of 3D-GSRD lies in its Selective Re-mask Decoding(SRD), which re-masks only 3D-relevant information from encoder representations while preserving the 2D graph structures. This SRD is synergistically integrated with a 3D Relational-Transformer(3D-ReTrans) encoder alongside a structure-independent decoder. We analyze that SRD, combined with the structure-independent decoder, enhances the encoder's role in MRL. Extensive experiments show that 3D-GSRD achieves strong downstream performance, setting a new state-of-the-art on 7 out of 8 targets in the widely used MD17 molecular property prediction benchmark. The code is released at https://github.com/WuChang0124/3D-GSRD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_16780 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 3D-GSRD: 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding Wu, Chang Liu, Zhiyuan Shu, Wen Wang, Liang Luo, Yanchen Lei, Wenqiang Bian, Yatao Fang, Junfeng Wang, Xiang Machine Learning Masked graph modeling (MGM) is a promising approach for molecular representation learning (MRL).However, extending the success of re-mask decoding from 2D to 3D MGM is non-trivial, primarily due to two conflicting challenges: avoiding 2D structure leakage to the decoder, while still providing sufficient 2D context for reconstructing re-masked atoms. To address these challenges, we propose 3D-GSRD: a 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding. The core innovation of 3D-GSRD lies in its Selective Re-mask Decoding(SRD), which re-masks only 3D-relevant information from encoder representations while preserving the 2D graph structures. This SRD is synergistically integrated with a 3D Relational-Transformer(3D-ReTrans) encoder alongside a structure-independent decoder. We analyze that SRD, combined with the structure-independent decoder, enhances the encoder's role in MRL. Extensive experiments show that 3D-GSRD achieves strong downstream performance, setting a new state-of-the-art on 7 out of 8 targets in the widely used MD17 molecular property prediction benchmark. The code is released at https://github.com/WuChang0124/3D-GSRD. |
| title | 3D-GSRD: 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.16780 |