{"ID":2853117,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.16780","arxiv_id":"2510.16780","title":"3D-GSRD: 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2510.16780","url_pdf":"https://arxiv.org/pdf/2510.16780v2","authors":"[\"Chang Wu\",\"Zhiyuan Liu\",\"Wen Shu\",\"Liang Wang\",\"Yanchen Luo\",\"Wenqiang Lei\",\"Yatao Bian\",\"Junfeng Fang\",\"Xiang Wang\"]","published":"2025-10-19T10:12:29Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false,"code_links":[{"ID":608059,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2853117,"paper_url":"https://arxiv.org/abs/2510.16780","paper_title":"3D-GSRD: 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding","repo_url":"https://github.com/WuChang0124/3D-GSRD","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}