3D-GSRD: 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding

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.

Authors

• Chang Wu Xidian University
• Zhiyuan Liu Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications, Chongqing, China Key Laboratory of Cyberspace Big Data Intelligent Security, Ministry of Education
• Wen Shu
• Liang Wang
• Yanchen Luo
• Wenqiang Lei Sichuan University
• Yatao Bian Tencent AI Lab
• Junfeng Fang
• Xiang Wang

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