3D分子图自编码器通过选择性重掩码提升性质预测性能
3D-GSRD: 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding
- 设计选择性重掩码机制，仅重掩3D信息保留2D结构
- 在MD17基准上7个任务达到新最优，8个任务中表现最佳
- 适合关注3D分子表征与生成的研究者
掩码图建模（MGM）是分子表征学习的有前景方法。然而，将重掩码解码从2D推广到3D面临双重挑战：避免2D结构信息泄露给解码器，同时保留足够2D上下文以重建被掩码原子。为此，我们提出3D-GSRD：一种具有选择性重掩码解码（SRD）的3D分子图自编码器。SRD仅从编码器表示中重掩3D相关信息，同时保留2D图结构。该机制与3D关系变换器（3D-ReTrans）编码器及结构无关解码器协同工作。实验表明，3D-GSRD在广泛使用的MD17分子性质预测基准上，8个目标中有7个达到新状态，显著提升性能。代码已开源。
原文摘要 · Abstract (English)
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.
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