提出旋转采样编码器，让3D分子图神经网络更稳定、高效。
Rotational Sampling: A Plug-and-Play Encoder for Rotation-Invariant 3D Molecular GNNs
- 通过旋转采样计算SO(3)期望，实现近似旋转不变性
- 在QM9和C10数据集上精度与泛化能力超越现有方法
- 低计算开销且可解释性强，适合药物与材料设计
图神经网络在分子性质预测中表现卓越，但传统图表示难以有效编码分子的三维空间结构，因分子在三维空间中的取向变化导致模型泛化与鲁棒性受限。现有方法多集中于旋转不变或旋转等变处理：不变方法依赖先验知识，泛化能力不足；等变方法计算成本过高。本文提出一种新型即插即用的3D编码模块——旋转采样，通过在SO(3)旋转群上计算期望，自然实现近似旋转不变性。进一步引入精心设计的后对齐策略，可在不损失性能的前提下实现严格不变性。在QM9和C10数据集上的实验表明，该方法在预测精度、鲁棒性和泛化能力方面均优于现有方法。同时保持低计算复杂度与高可解释性，为药物发现与材料设计中高效处理3D分子信息提供了新方向。
原文摘要 · Abstract (English)
Graph neural networks (GNNs) have achieved remarkable success in molecular property prediction. However, traditional graph representations struggle to effectively encode the inherent 3D spatial structures of molecules, as molecular orientations in 3D space introduce significant variability, severely limiting model generalization and robustness. Existing approaches primarily focus on rotation-invariant and rotation-equivariant methods. Invariant methods often rely heavily on prior knowledge and lack sufficient generalizability, while equivariant methods suffer from high computational costs. To address these limitations, this paper proposes a novel plug-and-play 3D encoding module leveraging rotational sampling. By computing the expectation over the SO(3) rotational group, the method naturally achieves approximate rotational invariance. Furthermore, by introducing a carefully designed post-alignment strategy, strict invariance can be achieved without compromising performance. Experimental evaluations on the QM9 and C10 Datasets demonstrate superior predictive accuracy, robustness, and generalization performance compared to existing methods. Moreover, the proposed approach maintains low computational complexity and enhanced interpretability, providing a promising direction for efficient and effective handling of 3D molecular information in drug discovery and material design.
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