提出新型球面变换器，高效建模分子3D结构
Equivariant Spherical Transformer for Efficient Molecular Modeling
- 将Transformer架构引入群表示的傅里叶空间，提升表达能力
- 在OC20和QM9数据集上达到顶尖性能，小模型胜过大模型
- 保持等变性，适合需要高精度分子建模的研究者
等变图神经网络（GNN）通过利用群表示显著推动了三维分子结构建模的发展。然而，其消息传递机制依赖于克莱布什-戈登张量积卷积，因非线性受限且群表示阶数较低，导致表达能力不足。为此，我们提出等变球面变换器（EST），一种新颖的即插即用框架，将类似Transformer的架构应用于群表示的傅里叶空间。EST在保持关键等变归纳偏置的同时，通过球面傅里叶变换的统一采样策略实现更高表达能力。实验表明，在OC20和QM9等挑战性基准上，基于EST的模型达到当前最优性能。对于OC20中的复杂分子系统，采用EST的小模型可超越部分更大模型及使用额外数据的模型。此外，我们从理论与实验两方面验证了EST的等变性，为该领域研究开辟新路径。
原文摘要 · Abstract (English)
Equivariant Graph Neural Networks (GNNs) have significantly advanced the modeling of 3D molecular structure by leveraging group representations. However, their message passing, heavily relying on Clebsch-Gordan tensor product convolutions, suffers from restricted expressiveness due to the limited non-linearity and low degree of group representations. To overcome this, we introduce the Equivariant Spherical Transformer (EST), a novel plug-and-play framework that applies a Transformer-like architecture to the Fourier spatial domain of group representations. EST achieves higher expressiveness than conventional models while preserving the crucial equivariant inductive bias through a uniform sampling strategy of spherical Fourier transforms. As demonstrated by our experiments on challenging benchmarks like OC20 and QM9, EST-based models achieve state-of-the-art performance. For the complex molecular systems within OC20, small models empowered by EST can outperform some larger models and those using additional data. In addition to demonstrating such strong expressiveness,we provide both theoretical and experimental validation of EST's equivariance as well, paving the way for new research in this area.
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