GNN-SKAN：利用SwallowKAN的力量推进基于GNN的分子表示学习
GNN-SKAN: Harnessing the Power of SwallowKAN to Advance Molecular Representation Learning with GNNs
AI总结：
针对GNN在分子表示学习中的注释不足、分子多样性和过度挤压问题，提出集成KAN及自适应RBF的SwallowKAN变体，构建GNN-SKAN和GNN-SKAN+，在多个数据集上取得最先进性能。
摘要：
有效的分子表示学习对于推进分子性质预测和药物设计至关重要。主流的分子表示学习方法基于图神经网络（GNNs）。然而，这些方法面临三个重大挑战：注释不足、分子多样性以及诸如过度挤压（over-squashing）等架构限制，这些限制导致关键结构细节的丢失。为应对这些挑战，我们引入了一类新的GNN，其集成了以强大数据拟合能力和在小规模AI+科学任务中高精度著称的Kolmogorov-Arnold网络（KANs）。通过将KANs融入GNNs，我们的模型增强了分子结构的表示。我们进一步通过一种名为SwallowKAN（SKAN）的变体推进了该方法，该变体采用自适应径向基函数（RBFs）作为非线性神经元的核心。这一创新提高了计算效率和对多样化分子结构的适应性。基于SKAN的优势，我们提出了一类新的GNN，即GNN-SKAN，及其增强变体GNN-SKAN+，后者结合了基于SKAN的分类器以进一步提升性能。据我们所知，这是首个将KANs集成到面向分子表示学习的GNN架构中的工作。在6个分类数据集、6个回归数据集和4个小样本学习数据集上的实验表明，我们的方法在准确性和计算成本方面达到了新的最先进性能。
英文摘要：
Effective molecular representation learning is crucial for advancing molecular property prediction and drug design. Mainstream molecular representation learning approaches are based on Graph Neural Networks (GNNs). However, these approaches struggle with three significant challenges: insufficient annotations, molecular diversity, and architectural limitations such as over-squashing, which leads to the loss of critical structural details. To address these challenges, we introduce a new class of GNNs that integrates the Kolmogorov-Arnold Networks (KANs), known for their robust data-fitting capabilities and high accuracy in small-scale AI + Science tasks. By incorporating KANs into GNNs, our model enhances the representation of molecular structures. We further advance this approach with a variant called SwallowKAN (SKAN), which employs adaptive Radial Basis Functions (RBFs) as the core of the non-linear neurons. This innovation improves both computational efficiency and adaptability to diverse molecular structures. Building on the strengths of SKAN, we propose a new class of GNNs, GNN-SKAN, and its augmented variant, GNN-SKAN+, which incorporates a SKAN-based classifier to further boost performance. To our knowledge, this is the first work to integrate KANs into GNN architectures tailored for molecular representation learning. Experiments across 6 classification datasets, 6 regression datasets, and 4 few-shot learning datasets demonstrate that our approach achieves new state-of-the-art performance in terms of accuracy and computational cost.
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