GeoScatt-GNN：一种基于几何散射变换的图神经网络模型用于Ames致突变性预测
GeoScatt-GNN: A Geometric Scattering Transform-Based Graph Neural Network Model for Ames Mutagenicity Prediction
AI总结：
本文提出GeoScatt-GNN模型，结合二维散射系数、几何图散射与图神经网络，在ZINC数据集上显著提升Ames致突变性预测精度，为药物发现和化学安全评估提供新方法。
摘要：
本文通过引入三种开创性方法，应对致突变性预测这一紧迫挑战。首先，展示了从分子图像中提取的二维散射系数相比传统分子描述符具有更优越的性能。其次，提出了一种混合方法，将几何图散射（GGS）、图同构网络（GIN）和机器学习模型相结合，在致突变性预测中取得了显著成果。第三，引入了一种新颖的图神经网络架构MOLG3-SAGE，该架构将GGS节点特征集成到全连接图结构中，提供了出色的预测精度。在ZINC数据集上的实验结果表明有显著改进，强调了将二维和几何散射技术与图神经网络相融合的有效性。本研究展示了GNN和GGS在致突变性预测方面的潜力，对药物发现和化学安全性评估具有广泛意义。
英文摘要：
This paper tackles the pressing challenge of mutagenicity prediction by introducing three ground-breaking approaches. First, it showcases the superior performance of 2D scattering coefficients extracted from molecular images, compared to traditional molecular descriptors. Second, it presents a hybrid approach that combines geometric graph scattering (GGS), Graph Isomorphism Networks (GIN), and machine learning models, achieving strong results in mutagenicity prediction. Third, it introduces a novel graph neural network architecture, MOLG3-SAGE, which integrates GGS node features into a fully connected graph structure, delivering outstanding predictive accuracy. Experimental results on the ZINC dataset demonstrate significant improvements, emphasizing the effectiveness of blending 2D and geometric scattering techniques with graph neural networks. This study illustrates the potential of GNNs and GGS for mutagenicity prediction, with broad implications for drug discovery and chemical safety assessment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
