Traditional methods like Graph Convolutional Networks (GCNs) face challenges with limited data and class imbalance, leading to suboptimal performance in graph classification tasks during toxicity prediction of molecules as a whole. To address these issues, we harness the power of Graph Isomorphic Networks, Multi Headed Attention and Free Large-scale Adversarial Augmentation separately on Graphs for precisely capturing the structural data of molecules and their toxicological properties. Additionally, we incorporate Few-Shot Learning to improve the model's generalization with limited annotated samples. Extensive experiments on a diverse toxicology dataset demonstrate that our method achieves an impressive state-of-art AUC-ROC value of 0.816, surpassing the baseline GCN model by 11.4%. This highlights the significance of our proposed methodology and Few Shot Learning in advancing Toxic Molecular Classification, with the potential to enhance drug discovery and environmental risk assessment processes.

翻译：传统方法如图卷积网络在处理有限数据和类别不平衡问题时面临挑战，导致在整体分子毒性预测的图分类任务中性能欠佳。为应对这些问题，我们分别在图结构上利用图同构网络、多头注意力机制和自由大规模对抗增强技术，精确捕获分子的结构数据及其毒理学特性。此外，我们引入少样本学习以提升模型在有限标注样本下的泛化能力。在多样化毒理学数据集上的大量实验表明，本方法取得了0.816的当前最优AUC-ROC值，较基线GCN模型提升11.4%。这凸显了所提方法及少样本学习在推进有毒分子分类中的重要意义，其有望增强药物发现与环境风险评估流程。