  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.
