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Traditional methods like Graph Convolutional Networks (GCNs) face challengeswith limited data and class imbalance, leading to suboptimal performance ingraph 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 separatelyon Graphs for precisely capturing the structural data of molecules and theirtoxicological properties. Additionally, we incorporate Few-Shot Learning toimprove the model's generalization with limited annotated samples. Extensiveexperiments on a diverse toxicology dataset demonstrate that our methodachieves an impressive state-of-art AUC-ROC value of 0.816, surpassing thebaseline GCN model by 11.4%. This highlights the significance of our proposedmethodology and Few Shot Learning in advancing Toxic Molecular Classification,with the potential to enhance drug discovery and environmental risk assessmentprocesses.

Benchmarking Toxic Molecule Classification using Graph Neural Networks and Few Shot Learning