Benchmarking Toxic Molecule Classification using Graph Neural Networks
  and Few Shot Learning
Bhavya Mehta Kush Kothari Reshmika Nambiar Seema Shrawne
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.