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

• URL: http://arxiv.org/abs/2311.13490v1
• Date: Wed, 22 Nov 2023 16:07:32 GMT
• Title: Benchmarking Toxic Molecule Classification using Graph Neural Networks and Few Shot Learning
• Authors: Bhavya Mehta, Kush Kothari, Reshmika Nambiar, Seema Shrawne
• Abstract summary: Traditional Graph Convolutional Networks (GCNs) face challenges with limited data and class imbalance. We harness the power of Graph Isomorphic Networks, Multi Headed Attention and Free Large-scale Adversarial Augmentation separately on Graphs. We incorporate Few-Shot Learning to improve the model's generalization with limited annotated samples.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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