Original title: Benchmarking Toxic Molecule Classification using Graph Neural Networks and Few Shot Learning
Authors: Bhavya Mehta, Kush Kothari, Reshmika Nambiar, Seema Shrawne
In the world of molecule toxicity prediction, standard methods like Graph Convolutional Networks (GCNs) often stumble due to limited data and unequal class representation. To conquer these hurdles, researchers explore Graph Isomorphic Networks, Multi Headed Attention, and Free Large-scale Adversarial Augmentation—each tailored to capture molecule structures and toxicity. They integrate Few-Shot Learning to boost the model’s adaptability with scant annotated data.
Original article: https://arxiv.org/abs/2311.13490