Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression

Haitz Sáez de Ocáriz Borde, Federico Barbero

Keywords: Meta-Learning, Molecular Property Prediction, Graph Neural Networks

Abstract: We demonstrate the applicability of model-agnostic algorithms for meta-learning, specifically Reptile, to GNN models in molecular regression tasks. Using meta-learning we are able to learn new chemical prediction tasks with only a few model updates, as compared to using randomly initialized GNNs which require learning each regression task from scratch. We experimentally show that GNN layer expressivity is correlated to improved meta-learning. Additionally, we also experiment with GNN emsembles which yield best performance and rapid convergence for k-shot learning.

Type Of Submission: Extended abstract (max 4 main pages).

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Type Of Submission: Extended abstract.