  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.
