  Few-shot learning is a promising approach to molecular property prediction as
supervised data is often very limited. However, many important molecular
properties depend on complex molecular characteristics -- such as the various
3D geometries a molecule may adopt or the types of chemical interactions it can
form -- that are not explicitly encoded in the feature space and must be
approximated from low amounts of data. Learning these characteristics can be
difficult, especially for few-shot learning algorithms that are designed for
fast adaptation to new tasks. In this work, we develop molecular embeddings
that encode complex molecular characteristics to improve the performance of
few-shot molecular property prediction. Our approach leverages large amounts of
synthetic data, namely the results of molecular docking calculations, and a
multi-task learning paradigm to structure the embedding space. On multiple
molecular property prediction benchmarks, training from the embedding space
substantially improves Multi-Task, MAML, and Prototypical Network few-shot
learning performance. Our code is available at
https://github.com/cfifty/IGNITE.
