Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property Prediction

Understand

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.