Molecule property prediction with molecular orbitals

Molecular orbitals describe the distribution of electrons in a molecule and are frequently used by chemists to understand properties of molecules, yet deep learning has largely neglected them for property prediction so far. In many setups where atom coordinates are computed, they can be obtained cheaply and quickly – a useful source of complementary information, particularly when data is scarce. We give an introduction to molecular orbitals for a machine learning audience and propose models to process three different representations of them. Experiments on a dataset with experimental properties show that including molecular orbitals significantly improves performance and sample efficiency over a pretrained molecular foundation model in real-world settings.