  Molecular shape and geometry dictate key biophysical recognition processes,
yet many graph neural networks disregard 3D information for molecular property
prediction. Here, we propose a new contrastive-learning procedure for graph
neural networks, Molecular Contrastive Learning from Shape Similarity
(MolCLaSS), that implicitly learns a three-dimensional representation. Rather
than directly encoding or targeting three-dimensional poses, MolCLaSS matches a
similarity objective based on Gaussian overlays to learn a meaningful
representation of molecular shape. We demonstrate how this framework naturally
captures key aspects of three-dimensionality that two-dimensional
representations cannot and provides an inductive framework for scaffold
hopping.
