  We investigate Siamese networks for learning related embeddings for augmented
samples of molecular conformers. We find that a non-contrastive (positive-pair
only) auxiliary task aids in supervised training of Euclidean neural networks
(E3NNs) and increases manifold smoothness (MS) around point-cloud geometries.
We demonstrate this property for multiple drug-activity prediction tasks while
maintaining relevant performance metrics, and propose an extension of MS to
probabilistic and regression settings. We provide an analysis of representation
collapse, finding substantial effects of task-weighting, latent dimension, and
regularization. We expect the presented protocol to aid in the development of
reliable E3NNs from molecular conformers, even for small-data drug discovery
programs.
