Computational methods that operate directly on three-dimensional molecular structure hold large potential to solve important questions in biology and chemistry. In particular deep neural networks have recently gained significant attention. In this work we present ATOM3D, a collection of both novel and existing datasets spanning several key classes of biomolecules, to systematically assess such learning methods. We develop three-dimensional molecular learning networks for each of these tasks, finding that they consistently improve performance relative to one- and two-dimensional methods. The specific choice of architecture proves to be critical for performance, with three-dimensional convolutional networks excelling at tasks involving complex geometries, while graph networks perform well on systems requiring detailed positional information. Furthermore, equivariant networks show significant promise. Our results indicate many molecular problems stand to gain from three-dimensional molecular learning. All code and datasets can be accessed via https://www.atom3d.ai .
Speakers: Martin Voegele