ATOM3D: Tasks On Molecules in Three DimensionsComputational methods that operate on three-dimensional molecular structure
have the potential to solve important questions in biology and chemistry. In
particular, deep neural networks have gained significant attention, but their
widespread adoption in the biomolecular domain has been limited by a lack of
either systematic performance benchmarks or a unified toolkit for interacting
with molecular data. To address this, we present ATOM3D, a collection of both
novel and existing benchmark datasets spanning several key classes of
biomolecules. We implement several classes of three-dimensional molecular
learning methods for each of these tasks and show that they consistently
improve performance relative to methods based on one- and two-dimensional
representations. The specific choice of architecture proves to be critical for
performance, with three-dimensional convolutional networks excelling at tasks
involving complex geometries, graph networks performing well on systems
requiring detailed positional information, and the more recently developed
equivariant networks showing significant promise. Our results indicate that
many molecular problems stand to gain from three-dimensional molecular
learning, and that there is potential for improvement on many tasks which
remain underexplored. To lower the barrier to entry and facilitate further
developments in the field, we also provide a comprehensive suite of tools for
dataset processing, model training, and evaluation in our open-source atom3d
Python package. All datasets are available for download from
https://www.atom3d.ai .
arxiv.org