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 thiswork 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 URL .
Computational 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 URL .
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