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
this https URL .

该研究介绍了 ATOM3D 的程序，该程序为生物分子的三维计算方法提供了统一的工具包，其中包含多个基准数据集以及多个三维分子学习任务的实现，结果表明多数分子问题都可从三维分子学习中获益，并提供全面套件的工具进行数据集处理、模型培训和评估。