Learning on 3D structures of large biomolecules is emerging as a distinct
area in machine learning, but there has yet to emerge a unifying network
architecture that simultaneously leverages the graph-structured and geometric
aspects of the problem domain. To address this gap, we introduce geometric
vector perceptrons, which extend standard dense layers to operate on
collections of Euclidean vectors. Graph neural networks equipped with such
layers are able to perform both geometric and relational reasoning on efficient
and natural representations of macromolecular structure. We demonstrate our
approach on two important problems in learning from protein structure: model
quality assessment and computational protein design. Our approach improves over
existing classes of architectures, including state-of-the-art graph-based and
voxel-based methods. We release our code at this https URL

通过引入基于几何向量感知器的图神经网络，在大型生物分子的 3D 结构上进行学习并利用其图结构和几何方面，从而在蛋白结构的质量评估和计算蛋白质设计两个重要问题上实现了优化，改进了现有各类体系结构，其中包括最先进的基于图的和基于体素的方法。