Learning from Protein Structure with Geometric Vector Perceptrons

GVP architecture

Abstract

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.

Publication
In Internation Conference on Learning Representations 2021
Raphaël John Lamarre Townshend
Raphaël John Lamarre Townshend
PhD Fellow in Computer Science

Bringing machine learning to an atomic world.