Representing and reasoning about 3D structures of macromolecules is emerging as a distinct challenge in machine learning. Here, we extend recent work on geometric vector perceptrons and apply equivariant graph neural networks to a wide range of tasks from structural biology. Our method outperforms all reference architectures on three out of eight tasks in the ATOM3D benchmark, is tied for first on two others, and is competitive with equivariant networks using higher-order representations and spherical harmonic convolutions. In addition, we demonstrate that transfer learning can further improve performance on certain downstream tasks. Code is available at https://github.com/drorlab/gvp-pytorch.
@article{arxiv.2106.03843,
title = {Equivariant Graph Neural Networks for 3D Macromolecular Structure},
author = {Bowen Jing and Stephan Eismann and Pratham N. Soni and Ron O. Dror},
journal= {arXiv preprint arXiv:2106.03843},
year = {2021}
}