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 4 out of 8 tasks in the ATOM3D benchmark and broadly improves over rotation-invariant graph neural networks . We also demonstrate that transfer learning can improve performance in learning from macromolecular structure.
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 URL
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4 out of 8
three out of eight
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and broadly improves over rotation-invariant graph neural networks . We also
, is tied for first on two others, and is competitive with equivariant networks using higher-order representations and spherical harmonic convolutions. In addition, we
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improve performance in learning from macromolecular structure.
further improve performance on certain downstream tasks. Code is available at URL