Equivariant Graph Neural Networks for 3D Macromolecular Structure
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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.
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