Equivariant Graph Neural Networks for 3D Macromolecular Structure

• URL: http://arxiv.org/abs/2106.03843v1
• Date: Mon, 7 Jun 2021 17:57:04 GMT
• Title: Equivariant Graph Neural Networks for 3D Macromolecular Structure
• Authors: Bowen Jing, Stephan Eismann, Pratham N. Soni, Ron O. Dror
• Abstract summary: We extend 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.
• Score: 2.064612766965483
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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