Corpus ID: 221785486

Learning from Protein Structure with Geometric Vector Perceptrons

@article{Jing2020LearningFP,
  title={Learning from Protein Structure with Geometric Vector Perceptrons},
  author={Bowen Jing and Stephan Eismann and Patricia Suriana and Raphael J. L. Townshend and Ron O. Dror},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.01411}
}
  • Bowen Jing, Stephan Eismann, +2 authors Ron O. Dror
  • Published 2020
  • Computer Science, Biology, Mathematics
  • ArXiv
  • 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… CONTINUE READING

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    References

    SHOWING 1-10 OF 34 REFERENCES
    Generative Models for Graph-Based Protein Design
    • 37
    • Highly Influential
    • PDF
    ProDCoNN: Protein design using a convolutional neural network
    • 3
    End-to-End Learning on 3D Protein Structure for Interface Prediction
    • 14
    • PDF
    Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
    • 48
    Computational Protein Design with Deep Learning Neural Networks
    • 52
    • PDF
    ProteinGCN: Protein model quality assessment using Graph Convolutional Networks
    • 9
    • Highly Influential
    • PDF
    Enhanced Deep‐Learning Prediction of Molecular Properties via Augmentation of Bond Topology
    • 10
    Protein sequence design with a learned potential
    • 11
    • PDF
    GraphQA: Protein Model Quality Assessment using Graph Convolutional Networks.
    • 7