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Learning from Protein Structure with Geometric Vector Perceptrons

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arxiv 2009.01411 v3 pith:QV3LCEUS submitted 2020-09-03 q-bio.BM cs.LGstat.ML

Bowen Jing , Stephan Eismann , Patricia Suriana , Raphael J.L. Townshend , Ron Dror This is my paper
classification q-bio.BMcs.LGstat.ML
keywords geometriclearningproteinstructureapproachlayersperceptronsvector
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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 relational reasoning on efficient and natural representations of macromolecular structure. We demonstrate our approach on two important problems in learning from protein structure: model quality assessment and computational protein design. Our approach improves over existing classes of architectures, including state-of-the-art graph-based and voxel-based methods. We release our code at https://github.com/drorlab/gvp.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 195 citations worldwide. Full citation record

  1. Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular Dynamics

    cs.LG 2026-04 conditional novelty 6.5 of 10

    Structure-pretrained diffusion plus an equivariant temporal interpolator generates chemically realistic MD trajectories on small molecules, tetrapeptides, and proteins by separating spatial and temporal learning.

  2. Cross-Chirality Generalization by Axial Vectors for Hetero-Chiral Protein-Peptide Interaction Design

    q-bio.BM 2026-02 conditional novelty 6.0 of 10

    Axial-feature injection into an E(3)-equivariant latent diffusion model enables zero-shot D-peptide binder design, with one CD38 binder (KD ≈ 10 µM) validated in vitro.

  3. Tokenizing Loops of Antibodies

    q-bio.BM 2025-09 conditional novelty 6.0 of 10

    Igloo is a multimodal antibody loop tokenizer that, when plugged into protein language models, modestly improves loop retrieval, affinity prediction, and structure-consistent loop generation.

  4. DualEquiNet: A Dual-Space Hierarchical Equivariant Network for Large Biomolecules

    q-bio.BM 2025-06 conditional novelty 6.0 of 10

    A dual-space, hierarchically pooled equivariant GNN reports lower error than seven geometric baselines on RNA and protein property-prediction benchmarks.

  5. EnerBridge-DPO: Energy-Guided Protein Inverse Folding with Markov Bridges and Direct Preference Optimization

    cs.LG 2025-06 conditional novelty 5.0 of 10

    A Markov-bridge inverse folding model fine-tuned with energy-based preference pairs and an explicit ΔΔG loss designs lower-energy protein complex sequences while keeping sequence recovery close to state-of-the-art.

  6. Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization

    cs.LG 2025-06 reject novelty 4.0 of 10

    A single-author preprint re-frames GNN over-smoothing as Anderson localization, defining a participation-degree metric and proposing degree-dependent edge reweighting as mitigation, without proof or experiments.

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