Data to 14 Oct 2026

Learning from Protein Structure with Geometric Vector Perceptrons

arXiv 2009.01411 ↗First posted 3 Sep 2020q-bio.BM cs.LG stat.MLcited by 313

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Cited by 30 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (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 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.

Key facts. Cited by 313 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Sep 2020.

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Citing papers per year: 2021: 7; 2022: 37; 2023: 78; 2024: 89; 2025: 61; 2026: 41.

313 papers.

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2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generationcited by 516
2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 441
2022Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphscited by 264
2022Torsional Diffusion for Molecular Conformer Generationcited by 263
2021Geometric and Physical Quantities Improve E(3) Equivariant Message Passingcited by 228
2023EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representationscited by 204
2023Geometric Latent Diffusion Models for 3D Molecule Generationcited by 186
2022Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pocketscited by 183
2022Protein Representation Learning by Geometric Structure Pretrainingcited by 162
2022ProGen2: Exploring the Boundaries of Protein Language Modelscited by 159
2022EquiBind: Geometric Deep Learning for Drug Binding Structure Predictioncited by 144
2023Equivariant flow matchingcited by 135

38 more

2023EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoningcited by 97
2023On the Expressive Power of Geometric Graph Neural Networkscited by 90
2022PiFold: Toward effective and efficient protein inverse foldingcited by 83
2023ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Textscited by 78
2023Structure-informed Language Models Are Protein Designerscited by 68
2023A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systemscited by 68
2023A Text-guided Protein Design Frameworkcited by 64
2024DPLM-2: A Multimodal Diffusion Protein Language Modelcited by 64
2023Equivariant Flow Matching with Hybrid Probability Transportcited by 60
2022DiffBP: Generative Diffusion of 3D Molecules for Target Protein Bindingcited by 58
2022Geometrically Equivariant Graph Neural Networks: A Surveycited by 56
2022Learning Hierarchical Protein Representations via Complete 3D Graph Networkscited by 55
2023Clifford Group Equivariant Neural Networkscited by 55
2023Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Cloudscited by 54
2021Equivariant Graph Neural Networks for 3D Macromolecular Structurecited by 53
2023Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generationcited by 50
2023A new perspective on building efficient and expressive 3D equivariant graph neural networkscited by 47
2024Transferable Boltzmann Generatorscited by 46
2023Graph Denoising Diffusion for Inverse Protein Foldingcited by 44
2022AlphaDesign: A graph protein design method and benchmark on AlphaFoldDBcited by 40
20233D molecule generation by denoising voxel gridscited by 40
2022Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrastcited by 37
2023Efficient Approximations of Complete Interatomic Potentials for Crystal Property Predictioncited by 37
2024Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Productscited by 36
2022State-specific protein-ligand complex structure prediction with a multi-scale deep generative modelcited by 35
2022E3Bind: An End-to-End Equivariant Network for Protein-Ligand Dockingcited by 35
2024Full-Atom Peptide Design based on Multi-modal Flow Matchingcited by 34
20223DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Designcited by 33
2022Equivariant Point Cloud Analysis via Learning Orientations for Message Passingcited by 32
2022Protein Sequence and Structure Co-Design with Equivariant Translationcited by 31
2022Contrastive Representation Learning for 3D Protein Structurescited by 30
2023Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaborationcited by 30
2024Antigen-Specific Antibody Design via Direct Energy-based Preference Optimizationcited by 30
2024A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generationcited by 29
2024A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applicationscited by 28
2023Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generationcited by 27
2023Interactive Humanoid: Online Full-Body Motion Reaction Synthesis with Social Affordance Canonicalization and Forecastingcited by 27
2023Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learningcited by 26

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