mlsanityData to 7 Oct 2026

Convolutional Networks on Graphs for Learning Molecular Fingerprints

arXiv 1509.09292 ↗First posted 30 Sep 2015cs.LG cs.NE stat.MLcited by 751

Not on the map. Why?

Abstract (arXiv)

We introduce a convolutional neural network that operates directly on graphs. These networks allow end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape. The architecture we present generalizes standard molecular feature extraction methods based on circular fingerprints. We show that these data-driven features are more interpretable, and have better predictive performance on a variety of tasks.

Key facts. Cited by 751 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Sep 2015.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 216; 2022: 192; 2023: 138; 2024: 107; 2025: 65; 2026: 32.

751 papers.

Sort:

By area:

  1. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  2. 2021How Attentive are Graph Attention Networks?cited by 818
  3. 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generationcited by 511
  4. 2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 438
  5. 2021Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognitioncited by 317
  6. 2021Combinatorial optimization and reasoning with graph neural networkscited by 267
  7. 2020Permutation Invariant Graph Generation via Score-Based Generative Modelingcited by 263
  8. 2021Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Developmentcited by 258
  9. 2022Structure-based Drug Design with Equivariant Diffusion Modelscited by 213
  10. 2021ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Predictioncited by 204
  11. 2021Large-Scale Chemical Language Representations Capture Molecular Structure and Propertiescited by 203
  12. 2021Adversarial Graph Augmentation to Improve Graph Contrastive Learningcited by 200
38 more
  1. 2022Graph neural networks for materials science and chemistrycited by 181
  2. 2022Translation between Molecules and Natural Languagecited by 173
  3. 2023GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networkscited by 166
  4. 2022Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editingcited by 165
  5. 2021Learning Gradient Fields for Molecular Conformation Generationcited by 162
  6. 2021Deep Parametric Continuous Convolutional Neural Networkscited by 161
  7. 2021From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awarenesscited by 154
  8. 2021Graph Condensation for Graph Neural Networkscited by 150
  9. 2021GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networkscited by 147
  10. 2021GraphiT: Encoding Graph Structure in Transformerscited by 146
  11. 2021Graph Learning: A Surveycited by 140
  12. 2021Nested Graph Neural Networkscited by 136
  13. 2021Scalars are universal: Equivariant machine learning, structured like classical physicscited by 127
  14. 2021Self-supervised Graph-level Representation Learning with Local and Global Structurecited by 125
  15. 2021Similarity Reasoning and Filtration for Image-Text Matchingcited by 121
  16. 2021Weisfeiler and Leman go Machine Learning: The Story so farcited by 121
  17. 2021Accurate Learning of Graph Representations with Graph Multiset Poolingcited by 120
  18. 2021Geometric Deep Learning on Molecular Representationscited by 113
  19. 2021Combinatorial Optimization with Physics-Inspired Graph Neural Networkscited by 107
  20. 2021MagNet: A Neural Network for Directed Graphscited by 104
  21. 2022How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervisioncited by 102
  22. 2022Condensing Graphs via One-Step Gradient Matchingcited by 100
  23. 2022Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvaturecited by 96
  24. 2021Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patternscited by 95
  25. 2021TPCN: Temporal Point Cloud Networks for Motion Forecastingcited by 94
  26. 2022A Generalization of ViT/MLP-Mixer to Graphscited by 93
  27. 2022How Powerful are K-hop Message Passing Graph Neural Networkscited by 90
  28. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  29. 2022Hierarchically Decomposed Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 85
  30. 2023Everything is Connected: Graph Neural Networkscited by 85
  31. 2021Size-Invariant Graph Representations for Graph Classification Extrapolationscited by 82
  32. 2021Graph Representation Learning in Biomedicinecited by 79
  33. 2022Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Predictioncited by 78
  34. 2022Deep Clustering: A Comprehensive Surveycited by 78
  35. 2021SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanismcited by 75
  36. 2022Expander Graph Propagationcited by 75
  37. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  38. 2023GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learningcited by 70

50 most cited of 751 All 751 (JSON)