mlsanityData to 7 Oct 2026

Graph-based Molecular Representation Learning

arXiv 2207.04869 ↗First posted 8 Jul 2022q-bio.QM cs.LGcited by 51

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Abstract (arXiv)

Molecular representation learning (MRL) is a key step to build the connection between machine learning and chemical science. In particular, it encodes molecules as numerical vectors preserving the molecular structures and features, on top of which the downstream tasks (e.g., property prediction) can be performed. Recently, MRL has achieved considerable progress, especially in methods based on deep molecular graph learning. In this survey, we systematically review these graph-based molecular representation techniques, especially the methods incorporating chemical domain knowledge. Specifically, we first introduce the features of 2D and 3D molecular graphs. Then we summarize and categorize MRL methods into three groups based on their input. Furthermore, we discuss some typical chemical applications supported by MRL. To facilitate studies in this fast-developing area, we also list the benchmarks and commonly used datasets in the paper. Finally, we share our thoughts on future research directions.

Key facts. Cited by 51 papers in the map (data through 7 Oct 2026). Builds on 44 of its 70 references that are in the map. First posted on arXiv 8 Jul 2022.

Builds on

44 of 70 references.

References · 44

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2018How Powerful are Graph Neural Networks?cited by 4,355
  5. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  6. 2020Graph Contrastive Learning with Augmentationscited by 1,203
  7. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,037
  8. 2019Strategies for Pre-training Graph Neural Networkscited by 964
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  10. 2018Junction Tree Variational Autoencoder for Molecular Graph Generationcited by 647
  11. 2020SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networkscited by 602
  12. 2020Directional Message Passing for Molecular Graphscited by 556
32 more
  1. 2019InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximizationcited by 441
  2. 2021GemNet: Universal Directional Graph Neural Networks for Moleculescited by 342
  3. 2020GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generationcited by 301
  4. 2017Grammar Variational Autoencodercited by 283
  5. 2021Pre-training Molecular Graph Representation with 3D Geometrycited by 248
  6. 2018Molecular Transformer - A Model for Uncertainty-Calibrated Chemical Reaction Predictioncited by 225
  7. 2020Hierarchical Generation of Molecular Graphs using Structural Motifscited by 204
  8. 2021Spherical Message Passing for 3D Graph Networkscited by 166
  9. 2021Motif-based Graph Self-Supervised Learning for Molecular Property Predictioncited by 159
  10. 20213D Infomax improves GNNs for Molecular Property Predictioncited by 146
  11. 2020Self-supervised Learning on Graphs: Deep Insights and New Directioncited by 108
  12. 2017Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Networkcited by 102
  13. 2018Learning Multimodal Graph-to-Graph Translation for Molecular Optimizationcited by 87
  14. 2021Few-Shot Graph Learning for Molecular Property Predictioncited by 81
  15. 2019GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulationcited by 75
  16. 2021Learning to Extend Molecular Scaffolds with Structural Motifscited by 70
  17. 2022Graph Rationalization with Environment-based Augmentationscited by 70
  18. 2022ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphscited by 69
  19. 2021GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Predictioncited by 61
  20. 2021An End-to-End Framework for Molecular Conformation Generation via Bilevel Programmingcited by 59
  21. 2018Graph Transformation Policy Network for Chemical Reaction Predictioncited by 56
  22. 2021SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinationscited by 51
  23. 2021Molecular Contrastive Learning with Chemical Element Knowledge Graphcited by 50
  24. 2021Property-Aware Relation Networks for Few-Shot Molecular Property Predictioncited by 41
  25. 2019A Model to Search for Synthesizable Moleculescited by 37
  26. 2021Chemical-Reaction-Aware Molecule Representation Learningcited by 36
  27. 2020Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Propertiescited by 30
  28. 2021Directional Message Passing on Molecular Graphs via Synthetic Coordinatescited by 24
  29. 2021Deep Molecular Representation Learning via Fusing Physical and Chemical Informationcited by 21
  30. 2021Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsitycited by 20
  31. 2018Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoderscited by 17
  32. 2021Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discoverycited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 13; 2024: 16; 2025: 13; 2026: 5.

51 papers.

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  1. 2023What can Large Language Models do in chemistry? A comprehensive benchmark on eight taskscited by 179
  2. 2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 79
  3. 2022Empowering Graph Representation Learning with Test-Time Graph Transformationcited by 66
  4. 2023Knowledge Distillation on Graphs: A Surveycited by 38
  5. 2024A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applicationscited by 28
  6. 2023A Survey on Spectral Graph Neural Networkscited by 26
  7. 2023Class-Imbalanced Learning on Graphs: A Surveycited by 24
  8. 2024Graph Condensation: A Surveycited by 23
  9. 2024Uni-Mol2: Exploring Molecular Pretraining Model at Scalecited by 20
  10. 2024Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Surveycited by 16
  11. 2023A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directionscited by 15
  12. 2022Boosting Graph Neural Networks via Adaptive Knowledge Distillationcited by 14
38 more
  1. 2024From Words to Molecules: A Survey of Large Language Models in Chemistrycited by 14
  2. 2024EditRoom: LLM-parameterized Graph Diffusion for Composable 3D Room Layout Editingcited by 14
  3. 2025Flow Matching Meets Biology and Life Science: A Surveycited by 14
  4. 2023GPT-MolBERTa: GPT Molecular Features Language Model for molecular property predictioncited by 13
  5. 2022Predicting Cellular Responses with Variational Causal Inference and Refined Relational Informationcited by 11
  6. 2022Link Prediction with Non-Contrastive Learningcited by 11
  7. 20243D-MolT5: Leveraging Discrete Structural Information for Molecule-Text Modelingcited by 9
  8. 2024RobGC: Towards Robust Graph Condensationcited by 8
  9. 2023SGOOD: Substructure-enhanced Graph-Level Out-of-Distribution Detectioncited by 7
  10. 2024GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insightscited by 7
  11. 2024Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learningcited by 6
  12. 2025Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyondcited by 6
  13. 2023Advancing Drug Discovery with Enhanced Chemical Understanding via Asymmetric Contrastive Multimodal Learningcited by 5
  14. 2024Node Duplication Improves Cold-start Link Predictioncited by 4
  15. 2026Multi-Domain Riemannian Graph Gluing for Building Graph Foundation Modelscited by 4
  16. 2024Multi-Type Point Cloud Autoencoder: A Complete Equivariant Embedding for Molecule Conformation and Posecited by 3
  17. 2025Incomplete Graph Learning: A Comprehensive Surveycited by 3
  18. 2025Towards Interpretable Drug-Drug Interaction Prediction: A Graph-Based Approach with Molecular and Network-Level Explanationscited by 3
  19. 2026Enhancing Molecular Property Predictions by Learning from Bond Modelling and Interactionscited by 3
  20. 2024Diffusion-based Graph Generative Methodscited by 2
  21. 2024UGMAE: A Unified Framework for Graph Masked Autoencoderscited by 2
  22. 2025MV-CLAM: Multi-View Molecular Interpretation with Cross-Modal Projection via Language Modelcited by 2
  23. 2023Target specific peptide design using latent space approximate trajectory collectorcited by 1
  24. 2023Similar Document Template Matching Algorithmcited by 1
  25. 2023MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structurescited by 1
  26. 2024Enhancing Molecular Property Prediction with Auxiliary Learning and Task-Specific Adaptationcited by 1
  27. 2024Towards Graph Prompt Learning: A Survey and Beyondcited by 1
  28. 2025Combining GCN Structural Learning with LLM Chemical Knowledge for Enhanced Virtual Screeningcited by 1
  29. 2025Multi-Modal Molecular Representation Learning via Structure Awarenesscited by 1
  30. 2025ChemHGNN: A Hierarchical Hypergraph Neural Network for Reaction Virtual Screening and Discoverycited by 1
  31. 2026Hierarchical Molecular Representation Learning via Fragment-Based Self-Supervised Embedding Predictioncited by 1
  32. 2023GlycoNMR: Dataset and benchmarks for NMR chemical shift prediction of carbohydrates with graph neural networkscited by 0
  33. 2025NovoMolGen: Rethinking Molecular Language Model Pretrainingcited by 0
  34. 2025Cross-Modality Controlled Molecule Generation with Diffusion Language Modelcited by 0
  35. 2025Flow-Based Fragment Identification via Binding Site-Specific Latent Representationscited by 0
  36. 2025MMM: Quantum-Chemical Molecular Representation Learning for Combinatorial Drug Recommendationcited by 0
  37. 2025Pruning and Quantization Impact on Graph Neural Networkscited by 0
  38. 2026MEGA-CL: A Molecular Foundation Model for Generalizable ADMET Prediction through Graph External Attention and Contrastive Learningcited by 0

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