mlsanityData to 14 Oct 2026

Graph Neural Networks for Molecules

arXiv 2209.05582 ↗First posted 12 Sep 2022cs.LG physics.chem-phcited by 21

Not on the map. Why?

Abstract (arXiv)

Graph neural networks (GNNs), which are capable of learning representations from graphical data, are naturally suitable for modeling molecular systems. This review introduces GNNs and their various applications for small organic molecules. GNNs rely on message-passing operations, a generic yet powerful framework, to update node features iteratively. Many researches design GNN architectures to effectively learn topological information of 2D molecule graphs as well as geometric information of 3D molecular systems. GNNs have been implemented in a wide variety of molecular applications, including molecular property prediction, molecular scoring and docking, molecular optimization and de novo generation, molecular dynamics simulation, etc. Besides, the review also summarizes the recent development of self-supervised learning for molecules with GNNs.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 221 of its 442 references that are in the map. First posted on arXiv 12 Sep 2022.

Builds on

221 of 442 references.

References · 221

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  4. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  5. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  6. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  7. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  8. 2020Denoising Diffusion Implicit Modelscited by 9,781
  9. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  10. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  11. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  12. 2017Inductive Representation Learning on Large Graphscited by 6,320
38 more
  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  2. 2022Generative Adversarial Networkscited by 5,627
  3. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,750
  4. 2018How Powerful are Graph Neural Networks?cited by 4,393
  5. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
  6. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  7. 2013Auto-Encoding Variational Bayescited by 4,053
  8. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  9. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  10. 2020Exploring Simple Siamese Representation Learningcited by 2,866
  11. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,857
  12. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,173
  13. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  14. 2014Conditional Generative Adversarial Netscited by 2,162
  15. 2016Density estimation using Real NVPcited by 2,042
  16. 2020Rethinking Attention with Performerscited by 1,601
  17. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,591
  18. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  19. 2016Context Encoders: Feature Learning by Inpaintingcited by 1,428
  20. 2016Variational Graph Auto-Encoderscited by 1,382
  21. 2018Relational inductive biases, deep learning, and graph networkscited by 1,360
  22. 2020Graph Contrastive Learning with Augmentationscited by 1,208
  23. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,102
  24. 2014NICE: Non-linear Independent Components Estimationcited by 1,058
  25. 2021VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learningcited by 1,057
  26. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,051
  27. 2016Geometric deep learning: going beyond Euclidean datacited by 1,048
  28. 2020Improved Techniques for Training Score-Based Generative Modelscited by 1,043
  29. 2021E(n) Equivariant Graph Neural Networkscited by 965
  30. 2015Gated Graph Sequence Neural Networkscited by 796
  31. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 787
  32. 2016Automatic chemical design using a data-driven continuous representation of moleculescited by 765
  33. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 754
  34. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 746
  35. 2022Recipe for a General, Powerful, Scalable Graph Transformercited by 713
  36. 2020Benchmarking Graph Neural Networkscited by 680
  37. 2022Equivariant Diffusion for Molecule Generation in 3Dcited by 677
  38. 2020A Generalization of Transformer Networks to Graphscited by 661

50 most cited of 221 All 221 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 6; 2025: 9; 2026: 6.

21 papers.

Sort:

  1. 2024LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Datasetcited by 128
  2. 2024MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generationcited by 7
  3. 2024GraphXForm: Graph transformer for computer-aided molecular designcited by 6
  4. 2025MatterTune: An Integrated, User-Friendly Platform for Fine-Tuning Atomistic Foundation Models to Accelerate Materials Simulation and Discoverycited by 5
  5. 2026Multi-Domain Riemannian Graph Gluing for Building Graph Foundation Modelscited by 5
  6. 2026Adaptive Diffusion Posterior Sampling for Data and Model Fusion of Complex Nonlinear Dynamical Systemscited by 3
  7. 2024A Self-feedback Knowledge Elicitation Approach for Chemical Reaction Predictionscited by 2
  8. 2024Graph Neural Networks Need Cluster-Normalize-Activate Modulescited by 2
  9. 2025Statistical physics analysis of graph neural networks: Approaching optimality in the contextual stochastic block modelcited by 2
  10. 2025Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detectioncited by 2
  11. 2025VALID-Mol: a Systematic Framework for Validated LLM-Assisted Molecular Designcited by 2
  12. 2025Molecular Odor Prediction Based on Multi-Feature Graph Attention Networkscited by 1
9 more
  1. 2025Large Language Models Transform Organic Synthesis From Reaction Prediction to Automationcited by 1
  2. 2024Lightweight Geometric Deep Learning for Molecular Modelling in Catalyst Discoverycited by 0
  3. 2025Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyondcited by 0
  4. 2025Spatio-Temporal Graph Structure Learning for Earthquake Detectioncited by 0
  5. 2025Self-Supervised Discovery of Neural Circuits in Spatially Patterned Neural Responses with Graph Neural Networkscited by 0
  6. 2026BadImplant: Injection-based Multi-Targeted Graph Backdoor Attackcited by 0
  7. 2026Fed-Listing: Federated Label Distribution Inference in Graph Neural Networkscited by 0
  8. 2026Closing the Prior-Posterior Loop: Self-Reflective Molecular Design with Analysis-Driven LLM Iterationcited by 0
  9. 2026Differential Learning for Robust Prediction of Thermal Stability with Application to Energetic Materialscited by 0