mlsanityData to 7 Oct 2026

GraphNVP: An Invertible Flow Model for Generating Molecular Graphs

arXiv 1905.11600 ↗First posted 28 May 2019stat.ML cs.AI cs.LGcited by 115

Not on the map. Why?

Cited by 27 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We propose GraphNVP, the first invertible, normalizing flow-based molecular graph generation model. We decompose the generation of a graph into two steps: generation of (i) an adjacency tensor and (ii) node attributes. This decomposition yields the exact likelihood maximization on graph-structured data, combined with two novel reversible flows. We empirically demonstrate that our model efficiently generates valid molecular graphs with almost no duplicated molecules. In addition, we observe that the learned latent space can be used to generate molecules with desired chemical properties.

Key facts. Cited by 115 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 May 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 17; 2022: 30; 2023: 24; 2024: 17; 2025: 18; 2026: 8.

115 papers.

Sort:

By area:

  1. 2022DiGress: Discrete Denoising diffusion for graph generationcited by 503
  2. 2022Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equationscited by 273
  3. 2020Permutation Invariant Graph Generation via Score-Based Generative Modelingcited by 263
  4. 2022Graph neural networks for materials science and chemistrycited by 181
  5. 2021GraphDF: A Discrete Flow Model for Molecular Graph Generationcited by 177
  6. 2022Generating 3D Molecules for Target Protein Bindingcited by 104
  7. 2023MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generationcited by 96
  8. 2021GraphEBM: Molecular Graph Generation with Energy-Based Modelscited by 85
  9. 2023A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?cited by 84
  10. 2023Autoregressive Diffusion Model for Graph Generationcited by 82
  11. 2024Variational Flow Matching for Graph Generationcited by 78
  12. 2023Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modelingcited by 75
38 more
  1. 2021AutoGCL: Automated Graph Contrastive Learning via Learnable View Generatorscited by 73
  2. 2024Discrete-state Continuous-time Diffusion for Graph Generationcited by 73
  3. 2022Equivariant Energy-Guided SDE for Inverse Molecular Designcited by 72
  4. 2021Keeping it Simple: Language Models can learn Complex Molecular Distributionscited by 68
  5. 2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
  6. 2024Efficient Evolutionary Search Over Chemical Space with Large Language Modelscited by 59
  7. 2021Differentiable Scaffolding Tree for Molecular Optimizationcited by 53
  8. 2022Regression Transformer: Concurrent sequence regression and generation for molecular language modelingcited by 49
  9. 2021Improving Graph Neural Networks with Simple Architecture Designcited by 41
  10. 2022LIMO: Latent Inceptionism for Targeted Molecule Generationcited by 40
  11. 2023A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Materialcited by 34
  12. 20223DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Designcited by 33
  13. 2023Genetic algorithms are strong baselines for molecule generationcited by 31
  14. 2021Artificial Intelligence in Drug Discovery: Applications and Techniquescited by 28
  15. 2022Data-Efficient Graph Grammar Learning for Molecular Generationcited by 26
  16. 2023SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generationcited by 26
  17. 2023Sparse Training of Discrete Diffusion Models for Graph Generationcited by 26
  18. 2024Diffusion Models in textitDe Novo Drug Designcited by 26
  19. 2021MolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generationcited by 22
  20. 2023Data-centric Graph Learning: A Surveycited by 22
  21. 2022Graph Neural Networks for Moleculescited by 21
  22. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  23. 2022Target-aware Molecular Graph Generationcited by 18
  24. 2022NVDiff: Graph Generation through the Diffusion of Node Vectorscited by 18
  25. 2024Generative Modelling of Structurally Constrained Graphscited by 17
  26. 2025Graph Generative Pre-trained Transformercited by 16
  27. 2023A Simple and Scalable Representation for Graph Generationcited by 15
  28. 2024Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunitiescited by 14
  29. 2022Molecule Generation for Drug Design: a Graph Learning Perspectivecited by 13
  30. 2022Modular Flows: Differential Molecular Generationcited by 11
  31. 2022Score-Based Generative Models for Molecule Generationcited by 10
  32. 2023An Equivariant Generative Framework for Molecular Graph-Structure Co-Designcited by 10
  33. 2023Discrete Graph Auto-Encodercited by 10
  34. 2022FastFlows: Flow-Based Models for Molecular Graph Generationcited by 9
  35. 2023Node-Aligned Graph-to-Graph (NAG2G): Elevating Template-Free Deep Learning Approaches in Single-Step Retrosynthesiscited by 9
  36. 2025Simple and Critical Iterative Denoising: A Recasting of Discrete Diffusion in Graph Generationcited by 9
  37. 2022TO-FLOW: Efficient Continuous Normalizing Flows with Temporal Optimization adjoint with Moving Speedcited by 8
  38. 2022Conditional β-VAE for De Novo Molecular Generationcited by 8

50 most cited of 115 All 115 (JSON)