mlsanityData to 14 Oct 2026

Constrained Graph Variational Autoencoders for Molecule Design

arXiv 1805.09076 ↗First posted 23 May 2018cs.LG stat.MLcited by 185

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Cited by 38 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Graphs are ubiquitous data structures for representing interactions between entities. With an emphasis on the use of graphs to represent chemical molecules, we explore the task of learning to generate graphs that conform to a distribution observed in training data. We propose a variational autoencoder model in which both encoder and decoder are graph-structured. Our decoder assumes a sequential ordering of graph extension steps and we discuss and analyze design choices that mitigate the potential downsides of this linearization. Experiments compare our approach with a wide range of baselines on the molecule generation task and show that our method is more successful at matching the statistics of the original dataset on semantically important metrics. Furthermore, we show that by using appropriate shaping of the latent space, our model allows us to design molecules that are (locally) optimal in desired properties.

Key facts. Cited by 185 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 May 2018.

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Citing papers per year: 2021: 39; 2022: 36; 2023: 41; 2024: 35; 2025: 22; 2026: 12.

185 papers.

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  1. 2022Equivariant Diffusion for Molecule Generation in 3Dcited by 677
  2. 2022DiGress: Discrete Denoising diffusion for graph generationcited by 520
  3. 2021Graph Self-Supervised Learning: A Surveycited by 289
  4. 2021Autoregressive Diffusion Modelscited by 210
  5. 2023Geometric Latent Diffusion Models for 3D Molecule Generationcited by 186
  6. 2022Graph neural networks for materials science and chemistrycited by 182
  7. 2021GraphDF: A Discrete Flow Model for Molecular Graph Generationcited by 180
  8. 20233D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Predictioncited by 177
  9. 2022A 3D Generative Model for Structure-Based Drug Designcited by 149
  10. 2022SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generatorscited by 99
  11. 2023MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generationcited by 98
  12. 2022Exploring Chemical Space with Score-based Out-of-distribution Generationcited by 95
38 more
  1. 2021Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-designcited by 86
  2. 2021GraphEBM: Molecular Graph Generation with Energy-Based Modelscited by 85
  3. 2023A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?cited by 84
  4. 2023Autoregressive Diffusion Model for Graph Generationcited by 83
  5. 2024Variational Flow Matching for Graph Generationcited by 83
  6. 2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 81
  7. 2022SELFIES and the future of molecular string representationscited by 76
  8. 2022Equivariant Energy-Guided SDE for Inverse Molecular Designcited by 73
  9. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  10. 2021Learning to Extend Molecular Scaffolds with Structural Motifscited by 71
  11. 2023GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learningcited by 70
  12. 2022A Survey on Deep Graph Generation: Methods and Applicationscited by 69
  13. 2021Vitruvion: A Generative Model of Parametric CAD Sketchescited by 66
  14. 2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
  15. 2023MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generationcited by 65
  16. 2023Equivariant Flow Matching with Hybrid Probability Transportcited by 60
  17. 2023DrugAssist: A Large Language Model for Molecule Optimizationcited by 59
  18. 2024Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generationcited by 54
  19. 2021Local Augmentation for Graph Neural Networkscited by 53
  20. 2023Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generationcited by 46
  21. 2023De Novo Molecular Generation via Connection-aware Motif Miningcited by 46
  22. 2022GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generationcited by 43
  23. 2022UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translationcited by 42
  24. 2021Top-N: Equivariant set and graph generation without exchangeabilitycited by 36
  25. 2023A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Materialcited by 35
  26. 2022Reinforced Causal Explainer for Graph Neural Networkscited by 34
  27. 20223DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Designcited by 33
  28. 2022Group SELFIES: A Robust Fragment-Based Molecular String Representationcited by 33
  29. 2021JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Designcited by 32
  30. 2023Contrastive Video Question Answering via Video Graph Transformercited by 32
  31. 2025DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectracited by 32
  32. 2023Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaborationcited by 30
  33. 2022SchNetPack 2.0: A neural network toolbox for atomistic machine learningcited by 29
  34. 2021Artificial Intelligence in Drug Discovery: Applications and Techniquescited by 28
  35. 2021How Much Space Has Been Explored? Measuring the Chemical Space Covered by Databases and Machine-Generated Moleculescited by 27
  36. 2023Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generationcited by 27
  37. 2023Sparse Training of Discrete Diffusion Models for Graph Generationcited by 27
  38. 2022Data-Efficient Graph Grammar Learning for Molecular Generationcited by 26

50 most cited of 185 All 185 (JSON)