mlsanityData to 14 Oct 2026

Junction Tree Variational Autoencoder for Molecular Graph Generation

arXiv 1802.04364 ↗First posted 12 Feb 2018cs.LG cs.NE stat.MLcited by 652

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Cited by 87 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We seek to automate the design of molecules based on specific chemical properties. In computational terms, this task involves continuous embedding and generation of molecular graphs. Our primary contribution is the direct realization of molecular graphs, a task previously approached by generating linear SMILES strings instead of graphs. Our junction tree variational autoencoder generates molecular graphs in two phases, by first generating a tree-structured scaffold over chemical substructures, and then combining them into a molecule with a graph message passing network. This approach allows us to incrementally expand molecules while maintaining chemical validity at every step. We evaluate our model on multiple tasks ranging from molecular generation to optimization. Across these tasks, our model outperforms previous state-of-the-art baselines by a significant margin.

Key facts. Cited by 652 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Feb 2018.

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Citing papers per year: 2020: 1; 2021: 99; 2022: 111; 2023: 127; 2024: 126; 2025: 97; 2026: 91.

652 papers.

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  9. 2020Permutation Invariant Graph Generation via Score-Based Generative Modelingcited by 270
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  11. 2022Long Range Graph Benchmarkcited by 263
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38 more
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  5. 2022Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pocketscited by 183
  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. 2021Learning Gradient Fields for Molecular Conformation Generationcited by 163
  10. 2021Motif-based Graph Self-Supervised Learning for Molecular Property Predictioncited by 159
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  12. 2022A 3D Generative Model for Structure-Based Drug Designcited by 149
  13. 2022GOOD: A Graph Out-of-Distribution Benchmarkcited by 141
  14. 2024Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decodingcited by 136
  15. 2022Learning GFlowNets from partial episodes for improved convergence and stabilitycited by 132
  16. 2022A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Languagecited by 131
  17. 2021MARS: Markov Molecular Sampling for Multi-objective Drug Discoverycited by 125
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  19. 2021Geometric Deep Learning on Molecular Representationscited by 113
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  22. 2022Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoderscited by 102
  23. 2022Multi-Objective GFlowNetscited by 101
  24. 2022SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generatorscited by 99
  25. 2022Exploring Chemical Space with Score-based Out-of-distribution Generationcited by 95
  26. 2022Physics-Informed Machine Learning: A Survey on Problems, Methods and Applicationscited by 92
  27. 2024LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discoverycited by 86
  28. 2021GraphEBM: Molecular Graph Generation with Energy-Based Modelscited by 85
  29. 2024GFT: Graph Foundation Model with Transferable Tree Vocabularycited by 85
  30. 2023Autoregressive Diffusion Model for Graph Generationcited by 83
  31. 2021Graph Representation Learning in Biomedicinecited by 80
  32. 2022Local Latent Space Bayesian Optimization over Structured Inputscited by 79
  33. 2022Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Predictioncited by 78
  34. 2021DIG: A Turnkey Library for Diving into Graph Deep Learning Researchcited by 76
  35. 2022SELFIES and the future of molecular string representationscited by 76
  36. 2024Discrete-state Continuous-time Diffusion for Graph Generationcited by 76
  37. 2022Equivariant Energy-Guided SDE for Inverse Molecular Designcited by 73
  38. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72

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