mlsanityData to 7 Oct 2026

NeVAE: A Deep Generative Model for Molecular Graphs

arXiv 1802.05283 ↗First posted 14 Feb 2018cs.LG physics.soc-ph stat.MLcited by 66

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Abstract (arXiv)

Deep generative models have been praised for their ability to learn smooth latent representation of images, text, and audio, which can then be used to generate new, plausible data. However, current generative models are unable to work with molecular graphs due to their unique characteristics-their underlying structure is not Euclidean or grid-like, they remain isomorphic under permutation of the nodes labels, and they come with a different number of nodes and edges. In this paper, we first propose a novel variational autoencoder for molecular graphs, whose encoder and decoder are specially designed to account for the above properties by means of several technical innovations. Moreover, in contrast with the state of the art, our decoder is able to provide the spatial coordinates of the atoms of the molecules it generates. Then, we develop a gradient-based algorithm to optimize the decoder of our model so that it learns to generate molecules that maximize the value of certain property of interest and, given a molecule of interest, it is able to optimize the spatial configuration of its atoms for greater stability. Experiments reveal that our variational autoencoder can discover plausible, diverse and novel molecules more effectively than several state of the art models. Moreover, for several properties of interest, our optimized decoder is able to identify molecules with property values 121% higher than those identified by several state of the art methods based on Bayesian optimization and reinforcement learning

Key facts. Cited by 66 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Feb 2018.

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Citing papers per year: 2021: 19; 2022: 17; 2023: 13; 2024: 10; 2025: 6; 2026: 1.

66 papers.

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  1. 2023Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Modelscited by 232
  2. 2022Local Latent Space Bayesian Optimization over Structured Inputscited by 78
  3. 2022A Survey on Deep Graph Generation: Methods and Applicationscited by 69
  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. 2022On Evaluation Metrics for Graph Generative Modelscited by 44
  7. 2021HDMapGen: A Hierarchical Graph Generative Model of High Definition Mapscited by 40
  8. 2021DOCKSTRING: easy molecular docking yields better benchmarks for ligand designcited by 33
  9. 2021Artificial Intelligence in Drug Discovery: Applications and Techniquescited by 28
  10. 2022Data-Efficient Graph Grammar Learning for Molecular Generationcited by 26
  11. 2024Diffusion Models in textitDe Novo Drug Designcited by 26
  12. 2021Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learningcited by 23
38 more
  1. 2022Graph Neural Networks for Moleculescited by 21
  2. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  3. 2021Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learningcited by 19
  4. 2025Latent Bayesian Optimization via Autoregressive Normalizing Flowscited by 19
  5. 2022NVDiff: Graph Generation through the Diffusion of Node Vectorscited by 18
  6. 2023Advancing Bayesian Optimization via Learning Correlated Latent Spacecited by 18
  7. 2021Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Modelscited by 12
  8. 2024Conformal Load Prediction with Transductive Graph Autoencoderscited by 11
  9. 2024Inversion-based Latent Bayesian Optimizationcited by 11
  10. 2023Discrete Graph Auto-Encodercited by 10
  11. 2021Mask-GVAE: Blind Denoising Graphs via Partitioncited by 9
  12. 2022Maximum Common Subgraph Guided Graph Retrieval: Late and Early Interaction Networkscited by 8
  13. 2022Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoderscited by 8
  14. 2023ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generationcited by 8
  15. 2024Generating Graphs via Spectral Diffusioncited by 8
  16. 2024GLAD: Improving Latent Graph Generative Modeling with Simple Quantizationcited by 8
  17. 2023MAGNet: Motif-Agnostic Generation of Molecules from Shapescited by 7
  18. 2021Augmenting Molecular Deep Generative Models with Topological Data Analysis Representationscited by 6
  19. 2021Fragment-based Sequential Translation for Molecular Optimizationcited by 6
  20. 2022Neural Estimation of Submodular Functions with Applications to Differentiable Subset Selectioncited by 6
  21. 2023The power of motifs as inductive bias for learning molecular distributionscited by 6
  22. 2024Procedural Synthesis of Synthesizable Moleculescited by 6
  23. 2021Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discoverycited by 5
  24. 2023Diffusing on Two Levels and Optimizing for Multiple Properties: A Novel Approach to Generating Molecules with Desirable Propertiescited by 5
  25. 2024Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Modelscited by 5
  26. 2025Enhanced Route Planning with Calibrated Uncertainty Setcited by 5
  27. 2022An Unpooling Layer for Graph Generationcited by 3
  28. 2022GEMS: Scene Expansion using Generative Models of Graphscited by 3
  29. 2022Structure-based drug discovery with deep learningcited by 3
  30. 2023RGCVAE: Relational Graph Conditioned Variational Autoencoder for Molecule Designcited by 3
  31. 2023PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertipscited by 3
  32. 2021G-VAE, a Geometric Convolutional VAE for ProteinStructure Generationcited by 2
  33. 2022FLOWGEN: Fast and slow graph generationcited by 2
  34. 2022Beyond 1-WL with Local Ego-Network Encodingscited by 2
  35. 2023CDGraph: Dual Conditional Social Graph Synthesizing via Diffusion Modelcited by 2
  36. 2025Residual Reweighted Conformal Prediction for Graph Neural Networkscited by 2
  37. 2021Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19cited by 1
  38. 2021Revisiting 2D Convolutional Neural Networks for Graph-based Applicationscited by 1

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