Data to 7 Oct 2026

MolGAN: An implicit generative model for small molecular graphs

arXiv 1805.11973 ↗First posted 30 May 2018stat.ML cs.LGcited by 392

Not on the map. Why?

Cited by 60 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Deep generative models for graph-structured data offer a new angle on the problem of chemical synthesis: by optimizing differentiable models that directly generate molecular graphs, it is possible to side-step expensive search procedures in the discrete and vast space of chemical structures. We introduce MolGAN, an implicit, likelihood-free generative model for small molecular graphs that circumvents the need for expensive graph matching procedures or node ordering heuristics of previous likelihood-based methods. Our method adapts generative adversarial networks (GANs) to operate directly on graph-structured data. We combine our approach with a reinforcement learning objective to encourage the generation of molecules with specific desired chemical properties. In experiments on the QM9 chemical database, we demonstrate that our model is capable of generating close to 100% valid compounds. MolGAN compares favorably both to recent proposals that use string-based (SMILES) representations of molecules and to a likelihood-based method that directly generates graphs, albeit being susceptible to mode collapse. Code at https://github.com/nicola-decao/MolGAN

Key facts. Cited by 392 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 May 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 80; 2022: 73; 2023: 71; 2024: 69; 2025: 65; 2026: 33.

392 papers.

Sort:

By area:

2022Equivariant Diffusion for Molecule Generation in 3Dcited by 666
2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANscited by 515
2021Flow Network based Generative Models for Non-Iterative Diverse Candidate Generationcited by 384
2022Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equationscited by 273
2023Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Modelscited by 232
2022Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pocketscited by 182
2022Graph neural networks for materials science and chemistrycited by 181
2021GraphDF: A Discrete Flow Model for Molecular Graph Generationcited by 177
2021MARS: Markov Molecular Sampling for Multi-objective Drug Discoverycited by 125
2021Accurate Learning of Graph Representations with Graph Multiset Poolingcited by 120
2023Machine Learning for Synthetic Data Generation: A Reviewcited by 118
2021Geometric Deep Learning on Molecular Representationscited by 113

38 more

2024InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Priorcited by 110
2022Generating 3D Molecules for Target Protein Bindingcited by 104
2021Deep Generative Models in Engineering Design: A Reviewcited by 103
2022Semantic Probabilistic Layers for Neuro-Symbolic Learningcited by 103
2022SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generatorscited by 96
2022Exploring Chemical Space with Score-based Out-of-distribution Generationcited by 95
2021GraphEBM: Molecular Graph Generation with Energy-Based Modelscited by 85
2023A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?cited by 84
2023Autoregressive Diffusion Model for Graph Generationcited by 82
2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 79
2022Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Predictioncited by 78
2021BertGCN: Transductive Text Classification by Combining GCN and BERTcited by 74
2021AutoGCL: Automated Graph Contrastive Learning via Learnable View Generatorscited by 73
2024Discrete-state Continuous-time Diffusion for Graph Generationcited by 73
2021Learning to Compose Visual Relationscited by 72
2021Learning to Extend Molecular Scaffolds with Structural Motifscited by 70
2022A Survey on Deep Graph Generation: Methods and Applicationscited by 69
2022Diffusion Models for Graphs Benefit From Discrete State Spacescited by 69
2021Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generationcited by 68
2021Keeping it Simple: Language Models can learn Complex Molecular Distributionscited by 68
2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
2023A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systemscited by 65
2021A Review of Graph Neural Networks and Their Applications in Power Systemscited by 62
2022MDM: Molecular Diffusion Model for 3D Molecule Generationcited by 58
2021Graph Coarsening with Neural Networkscited by 55
2021Molecule Generation by Principal Subgraph Mining and Assemblingcited by 53
2021Differentiable Scaffolding Tree for Molecular Optimizationcited by 53
2021Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learningcited by 52
2023Graph Generation with Diffusion Mixturecited by 52
2023On the Constrained Time-Series Generation Problemcited by 52
2022CLEAR: Generative Counterfactual Explanations on Graphscited by 48
2020TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learningcited by 46
2021Constructions in combinatorics via neural networkscited by 46
2022Reinforced Genetic Algorithm for Structure-based Drug Designcited by 46
2023Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generationcited by 46
2022GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generationcited by 43
2021HDMapGen: A Hierarchical Graph Generative Model of High Definition Mapscited by 40
2022LIMO: Latent Inceptionism for Targeted Molecule Generationcited by 40

50 most cited of 392 All 392 (JSON)