# Molecular graph generation with Graph Neural Networks

arXiv: https://arxiv.org/abs/2012.07397 · HTML: https://mlsanity.com/paper/2012.07397/

## Key facts

- Cited by 79 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 14 Dec 2020.

## Built on by (most cited first, 30 of 79)

- [Fairness in Graph Mining: A Survey](https://mlsanity.com/paper/2204.09888/) (2022)
- [Towards Self-Explainable Graph Neural Network](https://mlsanity.com/paper/2108.12055/) (2021)
- [Unnoticeable Backdoor Attacks on Graph Neural Networks](https://mlsanity.com/paper/2303.01263/) (2023)
- [Distributed Graph Neural Network Training: A Survey](https://mlsanity.com/paper/2211.00216/) (2022)
- [LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference](https://mlsanity.com/paper/2309.14331/) (2023)
- [MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training](https://mlsanity.com/paper/2312.08656/) (2023)
- [Provably Powerful Graph Neural Networks for Directed Multigraphs](https://mlsanity.com/paper/2306.11586/) (2023)
- [Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks](https://mlsanity.com/paper/2308.11825/) (2023)
- [Graph Neural Networks for Molecules](https://mlsanity.com/paper/2209.05582/) (2022)
- [FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralization](https://mlsanity.com/paper/2403.12474/) (2024)
- [Towards Sparsification of Graph Neural Networks](https://mlsanity.com/paper/2209.04766/) (2022)
- [Label-Wise Graph Convolutional Network for Heterophilic Graphs](https://mlsanity.com/paper/2110.08128/) (2021)
- [MGCVAE: Multi-objective Inverse Design via Molecular Graph Conditional Variational Autoencoder](https://mlsanity.com/paper/2202.07476/) (2022)
- [Towards Prototype-Based Self-Explainable Graph Neural Network](https://mlsanity.com/paper/2210.01974/) (2022)
- [A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy](https://mlsanity.com/paper/2306.08604/) (2023)
- [EvGNN: An Event-driven Graph Neural Network Accelerator for Edge Vision](https://mlsanity.com/paper/2404.19489/) (2024)
- [Graph Neural Networks for Brain Graph Learning: A Survey](https://mlsanity.com/paper/2406.02594/) (2024)
- [Advancing Graph Generation through Beta Diffusion](https://mlsanity.com/paper/2406.09357/) (2024)
- [MAG-GNN: Reinforcement Learning Boosted Graph Neural Network](https://mlsanity.com/paper/2310.19142/) (2023)
- [Safety in Graph Machine Learning: Threats and Safeguards](https://mlsanity.com/paper/2405.11034/) (2024)
- [RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks](https://mlsanity.com/paper/2307.07840/) (2023)
- [One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes](https://mlsanity.com/paper/2406.13544/) (2024)
- [ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation](https://mlsanity.com/paper/2309.05853/) (2023)
- [Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks](https://mlsanity.com/paper/2407.01979/) (2024)
- [RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification](https://mlsanity.com/paper/2308.02335/) (2023)
- [MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation](https://mlsanity.com/paper/2405.12519/) (2024)
- [Logical Characterizations of GNNs with Mean Aggregation](https://mlsanity.com/paper/2507.18145/) (2025)
- [Robust Graph Representation Learning via Predictive Coding](https://mlsanity.com/paper/2212.04656/) (2022)
- [Improving Expressive Power of Spectral Graph Neural Networks with Eigenvalue Correction](https://mlsanity.com/paper/2401.15603/) (2024)
- [Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation](https://mlsanity.com/paper/2404.00014/) (2024)

## Abstract (arXiv)

Drug Discovery is a fundamental and ever-evolving field of research. The design of new candidate molecules requires large amounts of time and money, and computational methods are being increasingly employed to cut these costs. Machine learning methods are ideal for the design of large amounts of potential new candidate molecules, which are naturally represented as graphs. Graph generation is being revolutionized by deep learning methods, and molecular generation is one of its most promising applications. In this paper, we introduce a sequential molecular graph generator based on a set of graph neural network modules, which we call MG^2N^2. At each step, a node or a group of nodes is added to the graph, along with its connections. The modular architecture simplifies the training procedure, also allowing an independent retraining of a single module. Sequentiality and modularity make the generation process interpretable. The use of graph neural networks maximizes the information in input at each generative step, which consists of the subgraph produced during the previous steps. Experiments of unconditional generation on the QM9 and Zinc datasets show that our model is capable of generalizing molecular patterns seen during the training phase, without overfitting. The results indicate that our method is competitive, and outperforms challenging baselines for unconditional generation.