# Graph Neural Network Architecture Search for Molecular Property Prediction

arXiv: https://arxiv.org/abs/2008.12187 · HTML: https://mlsanity.com/paper/2008.12187/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2020.

## Built on by (most cited first, 17 of 17)

- [Neural Architecture Search: Insights from 1000 Papers](https://mlsanity.com/paper/2301.08727/) (2023)
- [Automated Machine Learning on Graphs: A Survey](https://mlsanity.com/paper/2103.00742/) (2021)
- [Pooling Architecture Search for Graph Classification](https://mlsanity.com/paper/2108.10587/) (2021)
- [NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search](https://mlsanity.com/paper/2206.09166/) (2022)
- [AutoGEL: An Automated Graph Neural Network with Explicit Link Information](https://mlsanity.com/paper/2112.01064/) (2021)
- [Search to Fine-tune Pre-trained Graph Neural Networks for Graph-level Tasks](https://mlsanity.com/paper/2308.06960/) (2023)
- [Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions](https://mlsanity.com/paper/2201.01288/) (2022)
- [Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search](https://mlsanity.com/paper/2307.10438/) (2023)
- [Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification](https://mlsanity.com/paper/2302.08671/) (2023)
- [Automated Graph Learning via Population Based Self-Tuning GCN](https://mlsanity.com/paper/2107.04713/) (2021)
- [Combinatorial Optimization with Automated Graph Neural Networks](https://mlsanity.com/paper/2406.02872/) (2024)
- [Automatic Graph Topology-Aware Transformer](https://mlsanity.com/paper/2405.19779/) (2024)
- [Knowledge-aware Evolutionary Graph Neural Architecture Search](https://mlsanity.com/paper/2411.17339/) (2024)
- [Edge-featured Graph Neural Architecture Search](https://mlsanity.com/paper/2109.01356/) (2021)
- [A Graph Neural Architecture Search Approach for Identifying Bots in Social Media](https://mlsanity.com/paper/2411.16285/) (2024)
- [Deal: Distributed End-to-End GNN Inference for All Nodes](https://mlsanity.com/paper/2503.02960/) (2025)
- [Resource-Adaptive Successive Doubling for Hyperparameter Optimization with Large Datasets on High-Performance Computing Systems](https://mlsanity.com/paper/2412.02729/) (2024)

## Abstract (arXiv)

Predicting the properties of a molecule from its structure is a challenging task. Recently, deep learning methods have improved the state of the art for this task because of their ability to learn useful features from the given data. By treating molecule structure as graphs, where atoms and bonds are modeled as nodes and edges, graph neural networks (GNNs) have been widely used to predict molecular properties. However, the design and development of GNNs for a given data set rely on labor-intensive design and tuning of the network architectures. Neural architecture search (NAS) is a promising approach to discover high-performing neural network architectures automatically. To that end, we develop an NAS approach to automate the design and development of GNNs for molecular property prediction. Specifically, we focus on automated development of message-passing neural networks (MPNNs) to predict the molecular properties of small molecules in quantum mechanics and physical chemistry data sets from the MoleculeNet benchmark. We demonstrate the superiority of the automatically discovered MPNNs by comparing them with manually designed GNNs from the MoleculeNet benchmark. We study the relative importance of the choices in the MPNN search space, demonstrating that customizing the architecture is critical to enhancing performance in molecular property prediction and that the proposed approach can perform customization automatically with minimal manual effort.