# A Survey of Graph Neural Networks for Drug Discovery: Recent Developments and Challenges

arXiv: https://arxiv.org/abs/2509.07887 · HTML: https://mlsanity.com/paper/2509.07887/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 42 references that are in the map.
- First posted on arXiv 9 Sep 2025.

## Builds on (most cited first, 4 of 4)

- [Molecular graph generation with Graph Neural Networks](https://mlsanity.com/paper/2012.07397/) (2020)
- [DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations](https://mlsanity.com/paper/2107.02467/) (2021)
- [Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network](https://mlsanity.com/paper/2311.09261/) (2023)
- [GraphGANFed: A Federated Generative Framework for Graph-Structured Molecules Towards Efficient Drug Discovery](https://mlsanity.com/paper/2304.05498/) (2023)

## Abstract (arXiv)

Graph Neural Networks (GNNs) have gained traction in the complex domain of drug discovery because of their ability to process graph-structured data such as drug molecule models. This approach has resulted in a myriad of methods and models in published literature across several categories of drug discovery research. This paper covers the research categories comprehensively with recent papers, namely molecular property prediction, including drug-target binding affinity prediction, drug-drug interaction study, microbiome interaction prediction, drug repositioning, retrosynthesis, and new drug design, and provides guidance for future work on GNNs for drug discovery.