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Awesome-GNN-based-drug-discovery

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This is a curated list of research papers, resources and tools related to using Graph Neural Networks (GNNs) for drug discovery.

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<h1 align="center">Awesome-GNN-based-drug-discovery</h1>

This is a curated list of resources and tools related to using Graph Neural Networks (GNNs) for drug discovery. GNNs are a powerful class of machine learning models that can operate on graph-structured data, which makes them especially well-suited for analyzing molecules and molecular interactions.

This repository aims to provide an overview of the latest developments in GNN-based drug discovery, including:

- **[Awesome-lists:](Awesome-lists)** Awesome list for GNNs and drug discovery enthusiasts, including scholarly papers, datasets, tutorials, and software.
- **[Research papers:](Research_Papers)** A selection of recent papers on GNNs for molecular property prediction, ligand-based virtual screening, and other drug discovery tasks.
- **[Databases and Datasets:](Databases_and_Datasets)** Collections of molecular data suitable for training and evaluating GNN models, such as the MoleculeNet benchmark dataset.
- **[Software and libraries:](Software_and_Libraries)** Tools for building and training GNN models for drug discovery, including popular libraries like PyTorch Geometric, DeepChem, and RDKit.
- **[Tutorials and Courses:](Tutorials_and_Courses)** Resources for learning about GNNs and their application to drug discovery, including online courses and tutorials.

The goal of this repository is to provide a comprehensive starting point for researchers and practitioners interested in using GNNs for drug discovery. We welcome contributions and suggestions for additional resources to include.

## How to Contribute

I encourage contributions from the community! If you have suggestions for resources, tools, or anything else that could be added to this list, please follow these steps:

1. **Fork the repository**: Click the fork button on the top right of this page to create your own copy of this repository.
2. **Make your changes**: Add your suggested resources, fix typos, or make other improvements.
3. **Submit a pull request**: Once you're happy with your changes, submit a pull request to merge your changes into the main repository. Please provide a brief explanation of what you've added or changed.

I appreciate your contributions and look forward to growing this resource together!

## License

This repository is open source and available under the [MIT License](LICENSE). We believe in the power of open source to drive innovation and collaboration in the field of drug discovery.

Awesome-GNN-based-drug-discovery

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