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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 list for GNNs and drug discovery enthusiasts, including scholarly papers, datasets, tutorials, and software.
• 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: Collections of molecular data suitable for training and evaluating GNN models, such as the MoleculeNet benchmark dataset.
• 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: 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.

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!

This repository is open source and available under theMIT License. We believe in the power of open source to drive innovation and collaboration in the field of drug discovery.