🦠 Model Request: MolE molecular embeddings

[miquelduranfrigola]

Model Name

MolE molecular embeddings

Model Description

MolE is a foundation model for chemistry developed by Recursion. It combines geometric deep learning with transformers, to learn a meaningful representation of molecules. MolE leverages extensive labeled and unlabeled datasets in two pretraining steps. First it follows a novel self-supervised strategy using the graph representation of ~842 million molecules designed to properly learn to represent chemical structures. It is followed by a massive multi-task training to assimilate biological information.

Slug

mole-embeddings

Tag

Embedding

Publication

https://www.nature.com/articles/s41467-024-53751-y

Source Code

https://github.com/recursionpharma/mole_public

License

MIT

[miquelduranfrigola]

/approve

[github-actions[bot]]

New Model Repository Created! 🎉

@miquelduranfrigola ersilia model respository has been successfully created and is available at:

🔗 ersilia-os/eos3dq3

Next Steps ⭐

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• 🍴 Get started by creating a fork of your new model repository - docs
• 👯 Clone your forked repository - docs
• ✏️ Make edits to your new forked model repository - docs - Edits might include:
  • Updating the README.md file to accurately describe your model
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Additional Resources 📚

• CONTRIBUTING.md
• About Pull Requests
• About Forks

If you have any questions, please feel free to open an issue and get support from the community!

[miquelduranfrigola]

Unfortunately I cannot incorporate this model yet since the pre-trained models are not yet available in the repository. I have opened an issue: https://github.com/recursionpharma/mole_public/issues/2