🦠 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]

New Model Repository Created! 🎉

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

🔗 ersilia-os/eos3dq3

Next Steps ⭐

Now that your new model respository has been created, you are ready to start contributing to it!

Here are some brief starter steps for contributing to your new model repository:

Note: Many of the bullet points below will have extra links if this is your first time contributing to a GitHub repository

• 🍴 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
  • Add source code for your model
  • Adding documentation for your model
• 🚀 Open a Pull Request from your forked repository to the original repository. This will allow you to bring your local changes into the new ersilia model repository that was just created! - docs

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: recursionpharma/mole_public#2