🦠 Model Request: Ersilia Compound Embeddings

[GemmaTuron]

Model Name

Ersilia Compound Embeddings

Model Description

Bioactivity-aware chemical embeddings for small molecules. Using transfer learning, we have created a fast network that produces embeddings of 1024 features condensing physicochemical as well as bioactivity information The training of the network has been done using the FS-Mol and ChEMBL datasets, and Grover, Mordred and ECFP descriptors

Slug

eosce

Tag

Descriptor,Embedding

Publication

https://github.com/ersilia-os/compound-embedding

Source Code

https://github.com/ersilia-os/compound-embedding

License

GPL-3.0

[GemmaTuron]

/approve

[github-actions[bot]]

New Model Repository Created! 🎉

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

🔗 ersilia-os/eos2gw4

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!

[GemmaTuron]

This model is incorporated