---
license: mit
language:
- en
tags:
- chemistry
- biology
- medical
pretty_name: HuggingLigand Embedding Dataset
datasets:
- RSE-Group11/Hugging-Ligand-Embeddings
code_repository: https://codebase.helmholtz.cloud/tud-rse-pojects-2025/group-11
---
# HuggingLigand Dataset

## Overview

**HuggingLigand** is a deep learning pipeline developed to predict the binding affinity between proteins and ligands. This prediction task is essential in fields such as **drug discovery**, **biophysics**, and **computational biology**, where determining how strongly a small molecule ligand binds to a protein target is a key step in understanding molecular interactions and prioritizing drug candidates.

The dataset provides precomputed embeddings for proteins and ligands, enabling efficient training and testing of machine learning models for binding affinity prediction.

## Dataset

**BindingDB**: A Dataset containing proteins, ligands, and their affinities provided at https://www.bindingdb.org/rwd/bind/index.jsp.

## Embedding Models Used

- **ProtT5**: A transformer-based protein language model pretrained on millions of protein sequences.
- **ChemBERTa**: A transformer-based molecular language model trained on SMILES representations of chemical compounds.

Both models generate high-dimensional embeddings from raw sequence and molecular data. These embeddings are concatenated and used as input to a customizable regression model, which predicts continuous binding affinity values (e.g., **Kd**, **Ki**, or **IC50**).

## Applications

- Structure-free binding affinity prediction
- Virtual screening and hit prioritization in drug discovery
- Data-driven biophysical modeling
- Benchmarking molecular embedding models

## Dataset Structure

The dataset contains:
- Protein embeddings generated by **ProtT5**
- Ligand embeddings generated by **ChemBERTa**
- Binding affinity labels (e.g., Kd, Ki, IC50)

## Contributing
Thank you for your interest in contributing to HuggingLigand! We welcome contributions from the community to help improve protein-ligand binding affinity prediction.  

By participating in this project, you agree to foster a respectful, inclusive, and collaborative environment. Be considerate in your interactions with others, and help us maintain a positive community.  

For detailed guidelines on how to contribute — including setting up your development environment, reporting issues, and submitting pull requests — please refer to the CONTRIBUTING.md file.  

## Links & Resources
* Project Repository: [HuggingLigand on GitLab](https://codebase.helmholtz.cloud/tud-rse-pojects-2025/group-11/-/tree/main?ref_type=heads)

* TestPyPI Sandbox: [huggingligand on TestPyPI](https://test.pypi.org/project/huggingligand/)

* Dataset on Zenodo sandbox publication: [huggingligand on Zenodo]()

* Dataset on Hugging Face Hub: [Hugging-Ligand-Embeddings](https://huggingface.co/datasets/RSE-Group11/Hugging-Ligand-embeddings)

* ChemBERTa Pretrained Model: [ChemBERTa](https://huggingface.co/seyonec/ChemBERTa-zinc-base-v1)

* ProtT5 Pretrained Model: [ProtT5-XL-UniRef50]()

* BindingDB Data Source: [BindingDB](https://doi.org/10.25504/FAIRsharing.3b36hk)


## License

This project is licensed under the terms of the MIT License.
It also makes use of third-party components, which are subject to their respective licenses:

* ProtT5: MIT License / Academic Free License v3.0
* ChemBERTa: MIT License
* BindingDB: Creative Commons Attribution 3.0 License

Please review the LICENSE file for full details.