| | --- |
| | 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. |
| |
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| |
|
