metadata

title: README
emoji: 🚀
colorFrom: indigo
colorTo: pink
sdk: gradio
pinned: true
license: agpl-3.0
short_description: Accelerate Molecular Biology Research with Machine Learning
sdk_version: 5.25.2

MultiMolecule

Accelerate Molecular Biology Research with Machine Learning

🧬 Introduction

MultiMolecule is a framework that bridges molecular biology and machine learning. It offers machine learning tools specifically designed for biomolecular data (RNA, DNA, and protein).

MultiMolecule serves as a foundation for advancing research at the intersection of molecular biology and machine learning.

🚀 Features

📑 Resources

Model Hub: Models designed for biomolecular data.
Dataset Hub: Processed biomolecular datasets.

🛠️ Tools

pipelines: End-to-end workflows for applying models.
runner: Automatic Runner for training models.

⚙️ Infrastructure

data: Smart Dataset that automatically infer tasks—including their level (sequence, token, contact) and type (classification, regression).
tokenisers: Tokenizers for biomolecular sequences.
module: Neural network building blocks.

🔧 Installation

=== "Install the stable release from PyPI"

```bash
pip install multimolecule
```

=== "Install the latest development version"

```bash
pip install git+https://github.com/DLS5-Omics/multimolecule
```

📜 Citation

If you use MultiMolecule in your research, please cite us as follows:

@software{chen_2024_12638419,
  author    = {Chen, Zhiyuan and Zhu, Sophia Y.},
  title     = {MultiMolecule},
  doi       = {10.5281/zenodo.12638419},
  publisher = {Zenodo},
  url       = {https://doi.org/10.5281/zenodo.12638419},
  year      = 2024,
  month     = may,
  day       = 4
}

📄 License

We believe openness is the Foundation of Research.

MultiMolecule is licensed under the GNU Affero General Public License.

For additional terms and clarifications, please refer to our License FAQ.

Please join us in building an open research community.

SPDX-License-Identifier: AGPL-3.0-or-later