Biomedical data, from lab to paper.

Biocentral is an open-source, innovative bioinformatics platform designed to bridge the gap between the latest developments in bioinformatics and applications in molecular biology and diagnostic medicine.

Check out our getting started guide to learn how to use biocentral.

• User-Friendly GUI: Simplify complex protein data analysis, visualization, and modeling.
• One-Click AI Model Training: Train AI models on your data with just a few clicks.
• Distributed Computing: Run computational heavy operations on high-performance servers and inspect the results on your personal machine.
• Take Your Data Everywhere: All major desktop platforms (Windows, macOS, Linux) and browsers are supported.
• Open Source and FAIR: Committed to open science and FAIR principles.
• Customizable Plugins: Work only with the functionality that is fitting your specific research.

Download and install the latest version for your operating system from GitHub, or use it directly in the browser.

Current platform compatibility:

We heartily welcome contributions! Check out our contributing guidelines for details.

If you are a researcher in any related field (biology, medicine, informatics, ...) and want to use biocentral for your work, please do not hesitate to get in touch in case you encounter any issues or want to participate in the development of biocentral: [email protected]

Comprehensive documentation, tutorials, and API references are available at biocentral.cloud.

Biocentral is open-source software licensed under the GNU General Public License v3.0. See the LICENSE file for details.

Please cite our paper if you are using biocentral in your work:

@Article{Franz2026,
  author    = {Franz, Sebastian and Olenyi, Tobias and Schloetermann, Paula and Smaoui, Amine and Jimenez-Soto, Luisa F. and Rost, Burkhard},
  journal   = {Journal of Molecular Biology},
  title     = {biocentral: embedding-based protein predictions},
  year      = {2026},
  issn      = {0022-2836},
  month     = jan,
  pages     = {169673},
  doi       = {10.1016/j.jmb.2026.169673},
  groups    = {[JMB] biocentral: embedding-based protein predictions, swc_bo_engineering},
  publisher = {Elsevier BV},
}