Accessible, uniform protein property prediction with a scikit-learn based toolset AIDE
AIDE is an installable, importable python package inheriting from scikit-learn classes and API and is installable on Windows, Mac, and Linux. Many of the wrapped models internal to AIDE will be effectively inaccessible without a GPU, and some assume CUDA. The newest stable, tested version can be found at https://github.com/beckham-lab/aide_predict and a full user guide and API reference can be found at https://beckham-lab.github.io/aide_predict/. Static versions of both at the time of writing can be found on Zenodo.
Authors
- Japheth E. Gado (ORCID: https://orcid.org/0000-0002-0024-2531)
- Gregg T. Beckham (ORCID: https://orcid.org/0000-0002-3480-212X)
- Kresten Lindorff‐Larsen (ORCID: https://orcid.org/0000-0002-4750-6039)
- Evan Komp (ORCID: https://orcid.org/0000-0001-8386-8814)
- Kristoffer E. Johansson (ORCID: https://orcid.org/0000-0001-6054-0461)
- Nicholas P. Gauthier (ORCID: https://orcid.org/0000-0002-3680-7514)
Institutions
- University of Copenhagen (DK)
- National Laboratory of the Rockies (US)
- Harvard University (US)
- Dana-Farber Cancer Institute (US)
- Center for Systems Biology (US)
- Joint BioEnergy Institute (US)
Publication Details
- Journal
- Bioinformatics
- Published
- 2025-09-24
- DOI
- https://doi.org/10.1093/bioinformatics/btaf544
- Primary Topic
- Protein Structure and Dynamics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- U.S. Department of Energy
- Novo Nordisk
- Novo Nordisk Fonden
- Office of Science
- Office of Energy Efficiency and Renewable Energy
- Biological and Environmental Research
- Bioenergy Technologies Office
- Office of Energy Efficiency