Accessible, uniform protein property prediction with a scikit-learn based toolset AIDE.

Abstract

Summary: Protein property prediction via machine learning with and without labeled data is becoming increasingly powerful, yet methods are disparate and capabilities vary widely over applications. The software presented here, "Artificial Intelligence Driven protein Estimation (AIDE)," enables instantiating, optimizing, and testing many zero-shot and supervised property prediction methods for variants and variable length homologs in a single, reproducible notebook or script by defining a modular, standardized application programming interface (API) that is drop-in compatible with scikit-learn transformers and pipelines.

Availability and implementation: 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. (Komp and Beckham 2025).

Supplementary information: Digital supplementary data contains API examples and a user guide. Appendix A and B provide PDFs of notebooks for showcases. Source data for figures are provided.