Regularly updated benchmark sets for statistically correct evaluations of AlphaFold applications
AlphaFold2 changed structural biology by providing high-quality structure predictions for all possible proteins. Since its inception, a plethora of applications were built on AlphaFold2, expediting discoveries in virtually all areas related to protein science. In many cases, however, optimism seems to have made scientists forget about data leakage, a serious issue that needs to be addressed when evaluating machine learning methods. Here we provide a rigorous benchmark set that can be used in a broad range of applications built around AlphaFold2/3.
Authors
- Péter Tompa (ORCID: https://orcid.org/0000-0001-8042-9939)
- Gábor Tusnády (ORCID: https://orcid.org/0000-0001-8105-0285)
- László Dobson (ORCID: https://orcid.org/0000-0003-2765-3872)
Institutions
- Semmelweis University (HU)
- Vlaams Instituut voor Biotechnologie (BE)
- National Institute of Oncology (HU)
- VIB-VUB Center for Structural Biology (BE)
- HUN-REN Research Centre for Natural Sciences (HU)
Publication Details
- Journal
- Briefings in Bioinformatics
- Published
- 2025-02-27
- DOI
- https://doi.org/10.1093/bib/bbaf104
- Citations
- 4
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 3.22
Funders
- Nemzeti Kutatási Fejlesztési és Innovációs Hivatal