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Su, J.* ; Li, Z.* ; Tao, T.* ; Han, C.* ; He, Y.* ; Dai, F.* ; Yuan, Q.* ; Gao, Y.* ; Si, T.* ; Zhang, X.* ; Zhou, Y.* ; Shan, J.* ; Zhou, X.* ; Chang, X.* ; Jiang, S.* ; Ma, D.* ; Steinegger, M.* ; Ovchinnikov, S.* ; Yuan, F.* ; The OPMC (Heinzinger, M.)

Democratizing protein language model training, sharing and collaboration.

Nat. Biotechnol., DOI: 10.1038/s41587-025-02859-7 (2025)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Training and deploying large-scale protein language models typically requires deep machine learning expertise-a barrier for researchers outside this field. SaprotHub overcomes this challenge by offering an intuitive platform that facilitates training and prediction as well as storage and sharing of models. Here we provide the ColabSaprot framework built on Google Colab, which potentially powers hundreds of protein training and prediction applications, enabling researchers to collaboratively build and share customized models.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
ISSN (print) / ISBN 1087-0156
e-ISSN 1546-1696
Zeitschrift Nature Biotechnology
Verlag Nature Publishing Group
Verlagsort New York, NY
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)