SGNet: Folding Symmetrical Protein Complex with Deep Learning

doi:doi:10.57702/i0alhpxy

SGNet: Folding Symmetrical Protein Complex with Deep Learning

The SGNet dataset is a benchmark for symmetrical protein complex structure prediction. It contains a set of symmetrical protein complexes with different symmetry types (C, D, T, O, I). The dataset is used to evaluate the performance of the SGNet model in predicting symmetrical protein complex structures.

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The json representation of the dataset with its distributions based on DCAT.
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Zhaoqun Li, Jingcheng Yu, Qiwei Ye (2024). Dataset: SGNet: Folding Symmetrical Protein Complex with Deep Learning. https://doi.org/10.57702/i0alhpxy

DOI retrieved: December 16, 2024

Additional Info

Field	Value
Created	December 16, 2024
Last update	December 16, 2024
Defined In	https://doi.org/10.48550/arXiv.2403.04395
Author	Zhaoqun Li
More Authors	Jingcheng Yu Qiwei Ye