3d-equivariant graph neural networks for protein model quality assessment

HIGHLIGHTS

• who: Bioinformatics et al. from the Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA have published the article: 3D-equivariant graph neural networks for protein model quality assessment, in the Journal: (JOURNAL) of April/30,/2018
• what: The authors develop EnQA a novel graph-based 3D-equivariant neural network method that is equivariant to rotation and translation of 3D objects to estimate the accuracy of protein models by leveraging the features acquired from the state-of-the-art tertiary structure prediction method-AlphaFold2. The models from CASP812 are used for . . .
  

   

  

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