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While DeepMind has tentatively solved protein folding, its inverse problem --protein design which predicts protein sequences from their 3D structures --still faces significant challenges. Particularly, the lack of large-scalestandardized benchmark and poor accuray hinder the research progress. In orderto standardize comparisons and draw more research interest, we use AlphaFoldDB, one of the world's largest protein structure databases, to establish a newgraph-based benchmark -- AlphaDesign. Based on AlphaDesign, we propose a newmethod called ADesign to improve accuracy by introducing protein angles as newfeatures, using a simplified graph transformer encoder (SGT), and proposing aconfidence-aware protein decoder (CPD). Meanwhile, SGT and CPD also improvemodel efficiency by simplifying the training and testing procedures.Experiments show that ADesign significantly outperforms previous graph models,e.g., the average accuracy is improved by 8\%, and the inference speed is 40+times faster than before.

AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB