  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-scale
standardized benchmark and poor accuray hinder the research progress. In order
to standardize comparisons and draw more research interest, we use AlphaFold
DB, one of the world's largest protein structure databases, to establish a new
graph-based benchmark -- AlphaDesign. Based on AlphaDesign, we propose a new
method called ADesign to improve accuracy by introducing protein angles as new
features, using a simplified graph transformer encoder (SGT), and proposing a
confidence-aware protein decoder (CPD). Meanwhile, SGT and CPD also improve
model 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.
