1. A mathematical formulation for the entire problem is missed. Though the problem is complex and difficult to be solved with an end-to-end framework, the original formulation is still needed at the beginning of the Methods section, followed by a brief introduction of the entire framework, i.e. how to split the entire task into several components and what specific role does each component play. I also suggest authors present a figure to illustrate the overall computing procedure of ModelAngelo. 2. Quantitive evaluation results in Figure 3 only reflect middle outputs rather than the final outputs. Figure 4 illustrates the comparison of final results with a single data sample. Thereby, current evaluations are not convincing enough to confirm ModelAngelo’s superiority to competitors. Is it possible for a quantitive comparison on the final outputs? 3. Many losses (e.g. (RMSD) loss, backbone RMSD loss, amino-acid classification loss, local confidence score loss, torsion angles loss, and full atom loss) are involved in the learning of GNN. What are the definition of these losses? How do you get the ground truth labels required (if so) for these losses?
2. Quantitive evaluation results in Figure 3 only reflect middle outputs rather than the final outputs. Figure 4 illustrates the comparison of final results with a single data sample. Thereby, current evaluations are not convincing enough to confirm ModelAngelo’s superiority to competitors. Is it possible for a quantitive comparison on the final outputs?