When the same performance analysis was performed for Auto3D, we observed the mean RMSD was as low as 0.69 Å for model structures and 0.47 Å (Table ) for ideal structures in an average ensemble size of 62 (For individual ensemble sizes and RMSD values, refer to Supporting Information). Similarly, our DeepConf with different parameter settings (Table ) could produce mean RMSD as low as 0.54 Å for model and 0.39 Å for ideal structures. Here, "parset2" uses 180° (nfold=2) for each torsion point and produces the mean RMSD of 0.81 Å (model) and 0.54 Å (ideal) in just 18 average ensemble size. Moreover, by increasing the "nfold", one can easily boost up the performance of DeepConf. Here, in our test of parset4, where nfold=4 (i.e., 90° rotations on each torsion), we could improve the RMSD values to 0.54 Å and 0.39 Å for model and ideal structures, respectively. One might argue that the ensemble size is also increased tremendously (up to 322 in parset4). This is mostly because we have set the convergence criteria to 0.02 eV/Å (maximum force) in all parameter settings to reduce the computational cost. When lower convergence criteria applied, the ensemble size would drop to tens rather than hundreds.