Table 2. Benchmark performance of RaSP versus other structure-based methods on the S669 direct experimental data set (Pancotti et al., 2022).
Results for methods other than RaSP have been copied from Pancotti et al., 2022. We speculate that the higher RMSE and MAE values for Rosetta relative to RaSP are due to missing scaling of Rosetta output onto a scale similar to kcal/mol.
| Method | S669, direct | ||
|---|---|---|---|
| Pearson | RMSE [kcal/mol] | MAE [kcal/mol] | |
| Structure-based | |||
| ACDC-NN | 0.46 | 1.49 | 1.05 |
| DDGun3D | 0.43 | 1.60 | 1.11 |
| PremPS | 0.41 | 1.50 | 1.08 |
| RaSP | 0.39 | 1.63 | 1.14 |
| ThermoNet | 0.39 | 1.62 | 1.17 |
| Rosetta | 0.39 | 2.70 | 2.08 |
| Dynamut | 0.41 | 1.60 | 1.19 |
| INPS3D | 0.43 | 1.50 | 1.07 |
| SDM | 0.41 | 1.67 | 1.26 |
| PoPMuSiC | 0.41 | 1.51 | 1.09 |
| MAESTRO | 0.50 | 1.44 | 1.06 |
| FoldX | 0.22 | 2.30 | 1.56 |
| DUET | 0.41 | 1.52 | 1.10 |
| I-Mutant3.0 | 0.36 | 1.52 | 1.12 |
| mCSM | 0.36 | 1.54 | 1.13 |
| Dynamut2 | 0.34 | 1.58 | 1.15 |