Comparative benchmark of different methods on multi-point variations from the PTmul-NR dataset.
| Method . | PCC . | RMSE . | MAE . |
|---|---|---|---|
| DDGemb | 0.59 | 2.16 | 1.59 |
| FoldX | 0.36 | 5.51 | 3.66 |
| MAESTRO | 0.28 | 2.55 | 1.88 |
| DDGun | 0.23 | 2.55 | 2.10 |
| DDGun3D | 0.17 | 2.57 | 2.08 |
| Method . | PCC . | RMSE . | MAE . |
|---|---|---|---|
| DDGemb | 0.59 | 2.16 | 1.59 |
| FoldX | 0.36 | 5.51 | 3.66 |
| MAESTRO | 0.28 | 2.55 | 1.88 |
| DDGun | 0.23 | 2.55 | 2.10 |
| DDGun3D | 0.17 | 2.57 | 2.08 |
Comparative benchmark of different methods on multi-point variations from the PTmul-NR dataset.
| Method . | PCC . | RMSE . | MAE . |
|---|---|---|---|
| DDGemb | 0.59 | 2.16 | 1.59 |
| FoldX | 0.36 | 5.51 | 3.66 |
| MAESTRO | 0.28 | 2.55 | 1.88 |
| DDGun | 0.23 | 2.55 | 2.10 |
| DDGun3D | 0.17 | 2.57 | 2.08 |
| Method . | PCC . | RMSE . | MAE . |
|---|---|---|---|
| DDGemb | 0.59 | 2.16 | 1.59 |
| FoldX | 0.36 | 5.51 | 3.66 |
| MAESTRO | 0.28 | 2.55 | 1.88 |
| DDGun | 0.23 | 2.55 | 2.10 |
| DDGun3D | 0.17 | 2.57 | 2.08 |
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