Fold Classification
81.7Superfamily ScoreCDConv w/ MRM
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| CDConv w/ MRMInput Dimensionality=(3+1)D, MRM=true2026.06 | 81.7 | 60.8 | 99.7 | 80.7 | |
| CDConv w/ MRMPretraining Dataset=-2026.06 | 81.7 | 60.8 | 99.7 | 80.7 | |
| IEConv (Residue Level)Pretraining Dataset=PDB 476K2026.06 | 80.6 | 50.3 | 99.7 | 76.9 | |
| GearNet-E-IE with MCPretraining Dataset=AlphaFoldDB 805K2026.06 | 80.5 | 54.1 | 99.9 | 78.2 | |
| ESM-2Pretraining Dataset=UniRef50 24M2026.06 | 78.9 | — | 99.9 | — | |
| GearNet-E-IE with DPPretraining Dataset=AlphaFoldDB 805K2026.06 | 77.8 | 51.8 | 99.6 | 76.4 | |
| SaProt-PDBPretraining Dataset=PDB 60K2026.06 | 77.8 | 52.5 | 99.6 | 76.6 | |
| CDConvInput Dimensionality=(3+1)D2026.06 | 77.7 | 56.7 | 99.6 | 78 | |
| GearNet-E-IE with APPretraining Dataset=AlphaFoldDB 805K2026.06 | 76.3 | 56.5 | 99.6 | 77.5 | |
| GearNet-E-IE with DPPretraining Dataset=AlphaFoldDB 805K2026.06 | 73.5 | 50.9 | 99.4 | 74.6 | |
| ProNet w/ MRMInput Dimensionality=(3+1)D, MRM=true2026.06 | 72.7 | 53.7 | 99.3 | 75.2 | |
| GearNet-E-IE with RTPPretraining Dataset=AlphaFoldDB 805K2026.06 | 71 | 48.8 | 99.4 | 73.1 | |
| New IEConvInput Dimensionality=(3+1)D2026.06 | 70.2 | 47.6 | 99.2 | 72.3 | |
| GearNet-Edge w/ MRMInput Dimensionality=(3+1)D, MRM=true2026.06 | 69.9 | 47.1 | 99.3 | 72.1 | |
| ProNetInput Dimensionality=(3+1)D2026.06 | 69.9 | 51.5 | 99 | 73.5 | |
| IEConvInput Dimensionality=(3+1)D2026.06 | 69.7 | 45 | 98.8 | 71.2 | |
| GearNet-EdgeInput Dimensionality=(3+1)D2026.06 | 66.7 | 44 | 99.1 | 69.9 | |
| ESM-1bPretraining Dataset=UniRef50 24M2026.06 | 60.1 | 26.8 | 97.8 | 61.6 | |
| ProtBERT-BFDPretraining Dataset=BFD 2.1B2026.06 | 55.8 | 26.6 | 97.6 | 60 | |
| GearNet w/ MRMInput Dimensionality=(3+1)D, MRM=true2026.06 | 45.5 | 31.1 | 95.8 | 57.5 | |
| 3DCNNInput Dimensionality=3D2026.06 | 45.4 | 31.6 | 92.5 | 56.5 | |
| GearNetInput Dimensionality=(3+1)D2026.06 | 42.6 | 28.4 | 95.3 | 55.4 | |
| GraphQAInput Dimensionality=(3+1)D2026.06 | 32.5 | 23.7 | 84.4 | 46.9 | |
| GVPInput Dimensionality=(3+1)D2026.06 | 22.5 | 16 | 83.8 | 40.8 | |
| GCNInput Dimensionality=3D2026.06 | 21.3 | 16.8 | 82.8 | 40.3 | |
| DeepFRIPretraining Dataset=Pfam 10M2026.06 | 20.6 | 15.3 | 73.2 | 36.4 | |
| GATInput Dimensionality=3D2026.06 | 16.5 | 12.4 | 72.7 | 33.9 | |
| CNNInput Dimensionality=1D2026.06 | 13.4 | 11.3 | 53.4 | 26 | |
| TransformerInput Dimensionality=1D2026.06 | 8.81 | 9.22 | 40.4 | 19.5 | |
| ResNetInput Dimensionality=1D2026.06 | 7.21 | 10.1 | 23.5 | 13.6 | |
| LSTMInput Dimensionality=1D2026.06 | 4.33 | 6.41 | 18.1 | 9.6 |