Reaction Classification on Enzyme Reaction
89.5Reaction AccuracyCDConv w/ MRM
Evaluation results
| Method | Links | ||
|---|---|---|---|
| CDConv w/ MRMInput Dimensionality=(3+1)D, MRM=true2026.06 | 89.5 | — | |
| CDConvInput Dimensionality=(3+1)D2026.06 | 88.5 | — | |
| GearNet-Edge w/ MRMInput Dimensionality=(3+1)D, MRM=true2026.06 | 87.5 | — | |
| IEConvInput Dimensionality=(3+1)D2026.06 | 87.2 | — | |
| New IEConvInput Dimensionality=(3+1)D2026.06 | 87.2 | — | |
| ProNet w/ MRMInput Dimensionality=(3+1)D, MRM=true2026.06 | 87.2 | — | |
| GearNet-EdgeInput Dimensionality=(3+1)D2026.06 | 86.6 | — | |
| ProNetInput Dimensionality=(3+1)D2026.06 | 86 | — | |
| GearNet w/ MRMInput Dimensionality=(3+1)D, MRM=true2026.06 | 81.1 | — | |
| GearNetInput Dimensionality=(3+1)D2026.06 | 79.4 | — | |
| 3DCNNInput Dimensionality=3D2026.06 | 72.2 | — | |
| GCNInput Dimensionality=3D2026.06 | 67.3 | — | |
| GVPInput Dimensionality=(3+1)D2026.06 | 65.5 | — | |
| GraphQAInput Dimensionality=(3+1)D2026.06 | 60.8 | — | |
| GATInput Dimensionality=3D2026.06 | 55.6 | — | |
| CNNInput Dimensionality=1D2026.06 | 51.7 | — | |
| TransformerInput Dimensionality=1D2026.06 | 26.6 | — | |
| ResNetInput Dimensionality=1D2026.06 | 24.1 | — | |
| LSTMInput Dimensionality=1D2026.06 | 11 | — | |
| CDConv w/ MRMPretraining Dataset=-2026.06 | — | 89.5 | |
| DeepFRIPretraining Dataset=Pfam 10M2026.06 | — | 63.3 | |
| ESM-1bPretraining Dataset=UniRef50 24M2026.06 | — | 83.1 | |
| ESM-2Pretraining Dataset=UniRef50 24M2026.06 | — | 87.2 | |
| GearNet-E-IE with APPretraining Dataset=AlphaFoldDB 805K2026.06 | — | 86.8 | |
| GearNet-E-IE with DPPretraining Dataset=AlphaFoldDB 805K2026.06 | — | 87.5 | |
| GearNet-E-IE with DPPretraining Dataset=AlphaFoldDB 805K2026.06 | — | 87 | |
| GearNet-E-IE with MCPretraining Dataset=AlphaFoldDB 805K2026.06 | — | 87.5 | |
| GearNet-E-IE with RTPPretraining Dataset=AlphaFoldDB 805K2026.06 | — | 86.6 | |
| IEConv (Residue Level)Pretraining Dataset=PDB 476K2026.06 | — | 88.1 | |
| ProtBERT-BFDPretraining Dataset=BFD 2.1B2026.06 | — | 72.2 | |
| SaProt-PDBPretraining Dataset=PDB 60K2026.06 | — | 87.7 |