Antibody Antigen Binding Prediction On Mipe
평가 지표
AUC-PR
AUC-ROC
평가 결과
이 벤치마크에서 각 모델의 성능 결과
모델 이름 | AUC-PR | AUC-ROC | Paper Title | Repository |
|---|---|---|---|---|
| Paragraph | 0.650 | 0.927 | Paragraph—antibody paratope prediction using graph neural networks with minimal feature vectors | |
| PECAN | 0.713 | 0.915 | Learning context-aware structural representations to predict antigen and antibody binding interfaces | |
| Pesto | 0.724 | 0.856 | PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces | |
| AG-Fast-Parapred | 0.612 | 0.883 | Attentive cross-modal paratope prediction | - |
| ParaSurf | 0.781 | 0.967 | ParaSurf: A Surface-Based Deep Learning Approach for Paratope-Antigen Interaction Prediction | |
| Parapred | 0.652 | 0.868 | Parapred: antibody paratope prediction using convolutional and recurrent neural networks | |
| MIPE | 0.741 | 0.927 | Improving Paratope and Epitope Prediction by Multi-Modal Contrastive Learning and Interaction Informativeness Estimation |
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