Drug Discovery On Bace
Métriques
AUC
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | AUC | Paper Title | Repository |
|---|---|---|---|
| ContextPred | 0.845 | Strategies for Pre-training Graph Neural Networks | - |
| TrimNet | 0.878 | TrimNet: learning molecular representation from triplet messages for biomedicine | |
| Ensemble locally constant network | 0.874 | Oblique Decision Trees from Derivatives of ReLU Networks | |
| ProtoW-L2 | 0.873 | Optimal Transport Graph Neural Networks | |
| GIT-Mol(G+S) | 0.8108 | GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text | |
| elEmBERT-V1 | 0.856 | Structure to Property: Chemical Element Embeddings and a Deep Learning Approach for Accurate Prediction of Chemical Properties |
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