Drug Discovery On Tox21
Métriques
AUC
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | AUC |
|---|---|
| pre-training-graph-neural-networks | 0.781 |
| learning-graph-level-representation-for-drug | 0.854 |
| trimnet-learning-molecular-representation | 0.860 |
| relational-pooling-for-graph-representations | 0.748 |
| convolutional-networks-on-graphs-for-learning | 0.846 |
| toxicblend-virtual-screening-of-toxic | 0.862 |
| structure-to-property-chemical-element | 0.961 |
| git-mol-a-multi-modal-large-language-model | 0.759 |
| self-normalizing-neural-networks | 0.845 |
| all-smiles-vae | 0.871 |
| perforated-backpropagation-a-neuroscience | 0.885 |