Molecular Property Prediction On Tox21 1
Metriken
ROC-AUC
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | ROC-AUC |
|---|---|
| autogluon-tabular-robust-and-accurate-automl | 77.84 |
| n-gram-graph-a-novel-molecule-representation | 74.3 |
| uni-mol-a-universal-3d-molecular | 79.6 |
| galactica-a-large-language-model-for-science-1 | 54.3 |
| grover-self-supervised-message-passing | 74.3 |
| pre-training-graph-neural-networks | 78.1 |
| galactica-a-large-language-model-for-science-1 | 68.9 |
| low-data-drug-discovery-with-one-shot | 83.00 |
| galactica-a-large-language-model-for-science-1 | 79.6 |
| grover-self-supervised-message-passing | 73.5 |
| galactica-a-large-language-model-for-science-1 | 63.9 |
| molxpt-wrapping-molecules-with-text-for | 77.1 |
| chemrl-gem-geometry-enhanced-molecular | 78.1 |
| bioact-het-a-heterogeneous-siamese-neural | 89.80 |
| n-gram-graph-a-novel-molecule-representation | 75.8 |
| galactica-a-large-language-model-for-science-1 | 68.5 |
| pre-training-graph-neural-networks-on | 80.94±0.17 |
| are-learned-molecular-representations-ready | 75.9 |
| integrating-convolutional-layers-and-biformer | 92.4 |
| galactica-a-large-language-model-for-science-1 | 60.6 |