Molecular Property Prediction On Bbbp 1
評価指標
ROC-AUC
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
比較表
| モデル名 | ROC-AUC |
|---|---|
| grover-self-supervised-message-passing | 69.5 |
| uni-mol-a-universal-3d-molecular | 72.9 |
| galactica-a-large-language-model-for-science-1 | 53.5 |
| accurate-admet-prediction-with-xgboost | 90.5 |
| pushing-the-boundaries-of-molecular-property | 89.2 |
| integrating-convolutional-layers-and-biformer | 75.8 |
| therapeutics-data-commons-machine-learning | 85.5 |
| galactica-a-large-language-model-for-science-1 | 39.3 |
| n-gram-graph-a-novel-molecule-representation | 69.1 |
| chemberta-2-towards-chemical-foundation | 72.8 |
| galactica-a-large-language-model-for-science-1 | 66.1 |
| chemrl-gem-geometry-enhanced-molecular | 72.4 |
| molecular-structure-property-co-trained | 73.3 |
| pre-training-graph-neural-networks | 68.7 |
| self-guided-masked-autoencoders-for-domain | 75.0 |
| n-gram-graph-a-novel-molecule-representation | 69.7 |
| therapeutics-data-commons-machine-learning | 89.2 |
| grover-self-supervised-message-passing | 70.0 |
| selformer-molecular-representation-learning | 90.2 |
| pre-training-graph-neural-networks-on | 88.75±0.49 |
| are-learned-molecular-representations-ready | 71.0 |
| dumpling-gnn-hybrid-gnn-enables-better-adc | 96.4 |
| a-bayesian-flow-network-framework-for | 95.74 |
| molxpt-wrapping-molecules-with-text-for | 80.5 ± 0.5 |
| pushing-the-boundaries-of-molecular-property | 89.6 |
| galactica-a-large-language-model-for-science-1 | 72.9 |
| galactica-a-large-language-model-for-science-1 | 59.6 |
| galactica-a-large-language-model-for-science-1 | 60.4 |
| pushing-the-boundaries-of-molecular-property | 93.0 |