Molecular Property Prediction On Bace 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 |
|---|---|
| self-guided-masked-autoencoders-for-domain | 84.3 |
| n-gram-graph-a-novel-molecule-representation | 77.9 |
| pre-training-graph-neural-networks-on | 86.46±0.81 |
| chemrl-gem-geometry-enhanced-molecular | 85.6 |
| grover-self-supervised-message-passing | 82.6 |
| chemberta-2-towards-chemical-foundation | 79.9 |
| uni-mol-a-universal-3d-molecular | 85.7 |
| grover-self-supervised-message-passing | 81.0 |
| galactica-a-large-language-model-for-science-1 | 57.6 |
| galactica-a-large-language-model-for-science-1 | 72.7 |
| are-learned-molecular-representations-ready | 80.9 |
| galactica-a-large-language-model-for-science-1 | 56.1 |
| molxpt-wrapping-molecules-with-text-for | 88.4 |
| galactica-a-large-language-model-for-science-1 | 61.7 |
| a-bayesian-flow-network-framework-for | 73.56 |
| molecular-structure-property-co-trained | 83.0 |
| integrating-convolutional-layers-and-biformer | 83.6 |
| galactica-a-large-language-model-for-science-1 | 58.4 |
| n-gram-graph-a-novel-molecule-representation | 79.1 |
| pre-training-graph-neural-networks | 84.5 |