Molecular Property Prediction On
Metriken
RMSE
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | RMSE |
|---|---|
| n-gram-graph-a-novel-molecule-representation | 0.812 |
| self-guided-masked-autoencoders-for-domain | 0.609 |
| chemberta-2-towards-chemical-foundation | 0.798 |
| grover-self-supervised-message-passing | 0.823 |
| are-learned-molecular-representations-ready | 0.683 |
| n-gram-graph-a-novel-molecule-representation | 2.072 |
| grover-self-supervised-message-passing | 0.817 |
| molecular-structure-property-co-trained | 0.706 |
| uni-mol-a-universal-3d-molecular | 0.603 |
| a-bayesian-flow-network-framework-for | 0.746 |
| pre-training-graph-neural-networks-on | 0.762±0.042 |
| chemrl-gem-geometry-enhanced-molecular | 0.66 |
| pre-training-graph-neural-networks | 0.739 |