Table 7 Comparison of our GCN models (SL-GCN and SSL-GCN) and the models constructed using the DeepChem built-in ML methods
Model | Description | Overall score | Std. | Refs. |
|---|---|---|---|---|
logreg | Logistic regression model | 0.6397 | – | [52] |
tf | Deep neural network | 0.6582 | 0.0097 | [37] |
tf-robust | Deep neural network (with bypass layers) | 0.6825 | 0.0056 | [53] |
rf | Random forest model | 0.6618 | 0.0066 | [52] |
kernelsvm | Kernel SVM model | 0.7000 | – | [52] |
graphconv | Graph convolutional model | 0.6943 | 0.0043 | [54] |
irv | Influence relevance voting (IRV) classifier | 0.6853 | – | [55] |
xgb | Xgboost classification model | 0.6908 | 0.0039 | [56] |
SL-GCN | Supervised GCN model | 0.7156 | 0.0068 | This study |
SSL-GCN | Semi-supervised GCN model | 0.7571 | 0.0084 | This study |