Summary of the expert annotation ratios of three different sampling strategies on three benchmark datasetsa.
| Sampling strategy | Random | Diversity | Adversary | |
|---|---|---|---|---|
| Dataset A expert annotated (RMSE < 0.06) | DeepReac | 64.3% | 35.3% | 34.8% |
| RF | >90.0% | >90.0% | 64.3% | |
| Dataset B expert annotated (RMSE < 0.09) | DeepReac | 76.6% | 35.6% | 34.0% |
| MLP | >90.0% | >90.0% | 88.8% | |
| Dataset C expert annotated (MAE < 0.15) | DeepReac | >90.0% | 55.5% | 50.9% |
| SVM | >90.0% | >90.0% | 64.8% | |
a
The best results are given in bold. The criteria of model performance on the three benchmark datasets are shown in parentheses. RMSE, root-mean-square error. MAE, mean absolute error, in kcal mol−1. RF, random forest. MLP, multilayer perceptron. SVM, support vector machine.