Table 6.
Prediction accuracies of hERG blockade classification models developed by different methods with the same Doddareddy's training set.
| Model | SE | SP | MCC | Q (%) | AUC |
|---|---|---|---|---|---|
| Doddareddy's test set (255/P:108, N:147) | |||||
| Conv-CapsNet | 94.4% | 89.8% | 0.835 | 91.8% | 0.940 |
| RBM-CapsNet | 91.7% | 92.5% | 0.840 | 92.2% | 0.944 |
| CNN | 87.0% | 85.0% | 0.715 | 85.9% | 0.933 |
| MLP | 82.4% | 86.4% | 0.687 | 84.7% | 0.920 |
| DBN | 72.2% | 80.8% | 0.533 | 80.8% | 0.903 |
| SVM | 90.7% | 84.4% | 0.743 | 87.1% | 0.933 |
| kNN | 69.4% | 96.6% | 0.703 | 85.1% | 0.928 |
| Logistic regression | 88.8% | 83.7% | 0.710 | 85.5% | 0.858 |
| LightGBM | 79.6% | 82.3% | 0.617 | 81.2% | 0.810 |
| Doddareddy's external validation (60/P:18, N:42) | |||||
| Conv-CapsNet | 88.9% | 71.4% | 0.554 | 76.7% | 0.806 |
| RBM-CapsNet | 94.4% | 71.4% | 0.604 | 78.7% | 0.811 |
| CNN | 94.4% | 52.4% | 0.441 | 65.0% | 0.725 |
| MLP | 88.9% | 57.1% | 0.426 | 66.7% | 0.707 |
| DBN | 88.9% | 52.4% | 0.386 | 63.3% | 0.683 |
| SVM | 88.9% | 52.4% | 0.386 | 63.3% | 0.660 |
| kNN | 77.8% | 52.4% | 0.279 | 60.0% | 0.624 |
| Logistic regression | 83.3% | 52.4% | 0.332 | 61.7% | 0.623 |
| LightGBM | 61.1% | 59.5% | 0.190 | 60.0% | 0.609 |
(TP, true positive; TN, true negative; FP, false positive; FN, false negative; SE (%), sensitivity, SE = TP/(TP + FN); SP (%), specificity, SP = TN/(TN + FP); Q (%), overall accuracy, Q = [TP + TN)/(TP + TN + FP + FN)].