Table 8.
Comparison of Q2loo statistics of nD-QSAR methods for the property log K (CGB)† for 31 (or 30)
| nD-QSAR method | PCs/var. | Statistical method | loo | Equations/references |
|---|---|---|---|---|
| 31/30 Steroids (all dataset) | ||||
| Combined electrostatic and shape similarity matrix | 6 | Genetic NN | 0.941 | [59] |
| QuBiLS-MASc | 6 | MLR and GA | 0.937 | Equation 23 |
| QuBiLS-MAS | 6 | MLR and GA | 0.914 | Equation 23 |
| Hodking SM | 6 | Genetic NN | 0.903 | [59] |
| QuBiLS-MAS | 5 | MLR and GA | 0.902 | Equation 22 |
| QuBiLS-MAS | 4 | MLR and GA | 0.887 | Equation 21 |
| Fragment QS-SM | 4 | PLS | 0.886 | [60] |
| MEDV-13 | 5 | MLR and GA | 0.882 | [61] |
| MiDSASA—“template” | 2 “compounds” | – | 0.88 | [62] |
| SOMa | 3 | – | R2 0.85 | [63] |
| Tuned-QSAR | 6 | MLR and PCA | 0.842 | [64] |
| Autocorrelation vector 30 | – | – | 0.84 | [65] |
| CoMMA | 3 | PLS | 0.828 | [66] |
| QuBiLS-MAS | 3 | MLR and GA | 0.826 | Equation 20 |
| Similarity Indices (ESP MC matrix 30) | 1 | PLS | 0.820 | [65] |
| SOMFA/esp + ALPHA | – | SOR | 0.82 | [67] |
| Combined electrostatic and shape similarity matrix | 6 | MLR and GA | 0.819 | [59] |
| EEVA | 4 | PLS | 0.81 | [68] |
| SOM-4D-QSAR | 4 | SOM neural network | 0.80 | [69] |
| Charges and Properties from MEPS-AM1 | 5 | MLR | 0.80 | [70] |
| HE State/E-Statea,b | 3 | – | 0.80 | [71] |
| E-Statea,b | 3 | – | 0.79 | [71] |
| CoSA | 3 “Bins” | PLS | 0.78 | [72] |
| QSAR/E-State | 3 “atoms” | – | 0.78 | [73] |
| TQSI | 4 | MLR | 0.775 | [64] |
| EVA | 5 | PLS | 0.77 | [74] |
| CoMSA | 1 | PLS | 0.76 | [75] |
| MQSM | 5 | MLR and PCA | 0.759 | [64] |
| EVA + ALPHA | – | SOR | 0.75 | [67] |
| GRIND | – | PLS | 0.75 | [76] |
| SEAL | 3 | PLS | 0.748 | [77] |
| SOMFA/esp | 6 | PLS | 0.74 | [67] |
| CoSCoSAa | 3 | – | 0.74 | [78] |
| CoSASA | 3 “atoms” | PLS | 0.73 | [72] |
| E-State and kappa shape index | 4 | MLR | 0.72 | [79] |
| TARIS | 2 | – | 0.71 | [80] |
| MQSM | 3 | MLR | 0.705 | [64] |
| Combined electrostatic and shape similarity matrix | 5 | PLS | 0.70 | [59] |
| SAMFA-RF | – | RF | 0.69 | [81] |
| SAMFA-PLS | 4–5 | PLS | 0.69 | [81] |
| 4D-QSAR | 2 | PLS | 0.69 | [69] |
| CoMMA (ab initio) | 6 | PLS | 0.689 | [82] |
| QSARa | 3 | – | 0.68 | [83] |
| SOM-4D-QSAR | 4 | SOM Neural Network | 0.68 | [69] |
| Wagener’s (AMSP Method) | – | k-NN and FNN | 0.630 | [84] |
| SAMFA-SVM | – | SVM | 0.60 | [81] |
| ALPHA | 2 | PLS | 0.57 | [67] |
Italic values indicate the results of QuBiLS-MAS approach
aWhen it is applicable, specifies the number of components (PCs)
b1.0 A models
cCompound 31 excluded, taken as outlier, is not taken into account in the training set
†Logarithm of the binding affinity to the corticosteroid-binding globulin (CBG)