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Table 8 Comparison of Q 2loo statistics of nD-QSAR methods for the property log K (CGB)† for 31 (or 30)

From: QuBiLS-MAS, open source multi-platform software for atom- and bond-based topological (2D) and chiral (2.5D) algebraic molecular descriptors computations

nD-QSAR method PCs/var. Statistical method \({\text{Q}}^{2}\) 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]
  1. Italic values indicate the results of QuBiLS-MAS approach
  2. aWhen it is applicable, specifies the number of components (PCs)
  3. b1.0 A models
  4. cCompound 31 excluded, taken as outlier, is not taken into account in the training set
  5. †Logarithm of the binding affinity to the corticosteroid-binding globulin (CBG)
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Journal of Cheminformatics

ISSN: 1758-2946

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