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

31/30 Steroids (all dataset)

Combined electrostatic and shape similarity matrix

6

Genetic NN

0.941

[59]

QuBiLS-MAS^c

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]

SOM^a

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-State^a,b

3

–

0.80

[71]

E-State^a,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]

CoSCoSA^a

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]

QSAR^a

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]