Table 9 Statistical parameters and regression models for ABC.

From: A graph-based computational approach for modeling physicochemical properties in drug design

Property

Models

Equations

R

\(R^2\)

\(S_{E}\)

F

p-value

BP

Linear

\(97.8098 + 23.7906 \times ABC\)

0.973

0.947

1.776

179.413

1.03236E-07

Quadratic

\(140.5329 + 17.3140 \times ABC + 0.2162 \times ABC^2\)

0.973

0.947

29.664

82.009

1.66982E-06

ENP

Linear

\(29.3842 + 2.9721 \times ABC\)

0.929

0.864

0.371

63.941

1.18225E-05

Quadratic

\(47.7947 + 0.1812 \times ABC + 0.0932 \times ABC^2\)

0.929

0.873

6.055

30.988

9.20529E-05

FP

Linear

\(16.5105 + 13.6107 \times ABC\)

0.928

0.861

1.727

62.084

1.34481E-05

Quadratic

\(134.3638 + -4.2550 \times ABC + 0.5964 \times ABC^2\)

0.928

0.877

27.290

32.295

7.82283E-05

MR

Linear

\(30.4808 + 3.0422 \times ABC\)

0.925

0.855

0.394

59.413

1.62837E-05

Quadratic

\(47.7026 + 0.4315 \times ABC + 0.0871 \times ABC^2\)

0.925

0.862

6.478

28.326

0.000130757

POL

Linear

\(12.1047 + 1.2049 \times ABC\)

0.925

0.856

0.155

59.685

1.5963E-05

Quadratic

\(18.8590 + 0.1810 \times ABC + 0.0342 \times ABC^2\)

0.925

0.863

2.560

28.430

0.000128908

MW

Linear

\(138.9778 + 11.0552 \times ABC\)

0.903

0.8160

1.659

44.361

5.6352E-05

Quadratic

\(160.5555 + 7.7842 \times ABC + 0.1092 \times ABC^2\)

0.903

0.816

27.868

20.068

0.000481673

HAC

Linear

\(1.3250 + 1.1892 \times ABC\)

0.979

0.959

0.076

238.599

2.63638E-08

Quadratic

\(-1.2836 + 1.5847 \times ABC + -0.0132 \times ABC^2\)

0.979

0.960

1.276

110.735

4.59532E-07

Com

Linear

\(-19.1974 + 26.3594 \times ABC\)

0.952

0.907

2.658

98.324

1.71745E-06

Quadratic

\(-84.2710 + 36.2241 \times ABC + -0.3293 \times ABC^2\)

0.952

0.909

44.386

45.0007

2.05922E-05

MV

Linear

\(119.3617 + 6.6106 \times ABC\)

0.846

0.716

1.315

25.259

0.000517268

Quadratic

\(96.7676 + 10.0357 \times ABC + -0.1143 \times ABC^2\)

0.846

0.718

22.048

11.486

0.00333078

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