Table 10 Statistical parameters and regression models for RI(G).

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

\(74.0206 + 39.9856 \times RI\)

0.959

0.920

3.710

116.114

7.9826E-07

Quadratic

\(42.8556 + 47.2418 \times RI + -0.3795 \times RI^2\)

0.959

0.921

36.546

52.499

1.09156E-05

ENP

Linear

\(26.5630 + 4.9786 \times RI\)

0.913

0.834

0.700

50.577

3.24946E-05

Quadratic

\(30.3521 + 4.0964 \times RI + 0.0461 \times RI^2\)

0.913

0.835

6.903

22.808

0.000299283

FP

Linear

\(2.2366 + 22.9500 \times RI\)

0.917

0.842

3.136

53.532

2.55132E-05

Quadratic

\(81.9137 + 4.3986 \times RI + 0.9703 \times RI^2\)

0.917

0.848

30.338

25.273

0.000202887

MR

Linear

\(26.4929 + 5.2184 \times RI\)

0.930

0.866

0.647

64.976

1.10189E-05

Quadratic

\(50.5894 + -0.3920 \times RI + 0.2935 \times RI^2\)

0.930

0.878

6.111

32.387

7.73542E-05

POL

Linear

\(10.5236 + 2.0670 \times RI\)

0.931

0.867

0.255

65.392

1.07144E-05

Quadratic

\(20.0105 + -0.1419 \times RI + 0.1155 \times RI^2\)

0.931

0.878

2.413

32.572

7.56313E-05

MW

Linear

\(124.7720 + 18.9318 \times RI\)

0.907

0.823

2.771

46.645

4.57033E-05

Quadratic

\(150.0256 + 13.0520 \times RI + 0.3075 \times RI^2\)

0.907

0.824

27.289

21.121

0.00039877

HAC

Linear

\(-0.0096 + 2.0150 \times RI\)

0.973

0.948

0.149

182.666

9.47723E-08

Quadratic

\(-5.2643 + 3.2385 \times RI + -0.0640 \times RI^2\)

0.973

0.952

1.414

89.385

1.15549E-06

Com

Linear

\(-45.6506 + 44.3138 \times RI\)

0.939

0.882

5.107

75.262

5.75043E-06

Quadratic

\(-221.6903 + 85.3013 \times RI + -2.1439 \times RI^2\)

0.939

0.891

48.537

36.897

4.60345E-05

MV

Linear

\(108.0125 + 11.6385 \times RI\)

0.874

0.764

2.045

32.385

0.000200829

Quadratic

\(111.6924 + 10.7817 \times RI + 0.0448 \times RI^2\)

0.874

0.764

20.183

14.577

0.001503624

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