Table 14 Comparison of actual and computed values of Boiling Point from regression models of TIs.

From: A python based algorithmic approach to optimize sulfonamide drugs via mathematical modeling

 

 B.P

\(M_1(G)\)

  \(M_2(G)\)

  H(G)

  F(G)

  SS(G)

  \(RezG_2(G)\)

  \(RezG_3(G)\)

Sulfadiazine

 512.6

 570.2449

 559.4764

 565.4562

 552.1285

 562.0367

 562.3215

 558.1807

Dorzolamide

 575.8

 571.8505

 573.5856

 567.8664

 573.7601

 569.9107

 570.9987

 575.9213

Meloxicam

 520.9

 582.7329

 587.6948

 587.8185

 584.3121

 588.0482

 587.9285

 586.3667

Sulphadoxine

 522.8

 572.1181

 575.0973

 566.8091

 584.0483

 570.6602

 572.0116

 578.5741

Meticrane

 549.1

 570.9585

 560.5310

 559.7074

 556.3737

 562.8563

 564.2817

 568.7919

Famotidine

 662.4

 570.9585

 560.4842

 573.5726

 557.9321

 566.9575

 565.0265

 557.1859

Debrafenib

 653.7

 579.7001

 625.9912

 633.9795

 633.9065

 632.9611

 628.3611

 619.3609

Diurill

 608.8

 570.6909

 561.9959

 561.6098

 562.4167

 561.6184

 561.8643

 562.6573

Daranide

 590.5

 570.0665

 558.9725

 552.3101

 560.5701

 554.7327

 566.0946

 562.3257

Metahydrine

 631.3

 571.8505

 572.0739

 566.8091

 573.7601

 569.5678

 570.1820

 573.4343

Sulfapyridine

 473.5

 570.2449

 559.4764

 565.4562

 552.1285

 562.0367

 562.3215

 558.1807