Mathematical modeling and estimation of physicochemical characteristics of pneumonia treatment drugs through a novel approach K-Banhatti topological descriptors

Abstract

Introduction: Pneumonia is the primary cause of mortality in preterm infants in developing nations; yet, early detection and treatment can significantly reduce mortality rates. Pharmaceutical researchers are diligently striving to identify avariety of drugs that might effectively cure pneumonia.

Method: We are motivated to examine the quantitative structureproperty relationships (QSPR) of anti-pneumonia pharmaceuticals. We employed K-Banhatti topological descriptors and analyzed the findings to achieve this. For estimation of physicochemical properties of pneumonia treatment drugs we utilized linear, quadratic, cubic, and biquadratic regression analyses.

Results and conclusion: The drugs comprise linezolid, ceftabiprole, and clarithromycin, among others. Topological descriptors enable the exploration of the complexity, connectivity, and other essential attributes of molecules. The quantitative structure-property relationship (QSPR) analysis of pharmaceuticals for illness treatment employing K-Banhatti topological descriptors is an economical approach utilised by pharmaceutical researchers. We performed a QSPR analysis on 20 anti-pneumonia drugs to ascertain the most precise predictions for five properties: enthalpy, flash point, molecular weight, molar volume, and molar refractivity, employing five K-Banhatti indices. To do this, we used linear, quadratic, cubic, and biquadratic regression analyses to find links between molecules and the physical and chemical properties of drugs used to treat pneumonia. Employing molecular descriptors and regression models to investigate chemical patterns is a cost-effective and theoretical methodology.

Keywords: K-Banhatti descriptors; QSPR testing; anti-pneumonia drugs; chemical graph theory; molecular structure; physicochemical properties; regression models; topological descriptors.

FIGURE 1

Pneumonia in children.

FIGURE 2

Chemical structures of anti-pneumonia drugs (a) linezolid (b) tetracycline (c) tazobactam (d) amoxicillin (e) cefaclor (f) ceftriaxone (g) avibactam (h) lefamulin (i) levaquine (j) clarithromycin (k) cefpodoxime (l) doxycycline (m) omadacycline (n) penicillin (o) cefuroxime (p) carbapenem (q) unasyn (r) erythromycin (s) ceftabiprole (t) moxifloxacin.

FIGURE 3

$\mathbf{L}\mathbf{R}$ Models of Enthalpy of Vaporization for Pneumonia Treatment Drugs.

FIGURE 4

$\mathbf{L}\mathbf{R}$ models of molar refraction and molar volume for pneumonia treatment drugs.

FIGURE 5

LR models of flash point and molar weight for pneumonia treatment drugs.

FIGURE 6

$\mathbf{Q}\mathbf{R}$ models of enthalpy of vaporization for pneumonia treatment drugs.

FIGURE 7

QR models of molar refraction and molar volume for pneumonia treatment drugs.

FIGURE 8

QR models of flash point and molar weight for pneumonia treatment drugs.

FIGURE 9

CR models of enthalpy of vapourization for pneumonia treatment drugs.

FIGURE 10

CR models of molar refraction and molar volume for pneumonia treatment drugs.

FIGURE 11

CR models of flash point and molar weight for pneumonia treatment drugs.

FIGURE 12

$\mathbf{B}\mathbf{Q}\mathbf{R}$ models of enthalpy of vaporization for pneumonia treatment drugs.

FIGURE 13

$\mathbf{B}\mathbf{Q}\mathbf{R}$ models of molar refraction and molar volume for pneumonia treatment drugs.

FIGURE 14

$\mathbf{B}\mathbf{Q}\mathbf{R}$ models of flash point and molar weight for pneumonia treatment drugs.