QSPR/QSAR study of antiviral drugs modeled as multigraphs by using TI's and MLR method to treat COVID-19 disease

Abstract

The ongoing COVID-19 pandemic continues to pose significant challenges worldwide, despite widespread vaccination. Researchers are actively exploring antiviral treatments to assess their efficacy against emerging virus variants. The aim of the study is to employ M-polynomial, neighborhood M-polynomial approach and QSPR/QSAR analysis to evaluate specific antiviral drugs including Lopinavir, Ritonavir, Arbidol, Thalidomide, Chloroquine, Hydroxychloroquine, Theaflavin and Remdesivir. Utilizing degree-based and neighborhood degree sum-based topological indices on molecular multigraphs reveals insights into the physicochemical properties of these drugs, such as polar surface area, polarizability, surface tension, boiling point, enthalpy of vaporization, flash point, molar refraction and molar volume are crucial in predicting their efficacy against viruses. These properties influence the solubility, permeability, and bio availability of the drugs, which in turn affect their ability to interact with viral targets and inhibit viral replication. In QSPR analysis, molecular multigraphs yield notable correlation coefficients exceeding those from simple graphs: molar refraction (MR) (0.9860), polarizability (P) (0.9861), surface tension (ST) (0.6086), molar volume (MV) (0.9353) using degree-based indices, and flash point (FP) (0.9781), surface tension (ST) (0.7841) using neighborhood degree sum-based indices. QSAR models, constructed through multiple linear regressions (MLR) with a backward elimination approach at a significance level of 0.05, exhibit promising predictive capabilities highlighting the significance of the biological activity $I{C}_{50}$ (Half maximal inhibitory concentration). Notably, the alignment of predicted and observed values for Remdesivir's with obs $pI{C}_{50}=6.01$ ,pred $pI{C}_{50}=6.01$ ( $pI{C}_{50}$ represents the negative logarithm of $I{C}_{50}$ ) underscores the accuracy of multigraph-based QSAR analysis. The primary objective is to showcase the valuable contribution of multigraphs to QSPR and QSAR analyses, offering crucial insights into molecular structures and antiviral properties. The integration of physicochemical applications enhances our understanding of factors influencing antiviral drug efficacy, essential for combating emerging viral strains effectively.

Keywords: Antiviral drugs; M-polynomial; Molecular multigraphs; Multiple linear regression; NM-polynomial; QSAR/QSPR.

Figure 1

Chemical structures of (a) Lopinavir, (b) Ritonavir, (c) Arbidol, (d) Thalidomide, (e) Chloroquine, (f) Hydroxy-chloroquine, (g) Theaflavin, (h) Remdesivir.

Figure 2

Molecular multigraph of Lopinavir.

Figure 3

3D-plot generation of (a) M-polynomial and (b) NM-polynomial of Lopinavir.

Figure 4

Inverse sum indeg index versus predicted boiling point.

Figure 5

Comparison chart of ‘r’ values for multigraph versus simple graph: ‘D’.

Figure 6

Neighborhood inverse sum indeg index versus predicted boiling point.

Figure 7

Comparison chart of ‘r’ values for multigraph versus simple graph: ‘NBD’.

Figure 8

Comparison between observed and predicted values of $pI{C}_{50}$.