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Topological indices and QSPR/QSAR analysis of some antiviral drugs being investigated for the treatment of COVID-19 patients

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WILEY
DOI: 10.1002/qua.26594

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antiviral drugs; M‐ polynomial; NM‐ polynomial; QSPR; QSAR; topological indices

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The COVID-19 pandemic caused by the SARS-CoV-2 virus has led to a global health crisis, prompting scientists to explore various medication options, including drugs used for SARS and MERS. Research on topological indices has shown a strong correlation with the physicochemical properties of potential antiviral drugs, indicating their potential efficacy in combating the virus.
The spread of novel virus SARS-CoV-2, well known as COVID-19 has become a major health issue currently which has turned up to a pandemic worldwide. The treatment recommendations are variable. Lack of appropriate medication has worsened the disease. On the basis of prior research, scientists are testing drugs based on medical therapies for SARS and MERS. Many drugs which include lopinavir, ritonavir and thalidomide are listed in the new recommendations. A topological index is a type of molecular descriptor that simply defines numerical values associated with the molecular structure of a compound that is effectively used in modeling many physicochemical properties in numerous quantitative structure-property/activity relationship (QSPR/QSAR) studies. In this study, several degree-based and neighborhood degree sum-based topological indices for several antiviral drugs were investigated by using a M-polynomial and neighborhood M-polynomial methods. In addition, a QSPR was established between the various topological indices and various physicochemical properties of these antiviral drugs along with remdesivir, chloroquine, hydroxychloroquine and theaflavin was performed in order to assess the efficacy of the calculated topological indices. The obtained results reveal that topological indices under study have strong correlation with the physicochemical characteristics of the potential antiviral drugs. A biological activity (pIC50) of these compounds were also investigated by using multiple linear regressions (MLR) analysis.

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