Linear versus cubic regression models for analyzing generalized reverse degree based topological indices of certain latest corona treatment drug molecules

Abstract

The major challenges encountered by medical researchers in developing new drugs are time consumption, increased cost, establishing a safety profile for the drugs, poor solubility, and inadequate experimental data. In its theoretical aspects, chemical graph theory plays a vital role in drug design and development by analyzing the structural parameters of molecules. Topological indices aim at the mathematical representation of a molecular structure, which is used to analyze the effectiveness of drugs and enhance the drug development process. In this study, we consider certain recently used drugs such as dexamethasone, molnupiravir, nirmatrelvir, ivermectin, ribavirin, baricitinib, favipiravir, duvelisib, L‐ascorbic acid, sofosbuvir, remdesivir, and pioglitazone for omicron, delta and other variants of coronaviruses. For these drug molecules, we propose a generalized form of reverse degree parameters and compute their associated topological indices with limiting behaviors. We undertake QSPR study on the potential of generalized reverse‐degree indices using linear and cubic regression models.

Comparative analysis of modified reverse degree topological indices for certain carbon nanosheets using entropy measures and multi criteria decision‐making analysis

“…Moreover, the maximum of degrees in graph G is represented as ΔðGÞ. Notably, in 2018, S.Ediz introduced the notion of reverse degree for a given vertex v. More recently, Arockiaraj et al proposed an generalized version of the reverse degree v [20]. The modified reverse degree of a vertex is denoted as…”

QSPR analysis of distance-based structural indices for drug compounds in tuberculosis treatment

“…Numerous degree-based indices for the TB drugs were computed with QSPR analysis in [29] and computation of polynomials in [30] , [31] . On the other side, a lot of research has been devoted to COVID-19 drugs by considering different graph theoretical parameters induced by degree and its variations [32] , [33] , [34] , [35] and for cancer drugs [36] , [37] . In this paper, we aim to obtain a QSPR model between a few distance-based topological indices including Wiener, Szeged, PI and Mostar for the above described TB drugs and their physicochemical features.…”

Predictive potential of K‐Banhatti and Zagreb type molecular descriptors in structure–property relationship analysis of some novel drug molecules

“…Some commonly used topological indices include: Wiener Index (WI), Randic Index (RI), Zagreb Indices (ZI), Resistance Distance index, and Banhatti Index (BI) [30–33]. One can consult references [21–29, 34–38] for more information on topological descriptors of different structures. The degree‐based topological indices considered in this study can be defined as follows:…”