A study on anti-malaria drugs using degree-based topological indices through QSPR analysis

On QSAR modeling with novel degree-based indices and thermodynamics properties of eye infection therapeutics

Topological descriptors are numerical results generated from the structure of a chemical graph that are useful in identifying the physicochemical characteristics of a wide range of drugs. The introduction of molecular descriptors advances quantitative structure-property relationship research. This article focuses on the nine degree-based topological indices and the linear regression model of the eye infection drugs. We introduced two new indices, namely, the "first revised Randic index" and the "second revised Randic index, for the analysis of eye infection drugs. Topological indices are calculated by using edge partitioning, vertex degree counting, and vertex degree labeling. This analysis is done with a scientific calculator and then authenticated with Matlab, a potent tool for examining data. The experimental data and results of the topological indices serve as inputs for the statistical computations and provide the values of intercepts, slopes, and correlation coefficients. All the correlations for the eye-infection drugs are positive, indicating a direct relationship between the experimental and estimated results of the drugs. There are significant results of the p-test for all of the characteristics of eye infection, such as molecular weight, boiling point, enthalpy, flash point, molar refraction, and molar volume, that validate the accuracy of the computations. A significant link was determined in this study between the defined indices with two properties: molar weight and molar refraction. The molar weight and molar refraction have a correlation coefficient ranging from 0.9. These results demonstrate a strong association between the indices and the properties under investigation. The linear regression approach is a valuable tool for chemists and pharmacists to obtain data about different medicines quickly and cost-effectively.

On quantitative structure-property relationship (QSPR) analysis of physicochemical properties and anti-hepatitis prescription drugs using a linear regression model

Numerous studies demonstrate a strong intrinsic relationship between the boiling and melting temperatures, among other chemical properties, of chemical compounds and pharmaceutical and their molecular structures. Using topological indices, researchers can learn more about the physical traits, chemical stability, and bioactivities of these chemical molecular structures. Topological indices on the chemical structure of chemical materials and drugs are investigated in order to make up for the absence of chemical experiments and provide a theoretical basis for the manufacture of medications and chemical materials. According to well-known degree-based topological indices, the chemical structures of drugs used to treat hepatitis (A, B, C, D, and E) are assessed in this study. The atoms are thought of as the vertices of a graph, and the borders that separate them are thought of as the edges. Using degree-based topological indices, a quantitative structure-property relationship (QSPR) investigation was conducted to predict the physical properties of 16 hepatitis medications. These topological indices link the chemical structure to specific physical characteristics, such as the surface tension of hepatitis medication molecules and molecular weight, enthalpy, boiling point, density, vapor pressure, and logP. Using their molecular structures, the study's drugs are represented as molecular graphs, and 14 topological indices are computed.

QSPR Analysis of Drugs for Treatment of Schizophrenia Using Topological Indices

Schizophrenia is a chronic psychotic disorder characterized primarily by cognitive deficits. Drugs and therapies are helpful in managing the symptoms, mostly with long-term compliance. There is a pressing need to design more efficient drugs with fewer adverse effects. Solubility, metabolic stability, toxicity, permeability, and transporter effects are important parameters in the efficacy of drug design, which in turn depend upon different physical and chemical characteristics of drugs. In recent years, there has been growing interest in developing computational tools for the discovery and development of drugs for schizophrenia. Some of these methods use machine learning algorithms to predict the efficacy and side effects of the potential drugs. Other studies have used computer simulations to understand the molecular mechanisms underlying the disease and identify new targets for drug development. Topological indices are numeric quantities linked to the chemical structure of drugs and predict the properties, reactivity, and stability of drugs through the quantitative structure-property relationship (QSPR). This work is aimed at using statistical techniques to link QSPR correlating properties with connectivity indices using linear regression. The QSPR model gives quite a better estimation of the properties of drugs, such as melting point, boiling point, enthalpy, flash point, molar refractivity, refractive index, complexity, molecular weight, and refractivity. Results are validated by comparing actual values to estimated values for the drugs.

On K-Banhatti indices and entropy measure for rhodium (III) chloride via linear regression models

Rhodium (III) chloride is a metallic compound characterized by its shiny and silvery-white appearance. It possesses high reflectivity and exhibits excellent resistance to corrosion. This makes it a popular choice for applications such as plating materials in jewelry and other decorative items, imparting a lustrous and reflective surface to the coated objects. Topological indices are numerical parameters employed to characterize the topology of a molecular structure. These indices are derived from the connectivity of atoms within the molecule and serve as predictors for various molecular properties, including reactivity, stability, and solubility. On the other hand, the Shannon entropy of a graph finds extensive applications in network science. It is utilized in the analysis of diverse networks, such as social networks, biological networks, and transportation networks. The Shannon entropy allows for the characterization of a network's topology and structure, aiding in the identification of crucial nodes or structures that play significant roles in network functionality and stability. In this paper, our primary objective is to compute different K-Banhatti indices and employ them to evaluate the entropy measure of Rhodium (III) chloride R h C l 3 . Additionally, we conducted an examination through linear regression analysis involving various indices and entropies associated with Rhodium chloride. Moreover, we established a correlation between degree-based Banhatti indices and entropies via the line fit method.