Abstract
A topological index is a real number that is obtained from a chemical graph’s structure. Determining the physiochemical and biological characteristics of a variety of medications is useful since it more accurately represents the theoretical characteristics of organic molecules. This is accomplished using degree-based topological indices. The QSPR research has improved the structural understanding of the physiochemical properties of fungicides. Thirteen fungicides are examined for some of their physiochemical properties, and a QSPR model is built using nine of the drugs’ topological indices. Here, we examine the degree to which the topological indices and physiochemical attributes are connected. To do this, we create networks connecting each of the topological indices to the properties of fungicides and computationally construct topological indices of the drugs mentioned above. According to this QSPR model, the melting point, boiling point, flash point, complexity, surface tension, etc. of fungicides are strongly connected. It was discovered that the topological indices (TIs) applied to the fungicides more accurately represent their theoretical features and show a strong correlation with their physical attributes.
1. Introduction
For many decades, fungicides have predominantly been used to control fungal-caused plant diseases that threaten human health and crop production [1]. Losses in crops reached almost one billion dollars. The pathogen (fungus) is highly aggressive under field conditions when the environmental conditions favor the disease development [2]. Currently, due to the unavailability of cultivars with complete resistance, the application of fungicides is the main recommended tool for disease control along with cultural practices [3].
There are presently nine and forty seven groups of contact fungicides with multisite and single-site modes of action, respectively. Single-site active fungicides are less toxic to nontarget organisms. Modern systemic fungicides are typified by the triazoles. This group of fungicides is still the basis of cereal disease management strategies worldwide. Their antifungal activity is based on their ability to inhibit CYP51 (lanosterol 14-demethylase), a key enzyme for sterol biosynthesis in fungi [4]. Each triazole substance may have a somewhat different effect on the metabolic process that produces sterols [5], while the outcomes—abnormal fungal growth and death—are identical in different fungi. Triazole chemicals are crucial because of their outstanding antifungal effectiveness, comparatively low risk of resistance, and long-term stability in soil and water [1]. Triazoles can be used as early infection treatments or as a preventative measure. Some triazole fungicides have antisporulant qualities. However, these are ineffective once a fungus starts to develop spores as spores have enough sterol to form germ tubes. Within the triazole family, the principal compounds are difenoconazole, fenbuconazole, tebuconazole, cyproconazole, myclobutanil, penconazole, propiconazole, tetraconazole, triadimenol, prothioconazole, triticonazole, bromuconazole, epoxiconazole, fluquinconazole, flutriafol, ipconazole, metconazole, paclobutrazol, flusilazole, bitertanol, and triadimefon [6].
Topological indices (TIs) are quantitative descriptors obtained from a chemical graph that thoroughly characterize the chemical system and are widely employed in the study on the physiochemical features of numerous drugs. The chemical graph theory makes extensive use of polynomials and TIs, which are extensively used to depict the chemical structure. Graph invariants (TIs) have recently attracted a lot of attention in studies of quantitative structure-property relationships (QSPRs) and quantitative structure-activity relationships (QSARs) and are used in a wide range of mathematical fields, including bioinformatics, mathematics, informatics, and biology. For further study on QSPR modeling on certain drugs, we encourage readers to read [7–10].
We examined some of the physiochemical characteristics of thirteen fungus therapy medications and created a QSPR model utilizing nine topological indices. For this, we compute topological indices of the drugs analytically and depict graphs relating each of these topological indices to the characteristics of fungus drugs. The melting point, boiling point, flash point, complexity, surface tension, etc. of fungus medicines are closely related according to this QSPR model.
2. Preliminaries
In drug configuration, atoms depict vertices, and the associated bonds connecting the atoms are termed as edges. Graph is thought to be simple, finite, and connected, whereas V and E in the chemical graph are referred to as vertex and the edge set, respectively. The degree of a vertex in the graph G is the number of vertices adjacent to in G is denoted by . In chemistry, the valence of a compound and the degree of a vertex in a graph are concepts that are inextricably linked [11, 12]. The inspiration for this article comes from the idea that different medications (structures) may be identified, and that when they are examined for various factors while keeping topological indices in mind, their dominance can be rated. The QSPR model has been applied for the 9 topological indices, which are given in the following.
Definition 1. The ABC index [13] is given under
Definition 2. The first degree-based TI is Randic index calculated by Milan Randic in 1975 [14] is given under
Definition 3. The sum connectivity index [15] is given under
Definition 4. The GA index [16] is given under
Definition 5. First and second Zagreb indices [17] are given under
Definition 6. Harmonic index [18] of G is given under
Definition 7. Hyper Zagreb index [12] is defined as
Definition 8. Forgotten index [16] is given under
3. Quantitative Structure Analysis and Regression Model
In this section, TIs of the fungicides are computed. The relationship between QSPR analysis and TIs suggests that the physiochemical characteristics of the fungus are highly connected. Thirteen medicines are used in the analysis. The drug edifices are exhibited in Figure 1. We implement regression analysis calculations for this study. Drug computable structure analysis of nine TIs for QSPR modeling tenacity is performed. The topological indices of the respective drugs are computed in Table 1. The ten physical properties, such as solubility in water, boiling point (BP), density, melting point (MP), molar mass, flash point (FP), topological polar surface area, heavy atom count, complexity, and refractive index, are listed in Table 2. We impose a linear model by using the following equation:
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denotes the physiochemical property of the given drug. TI stands for topological index, stands for constant, and stands for regression coefficient. MATLAB and R-language software are helpful for results. Linear models are used to analyze nine TIs of the fungicides and their properties. ChemSpider and PubChem are used to get the information given in Table 2. The 2D and 3D graphs of the medicines with TIs are given in Figures 2 and 3, respectively.
3.1. Regression Models and Statistical Parameters Comparison between TIs and Correlation Coefficient of Properties
Relation between TIs and physical properties of fungicides is successfully analyzed by imposing QSPR modeling. This sort of analysis can be useful for the model. It is eminent the value of is less than 0.05 and r is greater than 0.6. Hence it is concluded that the entire properties given in Tables 3–11 are significant. Figure 4 depicts the graph.
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3.1.1. Regression Models for ABC(G)
3.1.2. Regression Models for RA(G)
3.1.3. Regression Models for SCI(G)
3.1.4. Regression Models for GA(G)
3.1.5. Regression Models for M1(G)
3.1.6. Regression Models for HM(G)
3.1.7. Regression Models for M2(G)
3.1.8. Regression Models for F(G)
3.1.9. Regression Models for H(G)
3.2. Standard Error of Estimate (SEE), Correlation Determination, and Comparison
A measure of variation for an observation calculated around the computed regression line is said to be the standard error estimate. It examines the extent of accuracy of predictions made about the calculated regression line in Table 12.
4. Conclusions
It is noted that Randic index RA(G) provides high correlated value of heavy atom count at r = 0981. F(G) index provides maximum correlated value for molar mass r = 0.776 and complexity r = 0.788. No correlation was found between TIs and density, polar surface area, flash point, boiling point, melting point, refractive index, and solubility in water.
In this work, the TIS for fungicides were computed, and they were contrasted with a linear QSPR model. Using the data gathered in this manner, the pharmaceutical industry will be able to create new medications to discover preventative treatments for the aforementioned illness. The variety of topological indicators for these medications is strongly affected by the correlation coefficient. The results offer a technique to evaluate physiochemical features for new discoveries of other disorders and are eye-opening for researchers working on drug science in the pharmaceutical sector.
Data Availability
All the data used to support the findings of the study are included within the article.
Conflicts of Interest
The authors declare that they have no conflicts of interest.