Chemical Properties Of Compounds Research Articles

Multicriteria decision making attributes and estimation of physicochemical properties of kidney cancer drugs via topological descriptors.

Based on topological descriptors, QSPR analysis is an incredibly helpful statistical method for examining many physical and chemical properties of compounds without demanding costly and time-consuming laboratory tests. Firstly, we discuss and provide research on kidney cancer drugs using topological indices and done partition of the edges of kidney cancer drugs which are based on the degree. Secondly, we examine the attributes of nineteen drugs casodex, eligard, mitoxanrone, rubraca, and zoladex, etc and among others, using linear QSPR model. The study in the article not only demonstrates a good correlation between TIs and physical characteristics with the QSPR model being the most suitable for predicting complexity, enthalpy, molar refractivity, and other factors and a best-fit model is attained in this study. This theoretical approach might benefit chemists and professionals in the pharmaceutical industry to forecast the characteristics of kidney cancer therapies. This leads towards new opportunities to paved the way for drug discovery and the formation of efficient and suitable treatment options in therapeutic targeting. We also employed multicriteria decision making techniques like COPRAS and PROMETHEE-II for ranking of said disease treatment drugs and physicochemical characteristics.

Evaluation of Various Topological Indices of Flabellum Graphs

Graph theory serves as an engaging arena for the investigation of proof methods within the field of discrete mathematics, and its findings find practical utility in numerous scientific domains. Chemical graph theory is a specialized branch of mathematics that uses graphs to represent and analyze the structure and properties of chemical compounds. Topological indices are mathematical properties of graphs that play a crucial role in chemistry. They provide a unique way to connect the structural characteristics of chemical compounds to their corresponding molecular graphs. The flabellum graph Fn(k,j) is obtained with the help of k≥2 duplicates of the cycle graph Cn with a common vertex (known as, central vertex). Then, in j of these duplicates, additional edges are added, joining the central vertex to all non-adjacent vertices. In this article, we compute different degree-based topological indices for flabellum graphs, including some well known indices, such as the Randić index, the atom bond connectivity index, the geometric–arithmetic index, and the Zagreb indices. This research provides an in-depth examination of these specific indices within the context of flabellum graphs. Moreover, the behavior of these indices is shown graphically, in terms of the parameters j,k, and n. Additionally, we have extended the concept of the first Zagreb index, to address the issue of cybercrime. This application enables us to identify criminals who exhibit higher levels of activity and engagement in multiple criminal activities when compared to their counterparts. Furthermore, we conducted a comprehensive comparative analysis of the first Zagreb index against the closeness centrality measure. This analysis sheds light on the effectiveness and relevance of the topological index in the context of cybercrime detection and network analysis.

DETERMINATION OF THE IONIZATION CONSTANT OF FAVIPIRAVIR API

One of the aspects of studying the physical and chemical properties of compounds is the establishment of their ionization constants. These indicators are extremely important from both a theoretical and a practical point of view. Theoretical analysis of proteolytic equilibria and potentiometric titration of an aqueous solution of an oral antiviral drug approved for the treatment of influenza in Japan, AFI favipiravir, were carried out in the paper. The presence of a hydroxy group in the third position of the pyrazine cycle in the molecule determines the ability of favipiravir to exhibit acid-base properties. According to acid-base titration, the ionization constant of Favipiravir API was determined: pKa = 5.05 ± 0.02. On the basis of the found pK value, the degree of formation of protolytic forms was calculated depending on the pH of the solution. It was established that favipiravir API in solutions exists in two protonated (non-ionized), tautomeric forms A1H and A2H and one deprotonated (ionized) form A-. Based on the calculations, the following conclusions can be drawn: - at a pH value of the solution from 0 to 3.5, approximately 100% of the substance will be in two protonated (non-ionized), tautomeric forms A1H and A2H; - at pH = 5, approximately 50% of the substance will be in two protonated (non-ionized), tautomeric forms, A1H and A2H; - at a pH value > 6.5, approximately 100% of the substance will be in one deprotonated (ionized) form of A-. This is confirmed by the nature of the electronic absorption spectra of aqueous solutions of favipiravir. In the spectrum of favipiravir in a 0.1 M hydrochloric acid solution, there are two bands at the wavelengths of 322 nm and 365 nm, which correspond to the two tautomeric forms A1H and A2H. In the spectrum of favipiravir in 0.1 M sodium hydroxide solution, there is one band at a wavelength of 364 nm, which corresponds to one ionized form of A-. At the stage of pharmaceutical development, these data on the acid-base properties of the Favipiravir API can be used in the development of production technology, the creation of quality control methods and their validation, as well as in the study of the solubility of the API and the dissolution profiles of the finished medicinal product.

S-coindex of some graph operations and its properties and conjugated polymers

The theory of chemical graphs is a field of mathematical chemistry that applies graph theory to the mathematical modeling of chemical phenomena. Topological index is a numerical descriptor of a molecule; it is found that there is strong correlation between the properties of chemical compounds and their molecular structure based on a specific topological feature of the corresponding molecular graph. The S-index of a graph is defined as the sum of five of the degree of vertices of a graph. In this paper, we introduce a new invariant of a graph which is identified as S-coindex. We study some basic mathematical properties and different types of graph operations like as Join, Cartesian Product, Composition, Tensor Product, Strong Product, Disjunction, Symmetric Difference, Corona Product of graphs.

On QSPR Analysis of Molecular Descriptor and Thermodynamic Features of Narcotic Drugs

The use of chemical graph theory has been increased in almost every field of science in the last two decades. The graph invariants called topological descriptor are the numbers used to understand the physical and chemical properties of compounds. The use of narcotics causes many health and social issues in society. Narcotic drug addiction results in decreased pain perception, euphoria, dizziness, physical dependence, decreased breathing, and sleepiness in people. The overuse of narcotics may cause death. For the care and treatment of drug addictions, special health institutes provide proper medicines. The structures of narcotic drugs are explored with the help of degree-based graph invariants. In this article, we used degree-based topological indices to estimate the properties of narcotic drugs. Moreover, we have obtained that the topological indices utilize the antinarcotics drugs have a very strong correlation with the physicochemical properties mentioned in this manuscript.

Computing the expected subtree number of random hexagonal and phenylene chains based on probability matrices

With structural analysis and probability matrix, this paper studies the subtree number index (number of non-labeled subtrees) of random hexagonal and phenylene chains, and presents exact recursive formulas for the expected values of subtree number index of the random hexagonal and phenylene chains in terms of probability matrices, as applications, we obtain the subtree number of hexagonal linear chain, hexagonal helicence chain, phenylene linear chain and phenylene helicence chain. Moreover, the proposed matrix method also builds a bridge to explore the deep correlation between structural-based and distance-based topological indices, and provides important theoretical support for prediction new properties of chemical compounds.

The pollution characteristics, source identification and health risks of multiple classes atmospheric SVOCs with complex emission sources of the petrochemical plant and other industries

SVOCs are ubiquitous air contaminants, whilst little was known about their environmental fates and sources in atmosphere in ambient areas around petrochemical and other industries. Herein, the levels of typical SVOCs, polycyclic aromatic hydrocarbons (PAHs), organophosphate flame retardants (OPFRs), polybrominated diphenyl ethers (PBDEs), phthalate acid esters (PAEs), and alternative plasticizers (APs), were investigated in atmosphere of residential areas around a petrochemical industry plant. The plant is bordering a developing metropolis, with highly developed modern industries and transportations. PAEs (128.7 ± 72.2 ng/m3) were found with the highest levels in atmosphere, followed by APs (19.1 ± 14.6 ng/m3), PAHs (13.6 ± 5.01 ng/m3), OPRs (3.89 ± 1.85 ng/m3), and PBDEs (0.08 ± 0.06 ng/m3). Seasonal variation was observed for SVOCs in the particulate and gaseous phases (p < 0.05), with higher concentrations of PAEs, APs, and OPFRs in summer than winter, whereas the opposite for PAHs. Chemical properties of compounds will influence the G-P partitioning of SVOC in the atmosphere. The sources of SVOCs in the study area are complex, and mainly from plastics manufacturing (20.9%), chemical additive application (46.5%), and petroleum combustion (32.9%). The estimated lifetime lung cancer risk of PAHs was moderate in the residential areas, and was higher in winter than summer. In contrast, the incremental lifetime cancer risk for di (2-ethylhexyl) phthalate inhalation was below the acceptable level. The petrochemical plant was one of the primary sources of PAHs in the investigated area, posing a nonnegligible risk to the surrounding residents. This study enhanced our understanding of the environmental fates and health risks of multiple classes of SVOCs in atmosphere with complex emission sources near the petrochemical plant and other industries.

Two Years of Life for a New Journal: Compounds

The present year marked the third year of Compounds (ISSN 2673-6918), which was born in 2021 with the aim of providing a platform for the communication of scientific achievements in the field of the synthesis, characterization, and properties of chemical compounds from both a theoretical point of view as experimental [...]

Occurrence and Risk Assessment of Atrazine and Diuron in Well and Surface Water of a Cornfield Rural Region

Herbicides have contributed to increased agricultural production. However, their residual amount can cause negative effects on environmental and public health. Therefore, this work aimed to determine the occurrence of both atrazine and diuron in surface and well water and investigate their link with drinking use. The samples were collected during dry and rainy seasons in three wells and surface water from a river and a pond located in the low plains of the Ixcatepec catchment, at the Amacuáhuitl community of the municipality of Arcelia, Guerrero State, in the center south of México, which is a rural community where farming is the main activity. The compounds were obtained by solid phase extraction and determined by HPLC-MS quadrupole with positive electrospray ionization mode. A geomorphic analysis was conducted inside the Ixcatepec catchment using the digital elevation model of the Shuttle Radar Topography Mission, SRTM-v4. The human risk for drinking water was calculated according to the Hazard Quotient. The concentrations of atrazine and diuron were between 5.77 and 402 ng L−1. Atrazine was the most abundant and frequent pesticide found with an average concentration of 105.18 ng L−1, while that of diuron was 86.56 ng L−1. The highest levels were found in pond Ushe, likely being the result of the lowest flow and stagnation of water, and during the cold-dry season a consequence of mobilization by irrigation runoff. The morphological analysis indicated that the compounds mainly reached body water located in the lower surfaces from cultivated areas. Therefore, the occurrence is mainly linked to agriculture activity within the rural community. However, chemical properties of compounds, crop irrigation, and environmental conditions could be contributing to the dispersion of residual amounts of herbicides within the hydrological system. The estimation of risk showed that atrazine can mainly generate health problems for children using the Azul well as a source of drinking water.

On Curvilinear Regression Analysis via Newly Proposed Entropies for Some Benzene Models

To avoid exorbitant and extensive laboratory experiments, QSPR analysis, based on topological descriptors, is a very constructive statistical approach for analyzing the numerous physical and chemical properties of compounds. Therefore, we presented some new entropy measures which are based on the sum of the neighborhood degree of the vertices. Firstly, we made the partition of the edges of benzene derivatives which are based on the degree sum of neighboring vertices and then computed the neighborhood version of entropies. Secondly, we made use of the software SPSS for developing a correlation between newly introduced entropies and the physicochemical properties of benzene derivatives. Our obtained results demonstrated that the critical temperature C T , critical pressure C P , and critical volume C V can be predicted through fifth geometric arithmetic entropy, second S K entropy, and fifth N D entropy, respectively. Other remaining physical characteristics include Gibb’s energy qℰ , log P , molar refractivity ℳℛ , and Henry’s law ℋℒ that can be predicted by using sixth N D entropy.

Chemical property prediction under experimental biases

Predicting the chemical properties of compounds is crucial in discovering novel materials and drugs with specific desired characteristics. Recent significant advances in machine learning technologies have enabled automatic predictive modeling from past experimental data reported in the literature. However, these datasets are often biased because of various reasons, such as experimental plans and publication decisions, and the prediction models trained using such biased datasets often suffer from over-fitting to the biased distributions and perform poorly on subsequent uses. Hence, this study focused on mitigating bias in the experimental datasets. We adopted two techniques from causal inference combined with graph neural networks that can represent molecular structures. The experimental results in four possible bias scenarios indicated that the inverse propensity scoring-based method and the counter-factual regression-based method made solid improvements.

Novel Solubility Prediction Models: Molecular Fingerprints and Physicochemical Features vs Graph Convolutional Neural Networks.

Predicting both accurate and reliable solubility values has long been a crucial but challenging task. In this work, surrogated model-based methods were developed to accurately predict the solubility of two molecules (solute and solvent) through machine learning and deep learning. The current study employed two methods: (1) converting molecules into molecular fingerprints and adding optimal physicochemical properties as descriptors and (2) using graph convolutional network (GCN) models to convert molecules into a graph representation and deal with prediction tasks. Then, two prediction tasks were conducted with each method: (1) the solubility value (regression) and (2) the solubility class (classification). The fingerprint-based method clearly demonstrates that high performance is possible by adding simple but significant physicochemical descriptors to molecular fingerprints, while the GCN method shows that it is possible to predict various properties of chemical compounds with relatively simplified features from the graph representation. The developed methodologies provide a comprehensive understanding of constructing a proper model for predicting solubility and can be employed to find suitable solutes and solvents.

The influence of the pH on the incorporation of caffeic acid into biomimetic membranes and cancer cells

Caffeic acid (CA) is a phenolic compound synthesized by all plant species. It constitutes the main hydroxycinnamic acid found in human diet and presents a variety of beneficial effects including anticancer activity. Current data suggests essential role of the interplay between anticancer drugs and the cell membrane. Given this, biophysical interactions between CA and cancer cells or biomimetic membranes were investigated. Glioblastoma cell line U118MG and colorectal adenocarcinoma cell line DLD-1, as well as lipid bilayers and liposomes, were used as in vitro models. Electrophoretic light scattering was used to assess the effect of CA on the surface charge of cancer cells and liposomal membranes. Electrochemical impedance spectroscopy was chosen to evaluate CA-dependent modulatory effect on the electrical capacitance and electrical resistance of the bilayers. Our results suggest that CA fulfills physicochemical criteria determining drug-like properties of chemical compounds, and may serve as a potential cytostatic agent in cancer treatment.

Hosoya Polynomial for Subdivided Caterpillar Graphs.

Computing Hosoya polynomial for a graph associated with a chemical compound plays a vital role in the field of chemistry. From Hosoya polynomial, it is easy to compute the Weiner index(Weiner number) and Hyper Weiner index of the underlying molecular structure. The Wiener number enables the identifying of three basic features of molecular topology: branching, cyclicity, and centricity (or centrality) and their specific patterns, which are well reflected by the physicochemical properties of chemical compounds. Caterpillar trees are used in chemical graph theory to represent the structure of benzenoid hydrocarbons molecules. In this representation, one forms a caterpillar in which each edge corresponds to a 6-carbon ring in the molecular structure, and two edges are incident at a vertex whenever the corresponding rings belong to a sequence of rings connected end-to-end in the structure. Due to the importance of Caterpillar trees, it is interesting to compute the Hosoya polynomial and the related indices. The Hosoya polynomial of a graph G is defined as H(G;x) = Σd(G)K=0 d(G.k)xk. In order to compute the Hosoya polynomial, we need to find its coefficient d(G.k) which is the number of pairs of vertices of G which are at distance k. We classify the ordered pair of vertices which are at distance , 2 ≤ m ≤ (n + 1)k in the form of sets. Then finding the cardinality of these sets and adding them up will give us the value of coefficient d(G.m) . Finally, using the values of coefficients in the definition, we get the Hosoya polynomial of uniform subdivision of caterpillar graph. In this work, we compute the closed formula of Hosoya polynomial for subdivided caterpillar trees. This helps us to compute the Weiner index and hyper-Weiner index of uniform subdivision of caterpillar graph. Caterpillar trees are among the important and general classes of trees. Thorn rods and thorn stars are the important subclasses of caterpillar trees. The idea of the present research article is to provide a road map to those researchers who are interested in studying the Hosoya polynomial for different trees.

Analysis of Distance-Based Topological Polynomials Associated with Zero-Divisor Graphs

Chemical compounds are modeled as graphs. The atoms of molecules represent the graph vertices while chemical bonds between the atoms express the edges. The topological indices representing the molecular graph corresponds to the different chemical properties of compounds. Let be are two positive integers, and be the zero-divisor graph of the commutative ring . In this article some direct questions have been answered that can be utilized latterly in different applications. This study starts with simple computations, leading to a quite complex ring theoretic problems to prove certain properties. The theory of finite commutative rings is useful due to its different applications in the fields of advanced mechanics, communication theory, cryptography, combinatorics, algorithms analysis, and engineering. In this paper we determine the distance-based topological polynomials and indices of the zero-divisor graph of the commutative ring (for as prime numbers) with the help of graphical structure analysis. The study outcomes help in understanding the fundamental relation between ring-theoretic and graph-theoretic properties of a zero-divisor graph .

VDB Entropy Measures and Irregularity-Based Indices for the Rectangular Kekulene System

Theoretical chemists are fascinated by polycyclic aromatic hydrocarbons (PAHs) because of their unique electromagnetic and other significant properties, such as superaromaticity. The study of PAHs has been steadily increasing because of their wide-ranging applications in several fields, like steel manufacturing, shale oil extraction, coal gasification, production of coke, tar distillation, and nanosciences. Topological indices (TIs) are numerical quantities that give a mathematical expression for the chemical structures. They are useful and cost-effective tools for predicting the properties of chemical compounds theoretically. Entropic network measures are a type of TIs with a broad array of applications, involving quantitative characterization of molecular structures and the investigation of some specific chemical properties of molecular graphs. Irregularity indices are numerical parameters that quantify the irregularity of a molecular graph and are used to predict some of the chemical properties, including boiling points, resistance, enthalpy of vaporization, entropy, melting points, and toxicity. This study aims to determine analytical expressions for the VDB entropy and irregularity-based indices in the rectangular Kekulene system.

The second Hyper-Zagreb index of graph operations

A graph can be recognized by numeric number, polynomial or matrix which represent the whole graph. Topological index is a numerical descriptor of a molecule, based on a certain topological feature of the corresponding molecular graph, it is found that there is a strong correlation between the properties of chemical compounds and their molecular structure. Zagreb indices are numeric numbers related to graphs. In this study, the second Hyper-Zagreb index for some special graphs, and graph operations has been computed, that have been applied to compute the second Hyper-Zagreb index for Nano-tube and Nano-torus.

Determination of absolute mobility and dissociation constant of lovastatin using capillary electrophoresis and empirical equation of ion mobility

作为一种可以预防动脉粥样硬化和冠心病的潜在药物洛伐他汀,其绝对淌度m0和解离常数pKa值的测定有助于其性质与应用的研究。在前期相关研究基础上,该文提出了一种基于毛细管区带电泳(CZE)和离子淌度经验公式测定洛伐他汀m0和pKa的新方法。首先,根据经验公式由实际淌度(mact)、有效淌度(meff)和m0之间的关系推导出m0的计算公式。对于一元酸HA,根据之前m0的计算公式,以氢离子的浓度为自变量,meff的倒数为因变量可得到一条直线。根据这条直线的斜率计算得到pKa。为了验证该方法的可行性和可靠性,应用该方法测定了巴比妥酸、苯甲酸、苄胺、苯酚、间甲酚等有机酸碱的m0和pKa值。同时,对于pH值低于6的缓冲体系,采用反向毛细管电泳技术,测定其pKa,并将测得的实验结果与理论参考值进行对比,发现两者具有较高的一致性,m0的标准偏差小于6.0%, pKa的标准偏差小于6.2%,且由线性回归方程的相关系数(R)可以看出测定pKa时的线性回归直线拟合度较好,说明该文建立的新方法具有较高的可靠性。最后基于这种CZE与经验公式结合的新方法,采用二甲基亚砜(DMSO)作为电渗流标记物测定了洛伐他汀的m0和pKa,得到的值分别为-1.70×10-8 m2/(V·s)和9.00。该方法适用于酸性和碱性分析物m0和pKa等理化参数的测定,在药物分析尤其是新药理化特性研究中具有重要意义。

On M-polynomial and some topological indices of Favipiravir (T – 705) and Ribavirin

The coronavirus disease (COVID-19) is upsetting human well-being and the economy around the globe. Scientists have been trying some antiviral drugs to control the disease and transmission of the 2019-nCoV in vitro. Two of these compounds are Favipiravir (T–705) and Ribavirin. Topological index also known as connectivity index, is a molecular descriptor which is calculated based on the molecular graph of a chemical compound. Topological indices are used to predict different biological and physicochemical properties of chemical compounds. In this article, various significant topological degree-based indices and the M-polynomials are investigated for the Favipiravir(T–705) and Ribavirin. The results obtained may aid in the designing of new medicine for the treatment and prevention of COVID-19 and other diseases.

Computing SS Index of Certain Dendrimers

The numerical descriptor gathers the data from the molecular graphs and helps to know the characteristics of the chemical structure known as topological index. The QSAR/QSPR/QSTR studies are benefited with the significant role played by topological indices in the drug design. Topological indices provide the information about the physical/chemical/biological properties of chemical compounds. The Zagreb indices are widely studied because of their extensive usage in chemical graph theory. Inspired by the earlier work on inverse sum indeg index (ISI index), novel topological index known as SS index is introduced and computed for four dendrimer structures. Also, the strong correlation coefficient between SS index and 5 physico-chemical characteristics such as boiling point (bp), molar volume (mv), molar refraction (mr), heats of vaporization (hv), and critical pressure (cp) of 67 alkane isomers have been determined. It is found that newly introduced index has shown good correlation in comparison with three most popular existing indices (ISI index and first and second Zagreb indices). In the last part, the mathematical properties of SS index are discussed.