Quantitative Structure-property Relationship Analysis Research Articles

On topological indices and QSPR analysis of some drugs used for treating Coronavirus patients

A topological index is a real number obtained from the molecular graph structure. It can predict the physicochemical and biological properties of many anticoronavirus drugs. In this article, the topological index of some drugs for the treatment of coronavirus was computed using the Wiener-Hosoya eigenvalues of the molecular structure of hydroxychloroquine, chloroquine, camostat, nafamostat, remdesivir, and lopinavir compounds, and the Quantitative Structure Property Relationship (QSPR) study of some properties: Boiling point, Molar weight, Flash point and Enthalpy were investigated. It was observed that the regression model value r is more than 0.6 and p value shows less than 0.05.

Analyzing polycyclic aromatic hydrocarbons using topological indices and QSPR analysis to reveal molecular complexity

Polycyclic aromatic hydrocarbons (PAHs) have distinctive chemical structures and are well known for their wide range of uses and environmental relevance. This work explores the impact of these structural characteristics on eccentricity-based topological indices offering information about the arrangement of atoms within the molecules. This study uses quantitative structure-property relationship (QSPR) analysis to construct prediction models for understanding and forecasting specific PAHs including Dibenzo[e,l]Pyrene, Heptacence, Heptaphene, Naphthacene, Naphthalene, Naphto[1,2a]pyrene, Naphtho[2,3a]pyrene, perylene, Perylene, Picene, Phenanthrene, Pyrene, Tetraphene, Tribenzo[b,n,pqr]perylene, Tribenzo[a,fg,op]tetracene and Triphenylene. Furthermore, regression analysis applies to clarify the quantitative correlations between the factors under study and improves the interpretability of the data produced. The combined use of these diverse approaches advances a thorough comprehension of the mutual influence of chemical structure, topological indices and predictive modeling about PAHs.

QSPR analysis of anti‐asthmatic drugs using some new distance‐based topological indices: A comparative study

AbstractAsthma is a widespread disease that has affected more than 300 million people worldwide. There is no efficient or preventive treatment for this disease yet. Drug scientists are striving hard to discover a variety of medicines that can treat asthma effectively. Quantitative structure‐property relationship (QSPR) analysis of drugs used in the treatment of a specific disease through topological descriptors is one of the best techniques adopted by drug scientists to cut down unnecessary laboratory expenses. In this article, we have done a QSPR analysis to identify the best predictor for the six properties namely, boiling point, enthalpy, flash point, log P, molar volume, and molar refractivity of 19 different anti‐asthmatic drugs using a set of 16 distance‐based topological indices. Also, we compare our results with QSPR analysis done by using certain degree‐based indices for the chosen drugs and obtain new directions.

Structure-property modeling of coumarins and coumarin-related compounds in pharmacotherapy of cancer by employing graphical topological indices.

Coumarins, a subgroup of colorless and crystalline oxygenated heterocyclic compounds originally discovered in the plant Dipteryx odorata, were the subject of a recent study investigating their quantitative structure-activity relationship (QSAR) in cancer pharmacotherapy. This study utilized graph theoretical molecular descriptors, also known as topological indices, as a numerical representation method for the chemical structures embedded in molecular graphs. These descriptors, derived from molecular graphs, play a pivotal role in quantitative structure-property relationship (QSPR) analysis. In this paper, intercorrelation between the Balban index, connective eccentric index, eccentricity connectivity index, harmonic index, hyper Zagreb index, first path Zagreb index, second path Zagreb index, Randic index, sum connectivity index, graph energy and Laplacian energy is studied on the set of molecular graphs of coumarins. It is found that the pairs of degree-based indices are highly intercorrelated. The use of these molecular descriptors in structure-boiling point modeling was analyzed. Finally, the curve-linear regression between considered molecular descriptors with physicochemical properties of coumarins and coumarin-related compounds is obtained.

Molecular insights into anti-Alzheimer’s drugs through predictive modeling using linear regression and QSPR analysis

The purpose of this paper is to discuss the use of topological indices (TIs) to anticipate the physical and biological aspects of innovative drugs used in the treatment of Alzheimer’s disease. Degree-based topological indices are generated using edge partitioning to assess the drugs Tacrine, Donepezil, Ravistigmine, Butein, Licochalcone-A and Flavokqwain-A. Furthermore, using linear regression, a quantitative structure–property relationship (QSPR) model is developed to predict the characteristics such as boiling point (BP), flash point (FP), molar volume (MV), molecular weight, complexity and polarizability. The findings show that topological indices have the potential to be used as a tool for drugs discovery and design in the field of Alzheimer’s disease treatment.

Exploring physico-chemical properties of HIV/AIDS drugs using neighborhood topological indices of molecular graphs

In this study, we investigate the efficacy of neighborhood-degree-based topological indices in the modeling of drug properties pertinent to HIV/AIDS. By representing molecular structures as graphs, we delve deep into atom-level environments, uncovering intricate relationships between local topological attributes and theoretical characteristics. Through meticulous quantitative structure–property relationship analysis, we establish robust correlations between these indices and drug properties. This breakthrough augurs predictive insights in the realm of pharmaceutical research, reducing the need for exhaustive experimentation. Our research underscores the pivotal role played by neighborhood-degree-based topological indices in advancing drug discovery, offering a powerful tool that resonates with chemists and industry professionals. It marks a transformative step in the trajectory of pharmaceutical development, promising to redefine and enhance the future of drug design and innovation.

Implementation of multi-criteria decision making for the ranking of drugs used to treat bone-cancer

<abstract> <p>The concept of "topological index" refers to a numerical value determined by the structure of a chemical network. It serves to determine the physicochemical and biological properties of diverse medications, offering a more precise depiction of the theoretical properties of organic materials, this is achieved through the utilization of degree-based topological indices. Because of the development of resistance to existing treatments and the unpleasant side effects associated with some current drugs, the hunt for new drugs remains a priority. In drug discovery, QSPR approaches are widely used to predict, from a chemical structure, the biological activity of potential novel drugs. Researchers can prioritize compounds for synthesis and optimize them to improve potency, preference, and other desired attributes by establishing a correlation between chemical features and biological activity. Rational drug design approaches incorporate research methodologies such as quantitative structure-activity relationships (QSAR) and quantitative structure-property relationships (QSPR), along with decision-making strategies. The goal of these strategies is to improve the biological activity and physicochemical qualities of existing leads. This research includes mathematical modeling of drug mechanisms utilizing multiple-criteria decision analysis and QSPR analysis. Furthermore, using decision-making techniques, I can determine the order of production for various drugs used to treat bone cancer based on their examination using QSPR analysis and topological indices.</p> </abstract>

Decolourization and degradation of azo-dyes by the NaClO/NiOOH catalytic oxidation system

ABSTRACT Azo dyes are highly toxic and difficult to degrade. Decolourization of acid red 18 (AR18) and reactive yellow 4 (RY4) dye wastewater by the NaClO/NiO x (OH) y catalytic system was studied. The influence of reaction conditions was investigated. Their degradation mechanism was discussed by free radical scavenging experiment, and the degradation pathways were proposed. The continuous flow experiment was conducted. The results showed that the catalytic oxidation has a good decolourization effect on AR18, but it is not ideal for RY4. The continuous flow experiment of 3000 min showed that the decolourization effect was stable. The concentration of Ni2+ in the effluent from the reactor was always up to the standard, and the catalyst was not deactivated. The decolourization mechanism of the two dyes is different: AR18 is mainly decolourized by atomic oxygen/singlet oxygen, while RY4 is mainly decolourized by NaClO. The catalysts were characterized by XPS and XRD. The surface contains the largest proportion of chemisorbed oxygen. With the extension of the catalyst service time, chemisorbed oxygen decreased slightly and was converted into atomic oxygen/singlet oxygen, which was consumed in the decolourization process. Compared with the used catalysts, the β-NiOOH crystallinity of the fresh catalyst is better. The quantitative structure–property relationship analysis based on the decolourization data shows that there is a good linear correlation between the decolourization rate of azo dyes and their molecular structure descriptors. The inorganic–organic properties balance (IOB) value was determined as the main molecular descriptor that affected the decolourization of dyes.

Extension of adjacency matrix in QSPR analysis

The utilization of extended adjacency matrices to investigate various combinatorial properties of graph is one of the most recent research directions in the spectral graph theory. This work proposes a useful modification to the classical adjacency matrix, as well as associated spectral radius and energy. When compared to existing descriptors, the novel energy and Estrada index are established to be significant molecular descriptors in structure–property modelling and isomer discrimination by means of quantitative structure property relationship (QSPR) analysis. Following that, mathematical attributes of the energy are demonstrated by estimating its tight bounds with identifying extremal graphs.

On sombor indices of tetraphenylethylene, terpyridine rosettes and QSPR analysis on fluorescence properties of several aromatic hetero‐cyclic species

AbstractAromatic heterocyclic compounds have received a lot of interest due to their various important medicinal and biological applications. The broad synthetic investigation and functional usefulness of heterocyclic molecules is driving a surge in research interest. They are found in more than 90% of innovative medications and bridge the gap between biology and chemistry, where so much scientific discovery and application happens. Heterocycles are also useful in a variety of domains, including pharmaceutical chemistry, biochemistry, and others. In this article, quantitative structure‐property relationship (QSPR) models is developed using sombor indices to predict fluorescence properties of aromatic hetero‐cyclic species based on their structural features. This allows researchers to estimate the fluorescence behavior of new molecules without performing experimental measurements. As an application, we have computed the sombor indices for self‐assembled supramolecular graphs made of terpyridine (TPE) and tetraphenylethylene (TPY) molecules that are produced as rosette cycles. This form of rosettes graph is used in electrical sensors, light emitting diodes, bioimaging and photoelectric devices, and so on. Tetraphenylethylene can be used to make fluorescent probes for next‐generation sensing applications with typical induced aggregative emission behavior.

PolyID: Artificial Intelligence for Discovering Performance-Advantaged and Sustainable Polymers.

A necessary transformation for a sustainable economy is the transition from fossil-derived plastics to polymers derived from biomass and waste resources. While renewable feedstocks can enhance material performance through unique chemical moieties, probing the vast material design space by experiment alone is not practically feasible. Here, we develop a machine-learning-based tool, PolyID, to reduce the design space of renewable feedstocks to enable efficient discovery of performance-advantaged, biobased polymers. PolyID is a multioutput, graph neural network specifically designed to increase accuracy and to enable quantitative structure-property relationship (QSPR) analysis for polymers. It includes a novel domain-of-validity method that was developed and applied to demonstrate how gaps in training data can be filled to improve accuracy. The model was benchmarked with both a 20% held-out subset of the original training data and 22 experimentally synthesized polymers. A mean absolute error for the glass transition temperatures of 19.8 and 26.4 °C was achieved for the test and experimental data sets, respectively. Predictions were made on polymers composed of monomers from four databases that contain biologically accessible small molecules: MetaCyc, MINEs, KEGG, and BiGG. From 1.4 × 106 accessible biobased polymers, we identified five poly(ethylene terephthalate) (PET) analogues with predicted improvements to thermal and transport performance. Experimental validation for one of the PET analogues demonstrated a glass transition temperature between 85 and 112 °C, which is higher than PET and within the predicted range of the PolyID tool. In addition to accurate predictions, we show how the model's predictions are explainable through analysis of individual bond importance for a biobased nylon. Overall, PolyID can aid the biobased polymer practitioner to navigate the vast number of renewable polymers to discover sustainable materials with enhanced performance.

On QSPR study of energies and thermodynamic aspects of anti-malaria medicines

Malaria is a serious infection that is transmitted by mosquitoes. It is transmitted when a person is bitten by an infected Anopheles mosquito. It effects the red blood cells and also the spleen and liver size increase as the infection deepens. Anemia or jaundice could also results from malaria. In extreme circumstances, it can target the brain and cause neurological issues. 3.3 billion peoples, or over half of the world’s population, are in danger from malaria. The frequency, signs, and causes of malaria, as well as the possible treatments, are all carefully discussed in this article. Nine medications that are used to treat malaria are employed in the quantitative structure-property relationship (QSPR) analysis. For the molecular structures of the drugs under consideration in this study, which are shown as molecular graphs, five energies are determined. There is also a statistical modeling for physio-chemical attributes with energies.

A study of novel molecular descriptors and quantitative structure–property relationship analysis of blood cancer drugs

A study of novel molecular descriptors and quantitative structure–property relationship analysis of blood cancer drugs

QSPR Analysis of Diverse Drugs Using Linear Regression for Predicting Physical Properties

The study focused on QSPR (Quantitative Structure-Property Relationship) analysis using a variety of topological indices on the medications Mefloquinone, Sertraline, Niclosamide, Tizoxanide, PHA-690509, Ribavirin, Emricasan, and Sofosbuvir. Through the use of computational modeling approaches, the study sought to determine how these medications’ chemical structures relate to their individual qualities. The discovered results provided information on the quantitative correlations between structural characteristics and pharmacological qualities, allowing for better comprehension and forecast of their behavior. The results of this study make a positive contribution to the field of medication discovery and design by offering important knowledge about the structure-property correlations of these medicinal molecules. In this article, we focus on using topological indices and a linear regression model to successfully predict various pharmacological features. This method enables more effective drug discovery and development by providing insights into the connection between molecular structure and pharmacological characteristics. We can improve our comprehension of drug behavior and assist targeted drug design by utilizing topological indices and regression analysis.

Effect of Al2O3 on the structure–property relationship of sodium aluminophosphate glasses: A combined study of experiments, MD simulations, and QSPR analysis

AbstractAluminophosphate glasses have found wide applications in various fields, such as biomedical materials, optical components, sealing materials, and nuclear waste forms. In spite of their well‐investigated short‐range ordered structures, the relationship between the properties and the medium‐range structural features is far from being understood. In this paper, atomistic structures of sodium aluminophosphate (SAP) glasses were reproduced by molecular dynamics simulations. In addition, experimental methods, including Raman, differential scanning calorimetry, and synchrotron X‐ray total scattering, have been applied to characterize the structures of these glasses, together with the measurements of various glass properties, such as the density, glass transition temperature (Tg), coefficient of thermal expansion (CTE), and hardness. Moreover, the quantitative structure–property relationship (QSPR) analysis was performed to correlate the simulated glass structures with the experimentally measured properties. The simulation results reveal that the P–O–P linkages in the glass network are gradually replaced by the P–O–Al linkages with additional alumina to the compositions, which contributes to the property changes of the SAP glass systems. Meanwhile, the long chains in the SAP glasses tend to form ring structures, and the primitive rings are concentrated in the range between 4‐ and 20‐membered rings. Furthermore, QSPR analysis shows that the simulated structures have good correlations with the experimental properties, and the established structure–property model is promising in predicting certain properties of aluminophosphate glass systems.

QSPR Analysis of Some Novel Drugs Used in Blood Cancer Treatment Via Degree Based Topological Indices and Regression Models

Topological indices (TIs) have several biological applications in the treatment of blood cancer. TIs can be used to predict the efficacy of drugs in cancer treatment by providing information about the molecular structures of the drugs and their related properties. By analyzing the degree-based TIs of different drugs, researchers can identify the most effective drugs for treating specific types of blood cancer. Additionally, TIs can also be used to predict the toxicity of drugs, which is essential for developing safe and effective treatments. Furthermore, TIs can aid in the discovery and design of new drugs by providing insights into the relationship between molecular structure and biological activity. This article focuses on the applications of topological indices (TIs) in predicting the physical and biological properties of novel drugs used in the treatment of blood cancer. The drugs glasdegib, palbociclib, and daunorubicin are analyzed using degree-based topological indices computed through edge partitioning. A quantitative structure-property relationship (QSPR) model is then developed using linear regression to predict the properties such as boiling point (BP), flash point (FP), molar volume (MV), molecular weight, and complexity. The results demonstrate the potential of topological indices as a tool for drug discovery and design in the field of cancer treatment.

Curvilinear regression analysis of benzenoid hydrocarbons and computation of some reduced reverse degree based topological indices for hyaluronic acid-paclitaxel conjugates

Graph theoretical molecular descriptors alias topological indices are a convenient means for expressing in numerical form the chemical structure encoded in a molecular graph. The structure descriptors derived from molecular graphs are widely used in quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) studies. The reason for introducing new indices is to obtain predictions of target properties of considered molecules that are better than the predictions obtained using already known indices. In this paper, we apply the reduced reverse degree based indices introduced in 2021 by Vignesh et al. In the QSPR analysis, we first compute the reduced reverse degree based indices for a family of benzenoid hydrocarbon molecules and then we obtain the correlation with the Physico-chemical properties of the considered molecules. We show that all the properties taken into consideration for the benzenoid hydrocarbons can be very effectively predicted by the reduced reverse degree based indices. Also, we have compared the predictive capability of reduced reverse degree based topological descriptors against 16 existing degree based indices. Further, we compute the defined reduced reverse degree based topological indices for Hyaluronic Acid-Paclitaxel Conjugates (HAP)_{n}, n ge 1.

Topological Indices and QSPR Analysis of NSAID Drugs

We were motivated to examine the quantitative structure–property relationships (QSPR) of nonsteroidal anti-inflammatory drugs (NSAIDs) by prior work on medicines. To do this, we made use of topological indices and analyzed the data (NSAIDs). A topological index is a measure that can be used to reflect the theoretical features of chemical substances. In this piece, well-known degree-based topological indices are applied to the chemical structures of medications that are used for the management of pain. These medicines include opiates, antidepressants, and others. These medications are taken to lessen or get rid of the feeling of discomfort in the affected area. When a molecule’s chemical structure is depicted as a graph, the elements themselves are considered to be the nodes of the graph, while the boundaries that exist between them take on the role of the graph’s edges. In addition, the QSPR analysis of topological indices was investigated, and the results showed that these indices have a strong correlation with the physical properties of chemical compounds that are used in the manufacturing of painkillers. This was demonstrated by the fact that topological indices were examined.

Computational design of quinone electrolytes for redox flow batteries using high-throughput machine learning and theoretical calculations

Molecular design of redox-active materials with higher solubility and greater redox potential windows is instrumental in enhancing the performance of redox flow batteries Here we propose a computational procedure for a systematic evaluation of organic redox-active species by combining machine learning, quantum-mechanical, and classical density functional theory calculations. 1,517 small quinone molecules were generated from the building blocks of benzoquinone, naphthoquinone, and anthraquinone with different substituent groups. The physics-based methods were used to predict HOMO-LUMO gaps and solvation free energies that account for the redox potential differences and aqueous solubility, respectively. The high-throughput calculations were augmented with the quantitative structure-property relationship analyses and machine learning/graph network modeling to evaluate the materials’ overall behavior. The computational procedure was able to reproduce high-performance cathode electrolyte materials consistent with experimental observations and identify new electrolytes for RFBs by screening 100,000 di-substituted quinone molecules, the largest library of redox-active quinone molecules ever investigated. The efficient computational platform may facilitate a better understanding of the structure-function relationship of quinone molecules and advance the design and application of all-organic active materials for RFBs.

QSPR/QSAR analysis of some eccentricity based topological descriptors of antiviral drugs used in COVID-19 treatment via $ \mathscr{D}\varepsilon $- polynomials.

In the field of chemical and medical sciences, topological indices are used to study the chemical, biological, clinical, and therapeutic aspects of pharmaceuticals. The COVID-19 pandemic is largely recognized as the most life-threatening crisis confronting medical advances. Scientists have tested various antiviral drugs and discovered that they help people recover from viral infections like COVID-19. Antiviral medications, such as Arbidol, Chloroquine, Hydroxy-Chloroquine, Lopinavir, Remdesivir, Ritonavir, Thalidomide and Theaflavin, are often used to treat COVID-19. In this paper, we define Diameter Eccentricity Based vertex degree and employ it to introduce a new polynomial called $ D\varepsilon- $ Polynomial. Using the newly introduced polynomial, we derive new topological indices, namely, diameter eccentricity based and hyper diameter eccentricity based indices. In order to check the efficacy of our indices, we derive the $ D\varepsilon- $ polynomials for the eight COVID-19 drugs mentioned above. Using these polynomials, we compute our proposed topological descriptors for the eight COVID-19 drugs. We perform quantitative structure-property relationship (QSPR) analysis by identifying the best fit curvilinear/multilinear regression models based on our topological descriptors for 8 physico- chemical properties of the COVID-19 drugs. We also perform quantitative structure-activity relationship (QSAR) analysis by identifying the best fit multilinear regression model for predicting the $ IC_{50} $ values for the eight COVID-19 drugs. Our findings and models may be useful in the development of new COVID-19 medication.