Quantitative Structure-property Relationship Analysis Research Articles

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.

Machine Learning with Enormous "Synthetic" Data Sets: Predicting Glass Transition Temperature of Polyimides Using Graph Convolutional Neural Networks.

In the present work, we address the problem of utilizing machine learning (ML) methods to predict the thermal properties of polymers by establishing "structure-property" relationships. Having focused on a particular class of heterocyclic polymers, namely polyimides (PIs), we developed a graph convolutional neural network (GCNN), being one of the most promising tools for working with big data, to predict the PI glass transition temperature T g as an example of the fundamental property of polymers. To train the GCNN, we propose an original methodology based on using a "transfer learning" approach with an enormous "synthetic" data set for pretraining and a small experimental data set for its fine-tuning. The "synthetic" data set contains more than 6 million combinatorically generated repeating units of PIs and theoretical values of their T g values calculated using the well-established Askadskii's quantitative structure-property relationship (QSPR) computational scheme. Additionally, an experimental data set for 214 PIs was also collected from the literature for training, fine-tuning, and validation of the GCNN. Both "synthetic" and experimental data sets are included into a PolyAskInG database (Polymer Askadskii's Intelligent Gateway). By using the PolyAskInG database, we developed GCNN which allows estimation of T g of PI with a mean absolute error (MAE) of about 20 K, which is 1.5 times lower than in the case of Askadskii QSPR analysis (33 K). To prove the efficiency and usability of the proposed GCNN architecture and training methodology for predicting polymer properties, we also employed "transfer learning" to develop alternative GCNN pretrained on proxy-characteristics taken from the popular quantum-chemical QM9 database for small compounds and fine-tuned on an experimental T g values data set from PolyAskInG database. The obtained results indicate that pretraining of GCNN on the "synthetic" polymer data set provides MAE which is almost twice as low as that in the case of using the QM9 data set in the pretraining stage (∼41 K). Furthermore, we address the questions associated with the influence of the differences in the size of the experimental and "synthetic" data sets (so-called "reality gap" problem), as well as their chemical composition on the training quality. Our results state the overall priority of using polymer data sets for developing deep neural networks, and GCNN in particular, for efficient prediction of polymer properties. Moreover, our work opens up a challenge for the theoretically supported generation of large "synthetic" data sets of polymer properties for the training of the complex ML models. The proposed methodology is rather versatile and may be generalized for predicting other properties of different polymers and copolymers synthesized through the polycondensation reaction.

On ve-Degree Irregularity Index of Graphs and Its Applications as Molecular Descriptor

Most of the molecular graphs in the area of mathematical chemistry are irregular. Therefore, irregularity measure is a crucial parameter in chemical graph theory. One such measure that has recently been proposed is the ve-degree irregularity index (irrve). Quantitative structure property relationship (QSPR) analysis explores the capability of an index to model numerous properties of molecules. We investigate the usefulness of the irrve index in predicting different physico-chemical properties by carrying out QSPR analysis. It is established that the irrve index is efficient to explain the acentric factor and boiling point of molecules with powerful accuracy. An upper bound of irrve for the class of all trees is computed with identifying extremal graphs. We noticed that the result is not correct. In this report, we provide a counter example to justify our argument and determine the correct outcome.

Quantitative Structure-Property Relationship Analysis for the Prediction of Propylene Adsorption Capacity in Pure Silicon Zeolites at Various Pressure Levels.

This work is devoted to the development of quantitative structure–property relationship (QSPR) models using various regression analyses to predict propylene (C3H6) adsorption capacity at various pressures in zeolites from a topologically diverse International Zeolite Association database. Based on univariate and multilinear regression analysis, the accessible volume and largest cavity diameter are the most crucial factors determining C3H6 uptake at high and low pressures, respectively. An artificial neural network (ANN) model with five structural descriptors is sufficient to predict C3H6 uptake at high pressures. For combined pressures, the prediction of an ANN model with pore size distribution is pleasing. The isosteric heat of adsorption (Qst) has a significant impact on the improvement of the prediction of low-pressure gas adsorption, which finely classifies zeolites into high or low C3H6 adsorbers. The conjunction of high-throughput screening and QSPR models contributes to being able to prescreen the database rapidly and accurately for top performers and perform further detailed and time-consuming computational-intensive molecular simulations on these candidates for other gas adsorption applications.

Efficient removal of polybrominated diphenyl ethers from soil washing effluent by dummy molecular imprinted adsorbents: Selectivity and mechanisms

Surfactant enhanced elution is an effective method for removing hydrophobic organic pollutants from soils. The key to the development of leaching technology is selective removal of targeted pollutants in soil washing effluent and recycling of surfactant solutions. In this study, a molecular imprinting technique was applied to selectively sorb polybrominated diphenyl ethers (PBDEs) in soil washing effluent. The novel molecular imprinted polymers (MIPs) using different template molecules were synthesized by precipitation polymerization. Adsorption behaviors and mechanisms of MIPs were studied through experiments and theoretical calculations. The results show that 4-bromo-4′-hydroxybiphenyl and toluene can be effective imprinting molecule for MIPs synthesis. The maximal adsorption capacity of selected dummy molecular imprinted polymer (D1-MIP) was 1032.36 µmol/g, and that of part molecular imprinted polymer (P-MIP) was 981.13 µmol/g. Their imprinting factors in 5 PBDEs adsorption ranged from 2.13 to 5.88, the recovery percentage of Triton X-100 can reach 99.09%, confirming the feasibility of reusing surfactant. Various PBDEs could be removed by MIPs, and Quantitative Structure Property Relationship analysis revealed that PBDEs’ molecular volume, planarity, polarity, and hydrophobicity have major influences on their adsorption performance. DFT calculation revealed that Van der Waals force and hydrogen bonding played important roles during selective adsorption. These results can provide effective theoretical guidance for surfactant enhanced soil elution in practical engineering applications.

ADMET and quantitative structure property relationship analysis of anti‐Covid drugs against omicron variant with some degree‐based topological indices

AbstractChemical graph theory is one of the important fields in mathematical chemistry which provides a useful tool called topological indices which transforms chemical structure of a molecule into a numerical value. Topological indices are used to investigate physicochemical properties, pharmaco‐kinetic properties, biological activity in quantitative structure property relationship (QSPR) and quantitative structure–activity relationship. The most life‐threatening disease the world facing is COVID‐19 and its prominent variants like alpha, beta, delta and omicron variants. The delta and omicron variants are deemed one of the most contagious strains of the SARS‐CoV‐2 virus. Since there is no exact drug for these variants, but researches are been carried out with some existing and new drugs which are effective against delta and omicron variants. Favipiravir, Baricitinib, Fluvoxamine, Nirmatrelvir and Molnupiravir are some of the drugs currently used to treat COVID‐19 omicron variant. In this paper QSPR model is designed to predict some selected physicochemical properties and ADMET properties of the above aforesaid drugs by using some degree‐based indices such as first K Banhatti index, second K Banhatti index, first K hyper Banhatti index, second K hyper Banhatti index, modified first K Banhatti index, modified second K Banhatti index and harmonic K Banhatti index via M‐Polynomial. The relationship analyses for the physicochemical and ADMET properties with the degree‐based indices are done by using the quadratic and cubic regression method respectively. The results obtained can be expanded to correlate several other properties associated with drugability of potential candidates that can further be utilized to construct a disease‐based library of drugs.

Prediction of Combined Sorbent and Catalyst Materials for SE-SMR, Using QSPR and Multitask Learning.

The process of sorption enhanced steam methane reforming (SE-SMR) is an emerging technology for the production of low carbon hydrogen. The development of a suitable catalytic material, as well as a CO2 adsorbent with high capture capacity, has slowed the upscaling of this process to date. In this study, to aid the development of a combined sorbent catalyst material (CSCM) for SE-SMR, a novel approach involving quantitative structure–property relationship analysis (QSPR) has been proposed. Through data-mining, two databases have been developed for the prediction of the last cycle capacity (gCO2/gsorbent) and methane conversion (%). Multitask learning (MTL) was applied for the prediction of CSCM properties. Patterns in the data of this study have also yielded further insights; colored scatter plots were able to show certain patterns in the input data, as well as suggestions on how to develop an optimal material. With the results from the actual vs predicted plots collated, raw materials and synthesis conditions were proposed that could lead to the development of a CSCM that has good performance with respect to both the last cycle capacity and the methane conversion.

Air-water interfacial adsorption of C4-C10 perfluorocarboxylic acids during transport in unsaturated porous media

The impact of chain length on air-water interfacial adsorption of perfluorocarboxylic acids (PFCAs) during transport in unsaturated quartz sand was investigated. Short-chain (C4-C7: PFBA, PFPeA, PFHxA, PFHpA) and long chain (C8-C10: PFOA, PFNA, PFDA) PFCAs were selected as a representative homologous series. Surface tensions were measured to characterize surface activities of the selected PFCAs. Miscible-displacement column experiments were conducted for each of the PFCAs to characterize the magnitudes of air–water interfacial adsorption under transport conditions. The transport of the long-chain PFCAs exhibited greater retardation than the short-chain PFCAs. Air–water interfacial adsorption (AWIA) was the predominant source of retention (≥63%) for the long-chain PFCAs. Conversely, AWIA contributed less to retention than did solid-phase sorption for the short-chain PFCAs, with the former contributions ranging from 4% to 40%. Direct examination of the breakthrough-curve profiles as well as mathematical-modeling results demonstrated that transport of the two longest-chain PFCAs was influenced by nonlinear AWIA, whereas that of the shorter-chain PFCAs was not. This disparate behavior is consistent with the input concentration used for the transport experiments in comparison to the respective surface activities and critical reference concentrations of the different PFCAs. Quantitative-structure/property-relationship (QSPR) analysis was applied to characterize the influence of molecular size on air-water interfacial adsorption. The logs of the air-water interfacial adsorption coefficients (Kia) determined from the transport experiments are a monotonic function of molar volume, consistent with prior QSPR analyses of surface-tension measured values. The Kia values determined from the transport experiments are very similar to those measured from surface-tension data, indicating that the transport experiments produced robust measurements of AWIA.

Quantitative structure–property relationship analysis of the spectrochemical series by employing electronic descriptors from DFT calculations

Correlations among Shimura’s parameter (dCo) from spectrochemical series for 34 ligands and electronic descriptors from density functional theory calculations are reported. Simple and multiple linear regression models were constructed. The best results were obtained with the descriptors estimated with MN15 functional, obtaining a multiple linear regression model based on a quantitative structure–property relationship (QSPR) approach, with four attributes and the statistical parameters: R 2 = 0.8543, Q2 LOO = 0.8050, and Q2 LMO = 0.7977. The four molecular descriptors are ligand field splitting (Δ), dipole moment (χC), enthalpy of the metal complex (HC) and the ligand HOMO energy (EHO_L). This model obtained for the Ni(0) complexes represents a novel approach to the spectroscopic series description based on computational tools also revising the models proposed from Werner’s description until modern recompilations carried out across the spectroscopic series developments. The applicability of the model was evaluated on cooper complexes and good prediction of the dCo values for some ligands was also obtained.

Degree‐Based Molecular Descriptors and QSPR Analysis of Breast Cancer Drugs

The disease that involves abnormal cell growth and spreads through the surrounding tissues damaging other parts of the body is cancer. Breast cancer is the most common one out of many types. Women are affected by breast cancer either by hormonal changes or genetic changes that occur in DNA. As breast cancer is a life‐threatening disease, it is necessary for further studies to gear up in fighting the deadliest disease. In this work, a detailed study is made on the occurrence, symptoms, and the drugs involved in its treatment. For this purpose, the quantitative structure‐property relationship (QSPR) analysis is made using 21 drugs used in the treatment of breast cancer. The drugs considered in the study are modelled as molecular graphs using their molecular structures, and 11 topological indices are computed. The QSPR analysis is carried out for these drugs, and conclusions are drawn based on the analysis.

Quantitative structure property relationship assisted development of Fluocinolone acetonide loaded transfersomes for targeted delivery

Fluocinolone acetonide (FA) is a corticosteroid, synthetic hydrocortisone used to reduce skin inflammation and itching in psoriasis. The present study investigate the formulation of nanotransfersomes containing FA (FA-NTs) using Quantitative Structure Property Relationship (QSPR) and Box–Behnken approach. FA-NTs containing phospholipid, edge activator and cholesterol in definite ratio were prepared by high pressure homogenization and optimized using Box–Behnken design. Based on the QSPR analysis, Phospholipon 90H was selected for nanotransfersome form along with sodium deoxycholate as edge activator. The impact of independent variables Phospholipid concentration (X1), sodium deoxycholate (X2) and number of homogenization cycles (X3) on dependent variables viz., particle size and entrapment efficiency (%EE) were studied. The optimized nanotransfersomal formulation was characterized for FTIR, XRD, DSC, particle size, zeta potential, entrapment efficiency, in-vitro release and ex-vivo permeation studies. FA-NTs batch evidenced 92 ± 4% entrapment efficiency and showed 29.9 nm particle size, 0.264 polydispersity index and −23.72 mV zeta potential. FA-NTs exhibited 68% drug release within 24 h and followed Higuchi release kinetic model (R2 = 0.9902). FA-NTs showed 22.56 μg/cm2/h transdermal flux with 12.40 fold higher than FA dispersion. Biologically, FA-NTs showed hyperproliferation inhibition of differentiated HaCaT cells as evidenced from cell viability, antiproliferative action, nitric oxide production, proinflammatory cytokine measurement, Quantitative Real time PCR and cellular uptake studies. Thus, the present study demonstrates the successful use of QSPR studies and Box–Behnken design in the preparation of nanotransfersomes for efficient delivery of FA in the management of psoriasis.

The influence of molecular structure on PFAS adsorption at air-water interfaces in electrolyte solutions

Fluid-fluid interfacial adsorption has been demonstrated to be an important retention process for per and polyfluoroalkyl substances (PFAS) in porous media with air or non-aqueous phase liquids (NAPLs) present. The objective of this study was to characterize the influence of PFAS molecular structure on air-water interfacial adsorption in electrolyte solutions. Measured and literature-reported surface-tension data sets were aggregated to generate the largest compilation of interfacial adsorption coefficients measured in aqueous solutions comprising environmentally representative ionic strengths. The surface activities and interfacial adsorption coefficients (Ki) exhibited chain length trends, with greater surface activities and larger Ki values corresponding to longer chain length. The impact of multiple-component PFAS solutions on the surface activity of a select PFAS was a function of the respective surface activities and concentrations. Quantitative structure-property relationship analysis (QSPR) employing a single molecular descriptor (molar volume) was used successfully to characterize the impact of PFAS molecular structure on air-water interfacial adsorption. A previously reported QSPR model based on PFAS data generated for deionized-water solutions was updated to include more than 60 different PFAS, comprising all head-group types and a wide variety of tail structures. The QSPR model developed for PFAS in electrolyte solution compared favorably to the model developed for deionized water. Additionally, the magnitude of ionic strength for non-zero ionic strength systems was determined to have relatively minimal impact on interfacial adsorption coefficients. The new QSPR model is therefore anticipated to be representative for a wide variety of PFAS and for a wide range of ionic compositions.