Property Relationship Analysis Research Articles

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.

Exploring spectrum-based descriptors in pharmacological traits through quantitative structure property (QSPR) analysis

The study centered on Quantitative Structure Property Relationship (QSPR) analysis with a focus on various graph energies, investigating drugs like Mefloquinone, Sertraline, Niclosamide, Tizoxanide, PHA-690509, Ribavirin, Emricasan, and Sofosbuvir. Employing computational modeling techniques, the research aimed to uncover the correlations between the chemical structures of these medications and their unique properties. The results illuminated the quantitative relationships between structural characteristics and pharmacological traits, advancing our predictive capabilities. This research significantly contributes to medication discovery and design by providing essential insights into the structure-property connections of these medicinal compounds. Notably, certain spectrum-based descriptors, such as positive inertia energy, adjacency energy, arithmetic-geometric energy, first zegrab energy, and the harmonic index, exhibited strong correlation coefficients above 0.999. In contrast, well-known descriptors like the Extended adjacency, Laplacian and signless Laplacian spectral radii, and the first and second Zagreb Estrada indices showed weaker performance. The article emphasizes the application of graph energies and a linear regression model to predict pharmacological features effectively, enhancing the drug discovery process and aiding in targeted drug design by elucidating the relationship between molecular structure and pharmacological characteristics.

Predictive potential of K‐Banhatti and Zagreb type molecular descriptors in structure–property relationship analysis of some novel drug molecules

AbstractTopological indices (TIs) can be used to forecast molecules' biological activity, physicochemical features, and toxicity. The use of topological indices and regression analysis can help us better understand drug behavior and contribute to the development of tailored drugs. This study investigates the predictive potential and efficacy of K‐Banhatti and Zagreb type degree‐based topological indices in quantitative structure–property relationship (QSPR) analysis of a comprehensive set of medications used for diabetes type‐I and type‐II disease. The K‐Banhatti and Zagreb type degree‐based topological indices are computed for 14 anti‐diabetes drug molecules using edge/vertex partitioning techniques. By leveraging these topological indices, QSPR regression models are developed to predict the physicochemical properties of the understudy drugs. The results show that the values of these topological indices are highly correlated with certain physicochemical properties of the anti‐diabetes drugs. Furthermore, the comparative analysis revealed that, for all the considered properties except enthalpy of vaporization, Zagreb type indices outperform K‐Banhatti indices with high predictive ability. Hence, it can be concluded that the Zagreb type indices are the best alternatives to theoretically predict the properties of anti‐diabetes drugs. This theoretical analysis can help chemists in their right choice of the topological indices to theoretically predict the properties of anti‐diabetes drugs without going into laborious experimentation.

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.

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

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.

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.

(E)-2-styrylanthracene-9,10-dione derivatives as novel fluorescent probes: synthesis, photophysical properties and application in mitochondria imaging

Our previous work firstly reported that (E)-2-styrylanthracene-9,10-dione is a novel fluorescent core (EK01) with the ability of specific mitochondria imaging. In this effort, we mainly focused our attention on the structure-photophysical property relationship and application in cells imaging of this new fluorescent chemotype. A series of the structural derivatives (TZ series) were designed and synthesized by introducing some substituents onto the 2-styryl moiety. The structure-photophysical property relationship analysis suggested that TZ03 is an excellent fluorescent molecular building block with the property of fluorescent “turn-on” effect after the modification of acylation, and TZ07 is an excellent fluorescent dye with a series of advantages such as high fluorescence intensity (Fmax = 4049.0 in CH2Cl2, 25.80 μM), moderate molar extinction coefficients (3.77 × 103-5.93 × 103 mol−1∙L∙cm−1), strong fluorescence quantum yield (Φmax = 0.739 in CH2Cl2), large Stokes shift (99.0 nm-161.8 nm) and well biological tolerance. As a classical D-π-A structure, the ICT characteristic of TZ07 was analyzed through spectroscopy verification and DFT calculations. Furthermore, optimized compound TZ07 was successfully applied in the living cells imaging with the excellent selectivity to mitochondria in a green fluorescent form. It was also suggested that the mechanism of TZ07 targeting mitochondria is independent of mitochondrial membrane potential, but probably related to the mitochondrial complex I. These findings may provide some insights into the development of novel mitochondria-targeted fluorescent probes.

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.

High-throughput screening of metal–organic frameworks for hydrogen purification

In industry, the separation/purification of hydrogen from methane impurity is of significant importance, especially under the background of carbon dioxide mitigation towards carbon neutrality. Adsorption separation is an effective route for this application regarding energy cost and separation efficiency, for which the selection of adsorbents is the key. This work proposes a computer-aided high-throughput screening strategy for the selection of metal–organic frameworks (MOFs) for CH4/H2 separation, striving to accelerate the practice of discovering better adsorbents for this particular application. Firstly, a prescreening was conducted on 12,020 computation-ready three-dimensional MOFs based on intuitive structure and chemical analysis, shortlisting 5,096 candidates. Secondly, a molecular simulation-assisted structure–property relationship analysis was performed based on small sampled MOFs, from which two structure criteria were derived for high-performing MOFs. Based on the criteria, a screening procedure was then carried out, narrowing the candidate MOFs down to 2,408. The rigorous molecular simulation was again applied to assess the separation performance metrics of these MOFs, covering adsorption selectivity, working capacity, adsorbent performance score, regenerability and separation potential. By analysing the simulated performance indicators, top-performing MOFs were identified and a more detailed analysis of the structure-performance relationship was carried out. Additionally, the impact of several practical factors, covering humidity, operation temperature and inlet gas composition, on the performance of the selected adsorbents in practice was explored. It was observed that the robustness of MOFs at humidity depends on their hydrophilicity and preferential adsorption sites. Meanwhile, operation temperature can be manipulated to accommodate flexible production scenarios.

A new perspective on the modeling and topological characterization of H-Naphtalenic nanosheets with applications.

In the past few years, two-dimensional (2D) layered nanomaterials have greatly attracted the scientific community. Among 2D nanomaterials, the porphyrin-based Naphtalenic nanosheets have been the subject of intense research due to their utilization in photo-dynamic therapy and nanodevices. New technologies based on nanomaterials or Naphtalenic nanosheet are advantageous in overcoming the problems in conventional drug delivery like poor solubility, toxicity and poor release pattern of drugs. In chemical network theory, various molecular descriptors are used to predict the chemical properties of molecules; these molecular descriptors are found to be very useful for Quantitative Structure–Activity/ Quantitative Structure–Property (QSAR/QSPR) relationship analysis in materials engineering, chemical and pharmaceutical industries. Researchers have computed the molecular descriptors for various nanostructures; however, despite intense research, the topology of nanostructures is not yet well understood. Specially, to our knowledge, the computation of topological indices for the line graph of subdivision graph of H-Naphtalenic nanosheet has not been discussed so far, which may open new perspectives for a more accurate and reliable topological characterization of this nanosheet.In this article, we employed some important degree-based topological indices to study the chemical structure of Naphtalenic nanosheet as a chemical network for QSAR/QSPR analysis. We have computed these degree-based topological indices for the line graph of subdivision graph of H-Naphtalenic nanosheet and derived formulas for them. Based on the derived formulas, numerical results are obtained and the physical and chemical properties of the under study nanosheet are investigated.

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.

Atomistic Understanding of Ion Exchange Strengthening of Boroaluminosilicate Glasses: Insights from Molecular Dynamics Simulations and QSPR Analysis.

Ion exchange (IOX) is an effective and widely used method to enhance mechanical properties of various glass products ranging from the touch screen of consumer electronics to window shields of airplanes and spacecrafts. IOX or chemical strengthening is achieved through the creation of a compressive surface layer on the glass product. Although widely studied experimentally, the fundamental understanding of the IOX strengthening process is still limited. In this work, we have applied large-scale atomistic simulations to understand IOX-induced mechanical property changes and their relation to the glass composition and structural characteristics. Two series of borosilicate glasses are studied to elucidate the composition effect, with boron oxide for silica and alumina for silica substitutions, respectively, on the mechanical properties of different levels of K+ to Na+ ion exchanges by using molecular dynamics (MD) simulations with a set of recently developed effective partial charge potentials. The linear network dilation coefficient (LNDC), a common measure of IOX behaviors, was calculated for each of the glass compositions. Quantitative structural property relationship (QSPR) analysis based on the MD-generated structural features was used to establish the structure-property correlations of mechanical and other properties. The results show strong composition dependence of the LNDC, hence the suitability of IOX strengthening. This behavior is discussed based on glass structure features of the glasses. It was found that glass compositions with a higher amount of mixed glass formers, higher network connectivity, and less complex components tend to show higher calculated LNDC and higher surface compressive stress. MD simulations, in combination with QSPR analysis, can thus provide atomistic insights into how the glass composition and structural characteristics affect IOX behaviors.

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.

Influence of substituents of Perylenebisimides on the surface energy and wettability: A systematic structure–property relationship analysis

This paper seeks to elucidate the influence on the surface wettability of a variety of substituents located in different positions of the perylene bisimide (PBI) core (ortho versus imide) with distinct electron-donor or -withdrawing character and diverse steric demand, using for this more than 20 planar PBIs. The correlation between the polarity of the individual functional group on the PBI and the surface wettability has been addressed by means of substituent descriptors in terms of Hansch-Fujita π parameter, Hammett σmeta and σpara constants, and steric parameters (Taft-Dubois Es' and Charton υ). With these parameters, a quantitative structure–property relationship (QSPR) analysis has been performed using multivariable linear regression (MLR) fittings. The relationship of Surface Energy, determined by the static contact angle method with three different solvents, to structural properties of PBIs is described. As well, the polar and dispersive contributions have been determined. For planar PBIs, a predominant influence of the substituents in the imide position on the surface wettability has been found despite of the electronic nature and steric hindrance of the substituents simultaneously located in ortho positions. This effect is more pronounced with the longer alkyl substituents at the imide position. This study paves the way for a rational chromophore design considering the on surface behavior, which will ultimately condition the contact and thus their performance in optoelectronic devices.

Predicting the dissolution rate of borosilicate glasses using QSPR analysis based on molecular dynamics simulations

AbstractQuantitative Structure Property Relationship (QSPR) analysis based on molecular dynamics (MD) simulations is a promising approach for establishing the composition‐property relationships of glasses and other materials with complex structures. A series of 20 borosilicate, aluminosilicate, and boroaluminosilicate glasses have been modeled using MD simulations with recently developed effective potentials. Short‐ and medium‐range structures of these glasses were analyzed and, based on the structural information, QSPR analysis of the initial dissolution rates (r0) of these glasses that were measured at 90°C and pH 9 by using various structural descriptors such as percentage of bridging oxygen species, network connectivity, and average ring size. The structural descriptor, Fnet, which contains both energetic information such as single bond strength and structural information such as cation coordination number and Qn distribution, was also used. It was found that while the overall network connectivity, average ring size and Fnet provide reasonable correlations with r0 of studied glasses, Fnet gives the best correlation among the descriptors. For glasses that show incongruent dissolution, it was found that modification of glass compositions to account for preferential release of modifier cations is necessary to achieve best correlations. The findings were discussed with results of recent studies on evaluating the compositional dependence of glass dissolution behavior using the topological‐constraints‐based models.

A comprehensive analysis of RAS-effector interactions reveals interaction hotspots and new binding partners

RAS effectors specifically interact with GTP-bound RAS proteins to link extracellular signals to downstream signaling pathways. These interactions rely on two types of domains, called RAS-binding (RB) and RAS association (RA) domains, which share common structural characteristics. Although the molecular nature of RAS-effector interactions is well-studied for some proteins, most of the RA/RB-domain-containing proteins remain largely uncharacterized. Here, we searched through human proteome databases, extracting 41 RA domains in 39 proteins and 16 RB domains in 14 proteins, each of which can specifically select at least one of the 25 members in the RAS family. We next comprehensively investigated the sequence–structure–function relationship between different representatives of the RAS family, including HRAS, RRAS, RALA, RAP1B, RAP2A, RHEB1, and RIT1, with all members of RA domain family proteins (RASSFs) and the RB-domain-containing CRAF. The binding affinity for RAS-effector interactions, determined using fluorescence polarization, broadly ranged between high (0.3 μM) and very low (500 μM) affinities, raising interesting questions about the consequence of these variable binding affinities in the regulation of signaling events. Sequence and structural alignments pointed to two interaction hotspots in the RA/RB domains, consisting of an average of 19 RAS-binding residues. Moreover, we found novel interactions between RRAS1, RIT1, and RALA and RASSF7, RASSF9, and RASSF1, respectively, which were systematically explored in sequence–structure–property relationship analysis, and validated by mutational analysis. These data provide a set of distinct functional properties and putative biological roles that should now be investigated in the cellular context.