Quantitative Structure Property Research Articles

Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR)

This work is devoted to the investigation of dielectric permittivity which is influenced by electronic, ionic, and dipolar polarization mechanisms, contributing to the material’s capacity to store electrical energy. In this study, an extended dataset of 86 polymers was analyzed, and two quantitative structure–property relationship (QSPR) models were developed to predict dielectric permittivity. From an initial set of 1273 descriptors, the most relevant ones were selected using a genetic algorithm, and machine learning models were built using the Gradient Boosting Regressor (GBR). In contrast to Multiple Linear Regression (MLR)- and Partial Least Squares (PLS)-based models, the gradient boosting models excel in handling nonlinear relationships and multicollinearity, iteratively optimizing decision trees to improve accuracy without overfitting. The developed GBR models showed high R2 coefficients of 0.938 and 0.822, for the training and test sets, respectively. An Accumulated Local Effect (ALE) technique was applied to assess the relationship between the selected descriptors—eight for the GB_A model and six for the GB_B model, and their impact on target property. ALE analysis revealed that descriptors such as TDB09m had a strong positive effect on permittivity, while MLOGP2 showed a negative effect. These results highlight the effectiveness of the GBR approach in predicting the dielectric properties of polymers, offering improved accuracy and interpretability.

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 Quantitative Read-Across Structure-Property Relationship Modeling of the Retention Time (Log tR) of Pesticide Residues Present in Foods and Vegetables.

The retention time (log tR) of pesticidal compounds in a reverse-phase high-performance liquid chromatography (HPLC) analysis has a direct relationship with lipophilicity, which could be related to the ecotoxicity potential of the compounds. The novel quantitative read-across structure-property relationship (q-RASPR) modeling approach uses similarity-based descriptors for predictive model generation. These models have been shown to enhance external predictivity in previous studies for several end points. The current study describes the development of a q-RASPR model using experimental retention time data (log tR) in the HPLC experiments of 823 environmentally significant pesticide residues collected from a large compound database. To model the retention time (log tR) end point, 0D-2D descriptors have been used along with the read-across-derived similarity descriptors. The developed partial least squares (PLS) model was rigorously validated by various internal and external validation metrics as recommended by the Organization for Economic Co-operation and Development (OECD). The final q-RASPR model is proven to be a good fit, robust, and externally predictive (ntrain = 618, R2 = 0.82, Q2LOO = 0.81, ntest = 205, and Q2F1 = 0.84) that literally outperforms the external predictivity of the previously reported quantitative structure-property relationship (QSPR) model. From the insights of modeled descriptors, lipophilicity is found to be the most important chemical property, which positively correlates with the retention time (log tR). Various other characteristics, such as the number of multiple bonds (nBM), graph density (GD), etc., have a substantial and inversely proportionate relationship with the retention time end point. The software tools utilized in this study are user-friendly, and most of them are free, which makes our methodology quite cost-effective when compared to experimentation. In a nutshell, to obtain better external predictivity, interpretability, and transferability, q-RASPR is an efficient technique that has the potential to be employed as a good alternative approach for retention time prediction and ecotoxicity potential identification.

An Algebraic Approach to Calculate Some Topological Coindices and QSPR Analysis of Some Novel Drugs Used in the Treatment of Breast Cancer

Breast cancer is one of the most leading deadly diseases in the world whose ranks first among all oncological diseases in women and it is the second spearheading reason of cancer deadness after lung cancer in the United States. Researchers from all over the world are working for finding better ways for preventing, identifying, and treating breast cancer. Topological indices are the functions generating a numerical value from the molecular graph of compounds and they are also a useful tool to estimate the pyhsicochemical and biological properties of molecules in the quantitative structure-property (activity) relationship (QSPR/QSAR) studies. In this paper, some drugs are studied which are used for the treatment of breast cancer. CoM-polynomials based on degree are occurred for chemical graphs of these drugs and the values of some variable topological coindices are calculated via these polynomials. The results obtained by regression analysis show that the correlations of the chosen coindices with the physicochemical properties of the determined drugs are good even strong for some of them. The QSPR models are constructed using the curvilinear regression method with topological coindices.

Quantitative structure-property relationship (QSPR) framework assists in rapid mining of highly Thermostable polyimides

Thermal stability is an invaluable aspect in assessing polymer properties, especially for polyimides (PIs), which are known for their excellent heat resistance. However, empirically oriented discoveries have retarded its development. Inspired by the emerging data-driven polymer informatics, we developed a framework for quantitative structure–property relationships (QSPR) related to thermal stability (i.e., thermal decomposition temperature (Td)) of PIs. Given that the Td of the same polymer under different measurement atmospheres and weight loss rates cannot be generalized, we carefully sorted out the data and established four Td-related models, namely Td5(N2), Td10(N2), Td5(Air), and Td10(Air). All models passed a rigorous validation procedure (external validation, internal validation, and Y-random validation) and presented excellent predictability and stability. The reliability of the predicted values was ensured by the validation of the leverage method. For the same polymer, the calculated Td rises with increasing weight loss rate, showing a trend consistent with reality based on different Td-related models. Given that a weight loss of 10% in a nitrogen environment is commonly adopted as an evaluation criterion for Td, we selected the Td10(N2) model for high-throughput screening of nearly 3000 PIs. Thermostable candidates of interest in different fields were presented, aided by the Tg model, to inspire future new PIs design and to accelerate the polymer informatics process.

Novel Random Forest Ensemble Modeling Strategy Combined with Quantitative Structure-Property Relationship for Density Prediction of Energetic Materials.

With the further development of the concept of green chemistry, the new generation of energetic materials tends to exhibit detonation properties such as higher insensitivity, higher density, and higher energy. Therefore, the precise molecular design and green and efficient synthesis of energetic materials will be one of the serious challenges. For the purpose of accurate prediction of detonation performance of energetic materials, an ensemble modeling strategy based on the combination of Monte Carlo (MC) and variable importance measurement (VIM) improved random forest (RF) and quantitative structure-property relationship (QSPR) is proposed, which was successfully used for density prediction of energetic materials. First, the structure of 162 energetic compounds was optimized by Gaussian software, and the molecular descriptor data were calculated by CODESSA software based on the optimized molecular structure. Then, the MCVIMRF_Med ensemble model was constructed on the basis of the above molecular descriptor data and the corresponding energetic compound density index. The joint X-Y distance algorithm (SPXY) is used to partition the data set. And then, MC is used to further divide the calibration set data into multiple subsets for the construction of the ensemble model. The subset size and the number of iterations of the MCVIMRF_Med ensemble model were optimized through MC cross validation. The final output strategy of the ensemble model is optimized based on the optimized parameters, and an output optimization method based on median screening is proposed and successfully applied for the prediction performance optimization of the MCVIMRF_Med ensemble model. To further investigate the performance of the MCVIMRF_Med ensemble model, the performance of it was compared with partial least squares, RF, VIMRF, and MCVIMRF calibration models. It shows that the MCVIMRF_Med ensemble model can achieve a better prediction result for the density of energetic materials, with R 2 CV of 0.9596, RMSECV of 0.0437 g/cm3, R 2 P of 0.9768, RMSEP of 0.0578 g/cm3, and relative analysis deviation of prediction set of 3.951. Therefore, the MCVIMRF_Med ensemble modeling strategy combined with QSPR is an effective approach for the density prediction of energetic materials. This work is expected to provide new research ideas and technical support for accurate prediction of detonation performance of energetic materials.

FERMAT ECCENTRIC DISTANCE SUM OF EXTENDED VICSEK NETWORKS

In this paper, we study Vicsek polygons and extended Vicsek networks, which are an extension of Vicsek fractal. Our research indices are some Fermat-type indices, including the Fermat eccentricity and the Fermat eccentric distance sum. Fermat-type indices are novel graph invariants with vast potential in research on structure–activity and quantitative structure–property. By the approach of finite pattern, we solve some integrals to gain their asymptotic formulas on Fermat eccentricity and Fermat eccentric distance sum.

Machine learning-based q-RASPR predictions of detonation heat for nitrogen-containing compounds

The study aims to predict the detonation heat of different classes of nitrogen-containing compounds by utilizing various in silico approaches such as QSPR, Read-across, q-RASPR, and ML.

Machine Learning-Based Quantitative Structure-Property Relationships for the Electronic Properties of Cyano Polycyclic Aromatic Hydrocarbons.

In this study, quantitative structure-property relationships (QSPR) based on a machine learning (ML) methodology and the truncated degree of π-orbital overlap (DPO) to predict the electronic properties, namely, the bandgaps, electron affinities, and ionization potentials of the cyano polycyclic aromatic hydrocarbon (CN-PAH) chemical class were developed. The level of theory B3LYP/6-31+G(d) of density functional theory (DFT) was used to calculate a total of 926 data points for the development of the QSPR model. To include the substituents effects, a new descriptor was added to the DPO model. Consequently, the new ML-DPO model yields excellent linear correlations to predict the desired electronic properties with high accuracy to within 0.2 eV for all multi-CN-substituted PAHs and 0.1 eV for the mono-CN-substituted PAH subclass.

The Generalized Inverse Sum Indeg Index of Some Graph Operations

The study of networks and graphs carried out by topological measures performs a vital role in securing their hidden topologies. This strategy has been extremely used in biomedicine, cheminformatics and bioinformatics, where computations dependent on graph invariants have been made available to communicate the various challenging tasks. In quantitative structure–activity (QSAR) and quantitative structure–property (QSPR) relationship studies, topological invariants are brought into practical action to associate the biological and physicochemical properties and pharmacological activities of materials and chemical compounds. In these studies, the degree-based topological invariants have found a significant position among the other descriptors due to the ease of their computing process and the speed with which these computations can be performed. Thereby, assessing these invariants is one of the flourishing lines of research. The generalized form of the degree-based inverse sum indeg index has recently been introduced. Many degree-based topological invariants can be derived from the generalized form of this index. In this paper, we provided the bounds related to this index for some graph operations, including the Kronecker product, join, corona product, Cartesian product, disjunction, and symmetric difference. We also presented the exact formula of this index for the disjoint union, linking, and splicing of graphs.

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.

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.

Application of Machine Learning in Developing Quantitative Structure-Property Relationship for Electronic Properties of Polyaromatic Compounds.

The degree of π orbital overlap (DPO) model has been demonstrated to be an excellent quantitative structure–property relationship (QSPR) that can map two-dimensional structural information of polycyclic aromatic hydrocarbons (PAHs) and thienoacenes to their electronic properties, namely, band gaps, electron affinities, and ionization potentials. However, the model suffers from significant limitations that narrow its applications due to inefficient manual procedures in parameter optimization and descriptor formulation. In this work, we developed a machine learning (ML)-based method for efficiently optimizing DPO parameters and proposed a truncated DPO descriptor, which is simple enough that can be automatically extracted from simplified molecular-input line-entry system strings of PAHs and thienoacenes. Compared with the result from our previous studies, the ML-based methodology can optimize DPO parameters with four times fewer data, while it can achieve the same level of accuracy in predictions of the mentioned electronic properties to within 0.1 eV. The truncated DPO model also has similar accuracy to the full DPO model. Consequently, the ML-based DPO approach coupled with the truncated DPO model enables new possibilities for developing automatic pipelines for high-throughput screening and investigating new QSPR for new chemical classes.

Quantitative structure-property relationship for predicting the diffusion coefficient of ionic liquids

Quantitative structure–property relationship (QSPR) models can offer theoretical prediction of physicochemical properties of ionic liquids (ILs), and the exploration of molecular descriptors that reflect the structural characteristics of ILs is of primary importance for QSPR models. Here, three novel QSPR models, termed as Model I, Model II and DB∞-QSPR model, are developed to predict the diffusion coefficient of ILs by using conductor-like screening model for segment activity coefficient (COSMO-SAC) based descriptors. The established Model I matches well with the experimental data set for both cations and anions, as evidenced by the high values of the coefficient of determination (R2) (greater than 0.95) in the training set and the testing set. Model II can be used to calculate the self-diffusion coefficients of ILs with a fixed anion of [NTf2], and the corresponding R2 values are greater than 0.97 in the training set and the testing set. The mutual diffusion coefficients of ILs in water at infinite dilution (DB∞) can reliably be predicted by DB∞-QSPR model, which demonstrates excellent predictive power because of the high values of R2 (greater than 0.97) in the training set and the testing set. In addition, the value of average absolute relative deviation (AARD) between experimental and calculated DB∞ is 5.023% in the overall set. According to the afore-mentioned QSPR models, the diffusion coefficient of ILs is positively correlated with temperature and negatively correlated with the cavity volume of ion.

Computation and Analysis of Topological Co-Indices for Metal-Organic Compound.

A topological descriptor is a mathematical illustration of a molecular construction that relates particular physicochemical properties of primary molecular structure as well as its mathematical depiction. Topological co-indices are usually applied for quantitative structure action relationships (QSAR) and quantitative structure property relationships (QSPR). Topological co-indices are topological descriptor which consider the noncontiguous vertex set. In this study, we studyied some of the accompanied renowned topological co-indices: the 1st and 2nd Zagreb co-indices, the 1st and 2nd multiplicative Zagreb co-indices, and the F-coindex. By applying structure based examinations and deductions, we discuss the earlier stated co-indices of few synthetic atomic structures that are frequently used in clinical, synthetic, and material designing.

Water Solubility Trends in Ionic Liquids: The Quantitative Structure-Property Relationship Model versus Molecular Dynamics.

The knowledge of water solubility in ionic liquids (ILs) is an important property with an impact on the design of many physical and chemical processes, like the purification of organic compounds or the establishment of decontamination procedures. The development of methods to predict or establish solubility trends in ILs is, therefore, extremely relevant, as it may avoid expensive and time-consuming experimental procedures. In this work, we compare results of water solubility in ILs predicted by a quantitative structure-property relationship (QSPR) model with trends found using aggregation studies in molecular dynamics (MD) simulation results. This study was performed for ILs combining the cations 1-butyl-1-methylpyrrolidinium and 1-butyl-1-methylmorpholinium, with the anions bis(pentafluoroethylsulfonyl)imide (BETI-), trifluoromethanesulfonate (TF-), and tetrafluoroborate (BF4-). Both methods indicated that, at 298.15 K, the water solubility in ILs was almost independent of the investigated cations. However, if the IL is composed of a hydrophobic anion, a slight increase in the mixability of the IL with water may be observed if the cation can form H-bonds. The QSPR model indicated that the hydrophobic BETI- anion leads to solubilities (xH2O ∼ 0.33), approximately half of those predicted when the cations are combined with TF- and BF4- anions (xH2O ∼ 0.60). The MD results suggested that this difference is essentially related to the ability of the water molecules to interact with the anion. This interaction involves the formation of networks of molecules, where H2O is completely solvated by anions. These structures make the formation of interactions between water molecules difficult, which are responsible for their segregation from solution and, therefore, to liquid-liquid phase separation. For the investigated ILs, the MD data also suggest that the solubility trends are inversely proportional to the number of "isolated" anions relative to ···AN-H2O-AN-H2O··· networks.

The quantitative structure-property relationships for the gas-ionic liquid partition coefficient of a large variety of organic compounds in three ionic liquids

Ionic liquids (ILs) have unique properties as solvents and electrolytes, which need to be studied using innovative machine learning (ML) approaches and which allow the identification of a chemical environment that can be adapted to different applications. The gas-ionic liquid partition coefficients of organic compounds is one such application-oriented parameter for selecting both ionic liquids and organic compounds as quickly, cost-effectively, and as accurately as possible. Therefore, multiple linear regression (MLR) and random forest (RF) quantitative structure–property relationships (QSPRs) were developed for predicting the gas-ionic liquid partition coefficient (log K) of structurally variable organic solutes in the ionic liquids N-butyl-N-methylpyrrolidinium tris(pentafluoroethyl)trifluorophosphate ([BMPyrr]+[FAP]−), N-butyl-N-methylpyrrolidinium tricyanomethanide ([BMPyrr]+[C(CN)3]−) and 1-(2-methoxyethyl)-1-methylpyrrolidinium tris(pentafluoroethyl)trifluorophosphate ([MeoeMPyrr]+[FAP]−). All derived models have excellent prediction capability evidenced by high 5-fold cross-validated coefficients of determination in the range 0.88 – 0.94, complemented with other high statistical parameters. Compared to the MLR approach, the non-linear RF models statistics improved in two of three data series. Analysis of the molecular descriptors selected into MLR models revealed major solvent–solute interactions, with primary contributions from Coulomb and dipolar or hydrogen bonding interactions and followed by the descriptors that expose dispersion force related interactions. Relations to all the aforementioned solvent–solute interactions were also found in RF models descriptor interpretation. Comparison of models demonstrated that a common anion in different ILs produces a significant correlation between the data series log K values, while that of ILs with a common cation are less but still significantly correlated. The lower correlation could be attributed to varying structural differences in the corresponding ions, or the anion might have a more substantial role in determining partition properties with the organic solutes in the series.

High-Throughput and Combinatorial Approaches for the Development of Multifunctional Polymers.

High-throughput (HT) development of new multifunctional polymers is accomplished by the combination of different HT tools established in polymer sciences in the last decade. Important advances are robotic/HT synthesis of polymer libraries, the HT characterization of polymers, and the application of spatially resolved polymer library formats, explicitly microarray and gradient libraries. HT polymer synthesis enables the generation of material libraries with combinatorial design motifs. Polymer composition, molecular weight, macromolecular architecture, etc. may be varied in a systematic, fine-graded manner to obtain libraries with high chemical diversity and sufficient compositional resolution as model systems for the screening of these materials for the functions aimed. HT characterization allows a fast assessment of complementary properties, which are employed to decipher quantitative structure-properties relationships. Moreover, these methods facilitate the HT determination of important surface parameters by spatially resolved characterization methods, including time-of-flight secondary ion mass spectrometry and X-ray photoelectron spectroscopy. Here current methods for the high-throughput robotic synthesis of multifunctional polymers as well as their characterization are presented and advantages as well as present limitations are discussed.

The QSPR Studies of [SO4 (H2O)n]2−, n = 1-4, 16 Clusters through Quantum-Chemical Descriptors

The quantitative structure property relationship (QSPR) is a powerful analytical approach that can link physicochemical properties of the molecular or ionic specimens mathematically with their 3D structures. The primary strategy of this predictive molecular based approach is to obtain optimum quantitative structure-property correlations by employing specific quantum-chemical descriptors (QCDs) that can encode structural features numerically. This theoretical insight is mainly aimed at computing HOMO and LUMO Eigen values and the associated QCDs such as HOMO-LUMO energy gap ( ), Ionization potential ( IP ), Electron affinity ( EA ), Electronegativity ( c ), Chemical hardness ( h ), Chemical softness ( s ), Electronic chemical potentials ( m ), and Electrophilicity index ( ω ) for the variably sized hydrated sulfate clusters [SO 4 (H 2 O) n ] 2− , n = 1-4, & 16 separately using DFT:B3LYP hybrid functional, and predicting their most significant physical properties plus kinetic stabilities quantum mechanically. The major structure-based physical properties assessed here are electronic localization & polarizability, electronegativity, electrophilicity, degree of chemical hardness & softness, and the extent of global electron density transfer. The general results show that [SO 4 (H 2 O) 4 ] 2− has more propensity to loose electrons among the ions with hydration number n < 4, and [SO 4 (H 2 O) 16 ] 2− is the most kinetically instable ion having significant electronic polarizability among other smaller clusters sampled in this study. It is believed that this QCD based QSPR studies for the biologically most important and abundant micronutrient SO 4 2 − ions would be highly useful to understand their physiological roles.

Quantitative structure-property relationships (QSPR) of valency based topological indices with Covid-19 drugs and application

The purpose of this analysis is to establish a quantitative structure–property relationship (QSPR) between eV and ve-degree based topological descriptors and measured physicochemical parameters of phytochemicals screened against SARS-CoV-2 3CLpro. A computer-based algorithm is developed to compute the eV and ve-degree based topological indices for the considered graphs. Our study revealed that the eV-degree based Zagreb index Mev and ve-degree based first beta Zagreb index M1βve are two important topological indices that can be useful in the prediction of molecular weight and the topological polar surface area of phytochemicals. Applications to certain anticancer drug (Camptothecin-Polymer Conjugate IT-101) are presented at the end.