Search

The topological indices provide quantitative structural characteristics of drug molecules that can be utilized to predict or establish correlations with the biological activity, physicochemical properties, and toxicity of the molecules. Such studies play a crucial role in the initial stages of drug development by aiding in the identification and optimization of potential drug candidates and providing cost-effective techniques for experimental studies. Cancer, a multifaceted disease, can originate in any part of the body due to various factors, including genetic mutations, environmental toxins, and lifestyle choices. Blood cancer, encompassing malignancies affecting the blood, bone marrow, and lymphatic systems, is the focus of this research paper. The current study investigates a comprehensive set of drugs employed in blood cancer treatment, including clofarabine, mercaptopurine, olutasidenib, glasdegib, gliteritinib, zanubrutinib, chlorambucil, ibrutinib, bosutinib, hydroxyurea, cyclophosphamide, doxorubicin, daunorubicin, ivosidenib, prednisone, busulfan, omacetaxine mepesuccinate, and asciminib. By conducting a thorough analysis of these drugs, we acquire valuable insights into their molecular properties, which are crucial for predicting their behavior and efficacy in blood cancer treatment. We have devised QSPR models by leveraging the reverse degree and entropy topological indices. Our proposed QSPR models are compared with existing degree-based models, emphasizing the superior effectiveness of our approach. [ABSTRACT FROM AUTHOR]

In this study, various beta‐blocker drugs used for heart disease were analyzed, and their degree‐based topological indices derived from the M‐polynomial were calculated. Linear and quadratic regression analysis was used to obtain quantitative structure‐property relationship models between the topological indices and eight the physicochemical properties of the drugs to determine their effectiveness. The results show that the harmonic index was the best predictor for boiling point, flashpoint, and enthalpy of vaporization, while the redefined third Zagreb index was effective for polarizability, molar refractivity, and molar volume. The inverse sum indeg index was found to be effective for molar refractivity, and the second modified Zagreb index was surface tension in linear regression models. In addition, the redefined third Zagreb index was the best predictor for polarizability and molar refractivity, while the second modified Zagreb index was effective for molar volume. The SDD index was found to be effective for surface tension in quadratic regression models. [ABSTRACT FROM AUTHOR]

The major challenges encountered by medical researchers in developing new drugs are time consumption, increased cost, establishing a safety profile for the drugs, poor solubility, and inadequate experimental data. In its theoretical aspects, chemical graph theory plays a vital role in drug design and development by analyzing the structural parameters of molecules. Topological indices aim at the mathematical representation of a molecular structure, which is used to analyze the effectiveness of drugs and enhance the drug development process. In this study, we consider certain recently used drugs such as dexamethasone, molnupiravir, nirmatrelvir, ivermectin, ribavirin, baricitinib, favipiravir, duvelisib, L‐ascorbic acid, sofosbuvir, remdesivir, and pioglitazone for omicron, delta and other variants of coronaviruses. For these drug molecules, we propose a generalized form of reverse degree parameters and compute their associated topological indices with limiting behaviors. We undertake QSPR study on the potential of generalized reverse‐degree indices using linear and cubic regression models. [ABSTRACT FROM AUTHOR]

The development of Pd‐ and Ni‐catalyzed reactions for C−C bond formation is one of the primary driving forces in modern organic synthesis and the fine chemical industry. However, understanding the role of conformational mobility in reaction mechanisms is a long‐standing challenge. We highlight the effect of a multirotamer (multiconformer) system on the effective Gibbs free energy of activation in the key C−C coupling process and promote the use of a simplified version of multiconformer transition state theory that is straightforward to apply. Multivariate regression helped to quantitatively map the effect of coupled organic substituents (their structural and electronic parameters), as well as to determine the relative activity of metals. We provide computational evidence for solvent control of the equilibrium in RE/C−C‐bond activation for some model complexes. We also demonstrate that Ni complexes, being unique in the catalysis of sp3‐sp3 couplings, can be more challenging for machine learning and computational chemistry. The modeling was performed at an exceptionally high level, DLPNO‐CCSD(T)/CBS//RIJCOSX‐PBE0‐D4/def2‐TZVP. The Conclusions section contains an infographic summarizing the key findings related to the fields of cross‐coupling catalysis, machine learning in catalysis, and computational chemistry. [ABSTRACT FROM AUTHOR]

The next generation of alternative fuels is being investigated through advanced chemical and biological production techniques for the purpose of finding suitable replacements for diesel and gasoline while lowering production costs and increasing process yields. Chemical conversion of biomass to fuels provides a plethora of pathways with a variety of fuel molecules, both novel and traditional, which may be targeted. In the search for new fuels, an initial, intuition-driven evaluation of fuel compounds with desired properties is required. Due to the high cost and significant production time needed to synthesize these materials at a scale sufficient for exhaustive testing, a predictive model would allow chemists to preemptively screen fuel properties of potentially desirable fuel candidates. Recent work has shown that predictive models, in this case artificial neural networks (ANN's) analyzing quantitative structure property relationships (QSPR's), can predict the cetane number (CN) of a proposed fuel molecule with relatively small error. A fuel's CN is a measure of its ignition quality, typically defined using prescribed ASTM standards and a cetane testing engine. Alternatively, the analogous derived cetane number (DCN), obtained using an Ignition Quality Tester (IQT), is a direct measurement alternative to the CN that uses an empirical inverse relationship to the ignition delay found in the constant volume combustion chamber apparatus. DCN data points acquired using an IQT were utilized for model validation and expansion of the experimental database used in this study. The present work improves on an existing model by optimizing the model architecture along with the key learning variables of the ANN and by making the model more generalizable to a wider variety of fuel candidate types, specifically the class of furans and furan derivatives, by including specific molecules for the model to incorporate. The new molecules considered include tetrahydrofuran, 2-methylfuran, 2-methyltetrahydrofuran, 5,5′-(furan-2-ylmethylene)bis(2-methylfuran), 5,5′-((tetrahydrofuran-2-yl)methylene)bis(2-methyltetrahydrofuran), tris(5-methylfuran-2-yl)methane, and tris(5-methyltetrahydrofuran-2-yl)methane. Model architecture adjustments improved the overall root-mean-square error (RMSE) of the base database predictions by 5.54%. Additionally, through the targeted database expansion, it is shown that the predicted cetane number of the furan-based molecules improves on average by 49.21% (3.74 CN units) and significantly for a few of the individual molecules. This indicates that a selected subset of representative molecules can be used to extend the model's predictive accuracy to new molecular classes. The approach, bolstered by the improvements presented in this paper, enables chemists to focus on promising molecules by eliminating less favorable candidates in relation to their ignition quality.

The inhibition of mild steel deterioration via organic substances has become popular nowadays. Among the myriads of organic substances applied as potential inhibitors, quinoxalines stand out as toxic-free, cheap and effective compounds in different electrolytes. This report investigates the computational aspects of selected quinoxaline compounds tested as suppressors of mild steel deterioration in HCl medium using quantum chemical method (Density Functional Theory, DFT) and quantitative structure property relationship (QSPR). Feature selection tool was utilized to choose five top molecular descriptors (constitutional indices) that were used to characterize the quinoxaline molecules. Linear (ordinary least squares regression) and nonlinear (artificial neural network) modelling were adopted to correlate the selected constitutional indices of the studied quinoxalines with their experimental inhibition performances. The nonlinear model showed better performance as shown by the obtained results; RMSE of 5.4160, MSE of 29.3336, MAD of 2.3816 and MAPE of 5.0389. The developed models were utilized to determine the inhibition performances of ten new quinoxaline-based corrosion inhibitors which showed excellent inhibition performances of 87.88 to 95.73%.

Quartz crystal resonators coated with gas-sorptive films were characterized by analyzing their structures and their organic gas-sorption behaviors, which were then studied using statistics and computational molecular simulations. The films had carbonaceous structures that were prepared by radio-frequency (rf) sputtering of aromatic biomolecules, such as cytosine and lignin. These films had been compared with the rf-sputtered films of hydrophilic biopolymer of lignin and gelatin.The composition and morphology of each film governed the organic gas-sorption characteristics of the resonator sensors used for organic gas-sensing. The cytosine film, which was nitrogen-rich and layered, had a moderate affinity for the larger molecules of normal alcohols and acetate esters. The cytosine film is repulsive to positively-charged moieties of adsorbates. In contrast, the agarose film, which was nitrogen-poor and columnar, had a high affinity for small adsorbates with short hydrocarbon chains. Molecular flexibility of the adsorbate played an important role in the gas-sorption ability of the agarose film. Molecular dynamics simulation clarified that the weakly dipolar adsorbate-aggregate were immiscible in the cytosine adsorbent-aggregate because the collision mixture was prone to be instable. In practice, the cytosine-sputtered film had a higher gas-sorption ability for weakly dipolar species that have the longer hydrocarbon chains.

Extractive distillation is a common method to separate the mixture, which has the low relative volatility. Many solvents were screened to separate the mixture based on the experiment. Because the number of candidate solvents is high and the experiment is time-consuming. Quantitative structure property relationship was introduced to model and evaluate the relative volatility of isopropanol and water mixture. The model was developed based on genetic function approximation and 10 molecular descriptors were involved in using Material Studio software. The result indicated that the built model was sufficient for predicting the suitable solvent for isopropanol and water mixture. [ABSTRACT FROM AUTHOR]

An integrated approach physicochemistry and structures property relationships has been carried out to study the odorant molecules retention/release phenomenon in the water. This study aimed to identify the molecular properties (molecular descriptors) that govern this phenomenon assuming that modifying the structure leads automatically to a change in the retention/release property of odorant molecules. ACD/ChemSketch, MarvinSketch, and ChemOffice programs were used to calculate several molecular descriptors of 51 odorant molecules (15 alcohols, 11 aldehydes, 9 ketones and 16 esters). A total of 37 molecules (2/3 of the data set) were placed in the training set to build the QSPR models, whereas the remaining, 14 molecules (1/3 of the data set) constitute the test set. The best descriptors were selected to establish the quantitative structure property relationship (QSPR) of the retention/release property of odorant molecules in water using multiple linear regression (MLR), multiple non-linear regression (MNLR) and an artificial neural network (ANN) methods. We propose a quantitative model according to these analyses. The models were used to predict the retention/release property of the test set compounds, and agreement between the experimental and predicted values was verified. The descriptors showed by QSPR study are used for study and designing of new compounds. The statistical results indicate that the predicted values are in good agreement with the experimental results. To validate the predictive power of the resulting models, external validation multiple correlation coefficient was calculated and has both in addition to a performant prediction power, a favorable estimation of stability. DOI: http://dx.doi.org/10.17807/orbital.v9i4.978

• Found the optimal control structure for extractive distillation with two solvents. • Using the ISE to judge the measure of dynamic control. • Dynamic control performance can be a way to assess the reliable of QSPR. Extractive distillation is a common method to separate the mixture which has the low relative volatility, such as acetone and tetrahydrofuran mixture. Because the number of candidate solvents is high and the experiment is time-consuming, the selection method of solvent is important. In our previous work, quantitative structure property relationship was introduced to model and evaluate the relative volatility of acetone and tetrahydrofuran mixture. The flowsheet of extractive distillation was set up and the total annual cost was calculated with n-Octane and butyl ether as new and former solvent, respectively. Except the mathematical verification, we think that the dynamic control of the configuration of extractive distillation can also be a method to evaluate the feasibility of the built model. In this paper, we studied the dynamic control for extractive distillation with n-Octane and butyl ether as solvent and the integral of squared error was calculated for purity of two products. The results show that some indexes for n-Octane as solvent are better than that for butyl ether as solvent. The dynamic control performance is consistent with the results of economical and can be a way to judge the reliability of quantitative structure property relationship. [ABSTRACT FROM AUTHOR]

Organic Rankine cycle (ORC) is a promising power cycle to convert low-grade thermal energy. The quick and easy evaluation of the potential working fluids lacking thermophysical property data is important for the working fluid selection of ORC. This paper proposes a systematic framework of working fluid evaluation from the molecular structure perspective, which couples the quantitative structure property relationship (QSPR) and universal equations of state. The thermodynamic performance of ORC is first correlated with four key thermophysical properties based on universal equations of state. Then, these thermophysical properties are obtained from the corresponding QSPR models based on the molecular structure of working fluids. The influence factors of ORC performance at a micro level are investigated using the QSPR models. Moreover, the effects of molecular characteristics on thermal efficiency are analyzed. The criteria for working fluid design/selection are recommended aiming at the improvement of thermal efficiency. The proposed framework shows sufficient accuracy with the relative errors of −0.41 - 15.85% and mean absolute error of 8.29% compared with the results based on REFPROP. It is found that the cyclization of carbon chain is an ideal method for the working fluid design based on alkanes and alkenes. This work can provide new insights for the evaluation, selection, and design of working fluids on the molecular structure level. • A framework of working fluid evaluation based on molecular structure is proposed. • Micro influence factors of ORC performance are investigated. • Effects of molecular characteristics on thermal efficiency are analyzed. • Criteria for working fluid design/selection are recommended. [ABSTRACT FROM AUTHOR]

Solubility parameters have been applied extensively in the chemical and pharmaceutical sciences. Particularly attractive is calculation of solubility parameters based on chemical structure and recently, new in silico methods have been proposed. Thus, screening charge densities of molecular surfaces (i.e. so-called σ -profiles) are used by the conductor-like screening model for real solvents (COSMO-RS) and can be employed in a quantitative structure property relationship (QSPR) to predict solubility parameters. In the current study, it was aimed to compare both in silico methods with an experimental dataset of pharmaceutical compounds, which was complemented with own measurements by inverse gas chromatography. An initial evaluation of the total solubility parameters of reference solvents resulted in excellent predictions (observed versus predicted values) with R 2 of 0.855 (COSMO-RS) and 0.945 (QSPR). The subsequent main study of pharmaceutical compounds exhibited R 2 values of 0.701 (COSMO-RS) and 0.717 (QSPR). The comparatively lower prediction was to some extent due to the solid state of pharmaceuticals with known conceptual limitations of the solubility parameter and possible experimental bias. Total solubility parameters were also estimated by classical group contribution methods, which had comparatively lower prediction power. Therefore, the new in silico methods are highly promising for pharmaceutical applications. [ABSTRACT FROM AUTHOR]

A quantitative structure–mobility relationship (QSMR) is proposed to estimate the electrophoretic mobility of diverse sets of analyses in capillary zone electrophoresis using Abraham solvation parameters of analyses, such as the excess molar refraction, polarizability, hydrogen bond acidity, basicity, and molar volume. QSMR was developed for prediction the electrophoretic mobility of 231 organic acids using the solvation parameters calculated by Abraham. Multiple linear regression (MLR) as a linear model and artificial neural network (ANN) methods were used to evaluate the nonlinear behavior of the involved parameters. The prediction results are obtained by nonlinear model, ANN, seem to be superior over MLR and were in good agreement with experimental data. In the proposed ANN–QSMR model, the overall mean percentage deviation values were 5.6, 5.4, and 5.3% and the coefficients of determinations (R2) were 0.84, 0.84, and 0.84 for training, test, and verification set, respectively. To investigate the robustness of the model, cross-validation methods have been established, i.e., leave-one-out and leave-N-out (N = 5 and 10) and model is showed good predictive ability against data variation in cross-validation process. This model is not only able to accurately predict the migration order of a diverse set of organic acids but also model finds that solvation parameters are responsible in separation mechanism. [ABSTRACT FROM PUBLISHER]

Artificial neural network (ANN) was applied to the prediction of cetane numbers (CN) of hydrocarbons and oxygenates. We built an ANN model based on the quantitative structure property relationship strategy. Descriptors of pure compounds, that can be easily obtained, were selected based on the knowledge of evaporation and combustion process of fuels inside compression-ignition engines. The homogeneity of these descriptors were also considered, in order to develop a general model. Our optimized ANN models, obtained by screening all possible configurations, showed higher accuracy than that using multiple linear regression. After performing ANN regressions on two subgroups of the dataset, average absolute errors of 6.5 and 4.4 were achieved for compounds with and without rings, respectively. Ranking the impact of descriptors on CN values hinted that evaporation relevant features and bond energy could be more important than molecular structure related features. [ABSTRACT FROM AUTHOR]

The ACD/ChemSketch, MarvinSketch, and ChemOffice programmes were used to calculate several molecular descriptors of 51 odorant molecules (15 alcohols, 11 aldehydes, 9 ketones and 16 esters). The best descriptors were selected to establish the Quantitative Structure-Property Relationship (QSPR) of the retention/release property of odorant molecules in pectin gels using Principal Components Analysis (PCA), Multiple Linear Regression (MLR), Multiple Non-linear Regression (MNLR) and Artificial Neural Network (ANN) methods We propose a quantitative model based on these analyses. PCA has been used to select descriptors that exhibit high correlation with the retention/release property. The MLR method yielded correlation coefficients of 0.960 and 0.958 for PG-0.4 (pectin concentration: 0.4% w/w) and PG-0.8 (pectin concentration: 0.8% w/w) media, respectively. Internal and external validations were used to determine the statistical quality of the QSPR of the two MLR models. The MNLR method, considering the relevant descriptors obtained from the MLR, yielded correlation coefficients of 0.978 and 0.975 for PG-0.4 and PG-0.8 media, respectively. The applicability domain of MLR models was investigated using simple and leverage approaches to detect outliers and outside compounds. The effects of different descriptors on the retention/release property are described, and these descriptors were used to study and design new compounds with higher and lower values of the property than the existing ones. [ABSTRACT FROM AUTHOR]

Mixtures are used widely in the chemical industry and most chemical processes are designed based on mixtures' critical properties. Therefore, it is extremely important to study critical properties of mixtures. In this work, a quantitative structure property relationship (QSPR) study was employed to predict critical temperatures of binary mixtures. Dragon software was used to calculate molecular descriptors of pure chemicals. Descriptors of mixtures were calculated as mole weighted average. The genetic algorithm (GA) was used to select the optimal subset of descriptors which significantly contribute to the critical temperature of binary mixtures. The multiple linear regression (MLR) method was used to build QSPR models. The validations including internal and external validation were used to check the stability and predictive capability of the obtained models. Three different strategies of external validation, including the “points out”, “mixtures out” and “compounds out”, were used to divide the training set and test set. The applicability domain (AD) for the models was also discussed. All the results have shown that the obtained models had great fitness with the experimental data (R 2 were 0.922, 0.925 and 0.900, root mean square error were 0.025, 0.029 and 0.030, average absolute error were 0.014, 0.021 and 0.018, respectively), excellent internal robustness (Q 2 LMO were 0.733, 0.904 and 0.888), and good predictive ability (Q 2 ext were 0.888, 0.822 and 0.780). The established models offer a reasonable estimation of the critical temperature of binary mixtures, and hence could provide guidance for chemical process design involving binary mixtures. [ABSTRACT FROM AUTHOR]

The chromatographic hydrophobicity index (CHI) is an HPLC-based parameter that provides reliable guidance in optimization of pharmacological efficiency and adsorption, distribution, metabolism and exertion (ADME) profile of drug candidates. In the present work, classical and three-dimensional quantitative structure-property relationship (QSPR) models were developed for prediction of CHI values of some 4-hydroxycoumarin analogs on immobilized artificial membrane column. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) as 3D-QSPR methods were performed to gain insight into the key structural factors affecting on the chromatographic hydrophobicity of interested chemicals. The calculated parameters of Q2, R2 and standard error were 0.545, 0.996 and 0.773 for CoMFA model and 0.815, 0.986 and 1.44 for CoMSIA model, respectively. The contour maps for steric fields of the CoMFA model illustrate that the hydrophobicity of chemicals will be higher when the positions of R6, R7 and R8 in the 4-hydroxycuomarin ring are substituted by alkyl groups. Moreover, by the analysis of the plots of electrostatic fields, it was concluded that the CHI value greatly increases if one hydrogen on coumarin ring is substituted by the F, Cl, Br, OH or OCH3 group. [ABSTRACT FROM AUTHOR]

The accurate prediction of the rate and extent of transdermal absorption from topical exposure to chemical mixtures would be beneficial in risk assessment and drug delivery applications. The isolated perfused porcine skin flap (IPPSF) has been used as an ex vivo model for assessing transdermal absorption from topical exposures. A mixed effect, pharmacokinetic tissue model was used to model finite dose, transdermal, absorption data from IPPSF experiments for 12 penetrants dosed in up to 10 different vehicles. The model was able to identify permeability constant, while accounting for between and within unit variability, across the entire data set. This approach provides a platform for exploring the relationship between covariates (chemical descriptors and functions thereof) and the model parameters. Successive models were employed that reduced the overall variability in the parameter estimate by modeling the parameters as functions of the covariates. Log kp was initially modeled as a function of Log P and MW of the pure penetrant (adjusted r2 = 0.48). The addition of mixture factors to account for the different dosing vehicles further improved the relationship: to r2 = 0.56 with Connolly molecular area (CMA) and r2 = 0.78 with the further addition of total polar surface area difference (TPSAd). The pharmacokinetic model and quantitative structure property relationship (QSPR) developed for the IPPSF may be relevant to clinical transdermal formulation development. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Optimal salinity (Sopt) is an important property that influences the relative phase volume and solubilization parameters in microemulsion flooding. Selecting an appropriate surfactant for chemical flooding with high Sopt requires characterizing lots of different chemicals and a sophisticated interpretation procedure. The main objective of this paper is to apply quantitative structure property relationship (QSPR) technique to model the Sopt of 20 different surfactant molecules. The surfactant molecules are sorted into two different groups according to their experimental conditions. Geometrical optimization of surfactants was performed at RM1 level. Then, many structural and quantum chemical descriptors were calculated using different computer software programs. Using variable selection of the genetic algorithm (GA-MLR), two descriptors were introduced as independent variables. The squared correlation coefficient ( R 2 ) and standard deviation ( s ) calculated for the selected model were 0.940 and 0.643 for molecular group-A; and 0.984 and 0.445 for molecular group-B, respectively. The results demonstrate high estimation accuracy and strong generalization capacity of the models. The descriptors in both models are related to polarizability and ionization potential of the molecules, thus they are conceptually related to the changes in Sopt. The performance of QSPR was further compared with some commonly used constitutional descriptors in previous literature reports. By comparing the results, one can conclude that the estimation of Sopt can be improved significantly by using QSPR compared with other correlations. [ABSTRACT FROM AUTHOR]

Quantitative structure property relationship (QSPR) models allow for estimation of unknown substance properties. Their accuracy is usually described by error metrics that compare the result of the estimation to an experimental value. However, it has to be kept in mind that measured values are not perfectly accurate as well. The actual reliability of a QSPR model is therefore often significantly lower than indicated by conventional error metrics. To deal with this issue, the combined experimental and predictive uncertainty (CEPU) is proposed. Analogously to error propagation in evaluation of experimental works, the reliability of estimation methods can be accessed by the root of the sum of the different squared contributions to overall uncertainty. Such a metric is able to describe the effects of experimental errors with uniform algebraic sign as well as of randomized experimental errors. [ABSTRACT FROM AUTHOR]