Journal articles: 'QSPkR'

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Zhivkova, Zvetanka. "QUANTITATIVE STRUCTURE–PHARMACOKINETICS RELATIONSHIP FOR PLASMA PROTEIN BINDING OF NEUTRAL DRUGS." International Journal of Pharmacy and Pharmaceutical Sciences 10, no. 4 (April 1, 2018): 88. http://dx.doi.org/10.22159/ijpps.2018v10i4.24612.

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Objective: Plasma protein binding (PPB) of drugs is important pharmacokinetic (PK) phenomena controlling the free drug concentration in plasma and the overall PK and pharmacodynamic profile. Prediction of PPB at the very early stages of drug development process is of paramount importance for the success of new drug candidates. The study presents a quantitative structure–pharmacokinetics relationship (QSPkR) modelling of PPB for neutral drugs.Methods: The dataset consists of 117 compounds, described by 138 molecular descriptors. Genetic algorithm and stepwise multiple linear regression are used for variable selection and QSPkR models development. The QSPkRs are evaluated by internal and external validation procedures.Results: A robust, significant and predictive QSPkR with explained variance r2 0.768, cross-validated q2LOO-CV 0.731,and geometric mean fold error of prediction (GMFEP) 1.79 is generated, which is able to predict the extent of PPB for 67.6% of the drugs in the dataset within the 2-fold error of experimental values. A simple empiric rule is proposed for distinguishing between drugs with different binding affinity, which allowed correct classification of 78% of the high binders and 87.5% of the low binders.Conclusions: PPB of neutral drugs is favored by lipophilicity, dipole moment, the presence of substituted aromatic and fused rings and a nine-member ring system, and is disfavored by the presence of aromatic N-atoms. Keywords: Plasma protein binding (PPB), Quantitative structure–pharmacokinetics relationship (QSPkR), In silico prediction, Human serum albumin (HSA), Alpha-1-acid glycoprotein (AGP).

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Zhivkova, Zvetanka. "QUANTITATIVE STRUCTURE–PHARMACOKINETICS MODELING OF THE UNBOUND CLEARANCE FOR NEUTRAL DRUGS." International Journal of Current Pharmaceutical Research 10, no. 2 (March 15, 2018): 56. http://dx.doi.org/10.22159/ijcpr.2018v10i2.25849.

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Objective: Prediction of pharmacokinetic behaviour of new candidate drugs is an important step in drug design. Clearance is a key pharmacokinetic parameter, controlling drug exposure in the body. It depends on numerous factors and is frequently restricted by plasma protein binding. The study is focused on the development of quantitative structure-pharmacokinetic relationship (QSPkR) for the unbound clearance (CLu) of neutral drugs.Methods: The dataset consisted of 117 neutral drugs, divided into training set (n = 94) and external test set (n = 23). Chemical structures were encoded by 113 theoretical descriptors. Genetic algorithm and step-wise multiple linear regression were applied for model development. The model was evaluated by cross-validation in the training set and external test set.Results: Significant, predictive and interpretable QSPkR model was developed with explained variance r2 = 0.617, cross-validated correlation coefficient q2LOO-CV = 0.554, external test set predictive coefficient r2pred = 0.656, and root mean square error in prediction RMSEP = 1.89. The model was able to predict CLu for 56% of the drugs in the external test set within the 2-fold error of experimental values.Conclusion: The model reveals the main molecular features governing CLu of neutral drugs. CLu is favoured by lipophilicity, the presence of fused aromatic rings, ester groups, dihydropyridine moieties and nine-member ring systems, while polarity, molecular size and strong electron withdrawing atoms and groups as substituents in aromatic rings affect negatively CL

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Morris, Marilyn E., Xinning Yang, Yash A. Gandhi, Suraj G. Bhansali, and Lisa J. Benincosa. "Interspecies scaling: prediction of human biliary clearance and comparison with QSPKR." Biopharmaceutics & Drug Disposition 33, no. 1 (January 2012): 1–14. http://dx.doi.org/10.1002/bdd.1761.

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Zhivkova, Zvetanka Dobreva. "Quantitative Structure – Pharmacokinetics Relationships for Plasma Protein Binding of Basic Drugs." Journal of Pharmacy & Pharmaceutical Sciences 20, no. 1 (September 14, 2017): 349. http://dx.doi.org/10.18433/j33633.

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Purpose. Binding of drugs to plasma proteins is a common physiological occurrence which may have a profound effect on both pharmacokinetics and pharmacodynamics. The early prediction of plasma protein binding (PPB) of new drug candidates is an important step in drug development process. The present study is focused on the development of quantitative structure – pharmacokinetics relationship (QSPkR) for the negative logarithm of the free fraction of the drug in plasma (pfu) of basic drugs. Methods. A dataset includes 220 basic drugs, which chemical structures are encoded by 176 descriptors. Genetic algorithm, stepwise regression and multiple linear regression are used for variable selection and model development. Predictive ability of the model is assessed by internal and external validation. Results. A simple, significant, interpretable and predictive QSPkR model is constructed for pfu of basic drugs. It is able to predict 59% of the drugs from an external validation set within the 2-fold error of the experimental values with squared correlation coefficient of prediction 0.532, geometric mean fold error (GMFE) 1.94 and mean absolute error (MAE) 0.17. Conclusions. PPB of basic drugs is favored by the lipophilicity, the presence of aromatic C-atoms (either non-substituted, or involved in bridged aromatic systems) and molecular volume. The fraction ionized as a base fB and the presence of quaternary C-atoms contribute negatively to PPB. A short checklist of criteria for high PPB is defined, and an empirical rule for distinguishing between low, high and very high plasma protein binders is proposed based. This rule allows correct classification of 69% of the very high binders, 71% of the high binders and 91% of the low binders in plasma. This article is open to POST-PUBLICATION REVIEW. Registered readers (see “For Readers”) may comment by clicking on ABSTRACT on the issue’s contents page.

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Zhivkova, Zvetanka Dobreva, Tsvetelina Mandova, and Irini Doytchinova. "Quantitative Structure – Pharmacokinetics Relationships Analysis of Basic Drugs: Volume of Distribution." Journal of Pharmacy & Pharmaceutical Sciences 18, no. 3 (October 12, 2015): 515. http://dx.doi.org/10.18433/j3xc7s.

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Purpose. The early prediction of pharmacokinetic behavior is of paramount importance for saving time and resources and for increasing the success of new drug candidates. The steady-state volume of distribution (VDss) is one of the key pharmacokinetic parameters required for the design of a suitable dosage regimen. The aim of the study is to propose a quantitative structure – pharmacokinetics relationships (QSPkR) for VDss of basic drugs. Methods: The data set consists of 216 basic drugs, divided to a modeling (n = 180) and external validation set (n = 36). 179 structural and physicochemical descriptors are calculated using validated commercial software. Genetic algorithm, stepwise regression and multiple linear regression are applied for variable selection and model development. The models are validated by internal and external test sets. Results: A number of significant QSPkRs are developed. The most frequently emerged descriptors are used to derive the final consensus model for VDss with good explanatory (r2 0.663) and predictive ability (q2LOO-CV 0.606 and r2pred 0.593). The model reveals clear structural features determining VDss of basic drugs which are summarized in a short list of criteria for rapid discrimination between drugs with a large and small VDss. Conclusions: Descriptors like lipophilicity, fraction ionized as a base at pH 7.4, number of cycles and fused aromatic rings, presence of Cl and F atoms contribute positively to VDss, while polarity and presence of strong electrophiles have a negative effect. This article is open to POST-PUBLICATION REVIEW. Registered readers (see “For Readers”) may comment by clicking on ABSTRACT on the issue’s contents page.

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van der Graaf, Pieter H., Jonas Nilsson, Erno A. van Schaick, and Meindert Danhof. "Multivariate Quantitative Structure–Pharmacokinetic Relationships (QSPKR) Analysis of Adenosine A1 Receptor Agonists in rat." Journal of Pharmaceutical Sciences 88, no. 3 (March 1999): 306–12. http://dx.doi.org/10.1021/js980294a.

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Durairaj, Chandrasekar, Jaymin C. Shah, Shruti Senapati, and Uday B. Kompella. "Prediction of Vitreal Half-Life Based on Drug Physicochemical Properties: Quantitative Structure–Pharmacokinetic Relationships (QSPKR)." Pharmaceutical Research 26, no. 5 (October 8, 2008): 1236–60. http://dx.doi.org/10.1007/s11095-008-9728-7.

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Louis, Bruno, and Vijay K. Agrawal. "Quantitative structure-pharmacokinetic relationship (QSPkP) analysis of the volume of distribution values of anti-infective agents from j group of the ATC classification in humans." Acta Pharmaceutica 62, no. 3 (September 1, 2012): 305–23. http://dx.doi.org/10.2478/v10007-012-0024-z.

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In this study, a quantitative structure-pharmacokinetic relationship (QSPkR) model for the volume of distribution (Vd) values of 126 anti-infective drugs in humans was developed employing multiple linear regression (MLR), artificial neural network (ANN) and support vector regression (SVM) using theoretical molecular structural descriptors. A correlation-based feature selection (CFS) was employed to select the relevant descriptors for modeling. The model results show that the main factors governing Vd of anti-infective drugs are 3D molecular representations of atomic van der Waals volumes and Sanderson electronegativities, number of aliphatic and aromatic amino groups, number of beta-lactam rings and topological 2D shape of the molecule. Model predictivity was evaluated by external validation, using a variety of statistical tests and the SVM model demonstrated better performance compared to other models. The developed models can be used to predict the Vd values of anti-infective drugs.

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Zhivkova, Zvetanka Dobreva. "Quantitative Structure – Pharmacokinetic Relationships for Plasma Clearance of Basic Drugs with Consideration of the Major Elimination Pathway." Journal of Pharmacy & Pharmaceutical Sciences 20 (May 29, 2017): 135. http://dx.doi.org/10.18433/j3mg71.

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Purpose. The success of a new drug candidate is determined not only by its efficacy and safety, but also by proper pharmacokinetic behavior. The early prediction of pharmacokinetic parameters could save time and resources and accelerate drug development process. Plasma clearance (CL) is one of the key determinants of drug dosing regimen. The aim of the study is development of quantitative structure – pharmacokinetics relationships (QSPkRs) for the CL. Methods. A dataset consisted of 263 basic drugs, which chemical structures were described by 154 descriptors. Genetic algorithm, stepwise regression and multiple linear regression were used for variable selection and model development. Predictive ability of the models was assessed by internal and external validation. Results. A number of significant QSPkR models for the CL were derived with respect to the primary elimination pathway (renal excretion, metabolism, or CYP3A4 mediated biotransformation), as well for the unbound clearance (CLu). The models were able to predict 52 – 80% of the drugs from external validation sets within the 2-fold error of the experimental values with geometric mean fold error 1.57 – 2.00. Conclusions. Plasma protein binding was the major restrictive factor for the CL of drugs, primarily cleared by metabolism. The clearance was favored by lipophilicity and several structural features like OH-groups, aromatic rings, large hydrophobic centers, aliphatic groups, connected with electro-negative atoms, and non-substituted aromatic C-atoms. The presence of Cl-atoms and abundance of 6-member aromatic rings or fused rings had negative effect. The presence of ether O-atoms contributed negatively to the CL of both metabolism and renally excreted drugs, and urine excretion was favored by the presence of 3-valence N-atoms. These findings give insight on the main structural features governing plasma CL of basic drugs and could serve as a guide for lead optimization in the drug development process. This article is open to POST-PUBLICATION REVIEW. Registered readers (see “For Readers”) may comment by clicking on ABSTRACT on the issue’s contents page.

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Honey, Suresh Thareja, Manoj Kumar, and V. R. Sinha. "Self-organizing molecular field analysis of NSAIDs: Assessment of pharmacokinetic and physicochemical properties using 3D-QSPkR approach." European Journal of Medicinal Chemistry 53 (July 2012): 76–82. http://dx.doi.org/10.1016/j.ejmech.2012.03.037.

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Goel, Honey, V. R. Sinha, Suresh Thareja, Saurabh Aggarwal, and Manoj Kumar. "Assessment of biological half life using in silico QSPkR approach: A self organizing molecular field analysis (SOMFA) on a series of antimicrobial quinolone drugs." International Journal of Pharmaceutics 415, no. 1-2 (August 2011): 158–63. http://dx.doi.org/10.1016/j.ijpharm.2011.05.065.

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Li, Yan Kun, and Xiao Ying Ma. "QSAR/QSPR Model Research of Complicated Samples." Advanced Materials Research 740 (August 2013): 306–9. http://dx.doi.org/10.4028/www.scientific.net/amr.740.306.

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QSAR/QSPR study is a hot issue in present chemical informatics research, and is the very active research domain. In present, a large number of QSAR/QSPR (quantitative structure-activity/property relationships) models have been widely studied and applied in a lot of different areas. This paper overviews the developments, research methods and applications of QSAR/QSPR model.

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Iswanto, Ponco, Eva Vaulina Yulistia Delsy, Ely Setiawan, and Fiandy Aminullah Putra. "Quantitative Structure-Property Relationship Analysis Against Critical Micelle Concentration of Sulfonate-Based Surfactant Based on Semiempirical Zindo/1 Calculation." Molekul 14, no. 2 (November 30, 2019): 78. http://dx.doi.org/10.20884/1.jm.2019.14.2.467.

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Development of anionic surfactant compound isvery important because the anionic surfactant class iswidely used in people's lives. For instance,anionic surfactantsare used as food additives and detergents. The novelcompound of sulfonate-basedsurfactantor proposed compound has predictedthe CriticalMicelle Concentration(CMC) value of experiment. Quantitative Structure-Property Relationship (QSPR)analysisbased on semiempiricalZINDO/1 calculationwas conducted to obtain QSPR equation. Theoretical predictorsor independent variable which have an influence on the value of CMC are used to construct QSPR equation. The theoretical predictors areclassified intopredictor of electronic properties, solubility and steric. A total of 108experimentalCMCbelongs to sulfonate-basedsurfactant are calculated their theoretical predictors and analyzed by multiple linear regression. The QSPR equationwhich is obtainedfromthis study contains theimportant theoretical predictors.They are solubility properties, molecular weight, molecular size and net charge of carbon atomin thepolar partof sulfonate-based surfactant. This QSPR equation couldbe used to predict the CMC value of the novelsulfonate-based surfactant.

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Ng, Chee, Yunde Xiao, Wendy Putnam, Bert Lum, and Alexander Tropsha. "Quantitative structure−pharmacokinetic parameters relationships (QSPKR) analysis of antimicrobial agents in humans using simulated annealing k‐nearest‐neighbor and partial least‐square analysis methods**This paper was presented in part at the Annual Meeting of the American Association of Pharmaceutical Scientists in Toronto in 2003." Journal of Pharmaceutical Sciences 93, no. 10 (October 2004): 2535–44. http://dx.doi.org/10.1002/jps.20117.

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Poulin, Patrick, and Kannan Krishnan. "A Quantitative Structure-toxicokinetic Relationship Model for Highly Metabolised Chemicals." Alternatives to Laboratory Animals 26, no. 1 (January 1998): 45–55. http://dx.doi.org/10.1177/026119299802600109.

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The aim of the present study was to develop a quantitative structure-toxicokinetic relationship (QSTkR) model for highly metabolised chemicals (HMCs). The proposed QSTkR model is essentially a physiologically based toxicokinetic (PBTK) model, in which the blood:air and tissue:blood partition coefficients (PCs) are predicted from the molecular structure of chemicals, and the liver blood flow rate (Q1) is used to describe hepatic clearance. Molecular structure-based prediction of the blood:air and tissue:blood PCs was performed from the n-octanol:water and water:air PCs of chemicals obtained with the conventional fragment constant methods. The validity of incorporating Q1 instead of metabolic rate constants, as the hepatic clearance factor, in PBTK models for HMCs (extraction ratio > 0.7) was verified by comparing the simulations of venous blood concentration (Cv) profiles obtained with both the QSTkR and PBTK model approaches for 1,1-dichloroethylehe, trichloroethylene and furan in the rat. Following the validation of this alternative approach for describing hepatic clearance of HMCs, a QSTkR model for dichloromethane was constructed. This model used molecular structure information as the sole input, and provided simulations of Cv for human exposure to low concentrations of dichloromethane. The QSTkR model simulations were similar to those obtained with the previously validated, conventional human PBTK model with experimentally determined PCs and metabolic rate constants (Vmax, Km and Kf) for dichloromethane. The present methodology is the first validated example of a mechanistically based prediction of the inhalation toxicokinetics of HMCs made solely from information on molecular structure.

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Romanovskaya, Irina, Victor Kuz’min, Olga Oseychuk, Eugeniy Muratov, Anatoliy Artemenko, and Sergei Andronati. "QSPR Analysis of Peroxidase Substrates Reactivity." Chemistry & Chemical Technology 3, no. 4 (December 15, 2009): 255–61. http://dx.doi.org/10.23939/chcht03.04.255.

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Quantitative structure-property relationship (QSPR) analysis of phenol derivatives reactivity in the horseradish peroxidase catalyzed oxidative reactions was carried out. The statistic models, which describe the substituted phenols reactivity (Кm-1, Vmax) quite adequately, were obtained by multiple linear regression and partial least squares (PLS) methods. The electronic parameters of molecules, their lipophylicity, molecular refraction, and form parameters were used as descriptors for molecular structure. The obtained models allow to predict the reactivity of the new phenolic substrates with satisfactory reliability.

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Nikolic, Katarina, Slavica Filipic, Adam Smoliński, Roman Kaliszan, and Danica Agbaba. "Partial Least Square and Hierarchical Clustering in ADMET Modeling: Prediction of Blood – Brain Barrier Permeation of α-Adrenergic and Imidazoline Receptor Ligands." Journal of Pharmacy & Pharmaceutical Sciences 16, no. 4 (November 5, 2013): 622. http://dx.doi.org/10.18433/j3jk5p.

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PURPOSE. Rate of brain penetration (logPS), brain/plasma equilibration rate (logPS-brain), and extent of blood-brain barrier permeation (logBB) of 29 α-adrenergic and imidazoline-receptors ligands were examined in Quantitative-Structure-Property Relationship (QSPR) study. METHODS. Experimentally determined chromatographic retention data (logKw at pH 4.4, slope (S) at pH 4.4, logKw at pH 7.4, slope (S) at pH 7.4, logKw at pH 9.1, and slope (S) at pH 9.1) and capillary electrophoresis migration parameters (μeff at pH 4.4, μeff at pH 7.4, and μeff at pH 9.1), together with calculated molecular descriptors, were used as independent variables in the QSPR study by use of partial least square (PLS) methodology. RESULTS. Predictive potential of the formed QSPR models, QSPR(logPS), QSPR(logPS-brain), QSPR(logBB), was confirmed by cross- and external validation. Hydrophilicity (Hy) and H-indices (H7m) were selected as significant parameters negatively correlated with both logPS and logPS-brain, while topological polar surface area (TPSA(NO)) was chosen as molecular descriptor negatively correlated with both logPS and logBB. The principal component analysis (PCA) and hierarchical clustering analysis (HCA) were applied to cluster examined drugs based on their chromatographic, electrophoretic and molecular properties. Significant positive correlations were obtained between the slope (S) at pH 7.4 and logBB in A/B cluster and between the logKw at pH 9.1 and logPS in C/D cluster. CONCLUSIONS. Results of the QSPR, clustering and correlation studies could be used as novel tool for evaluation of blood-brain barrier permeation of related α-adrenergic/imidazoline receptor ligands.This article is open to POST-PUBLICATION REVIEW. Registered readers (see “For Readers”) may comment by clicking on ABSTRACT on the issue’s contents page.PURPOSE. Rate of brain penetration (logPS), brain/plasma equilibration rate (logPS-brain), and extent of blood-brain barrier permeation (logBB) of 29 α-adrenergic and imidazoline-receptors ligands were examined in Quantitative-Structure-Property Relationship (QSPR) study. METHODS. Experimentally determined chromatographic retention data (logKw at pH 4.4, slope (S) at pH 4.4, logKw at pH 7.4, slope (S) at pH 7.4, logKw at pH 9.1, and slope (S) at pH 9.1) and capillary electrophoresis migration parameters (μeff at pH 4.4, μeff at pH 7.4, and μeff at pH 9.1), together with calculated molecular descriptors, were used as independent variables in the QSPR study by use of partial least square (PLS) methodology. RESULTS. Predictive potential of the formed QSPR models, QSPR(logPS), QSPR(logPS-brain), QSPR(logBB), was confirmed by cross- and external validation. Hydrophilicity (Hy) and H-indices (H7m) were selected as significant parameters negatively correlated with both logPS and logPS-brain, while topological polar surface area (TPSA(NO)) was chosen as molecular descriptor negatively correlated with both logPS and logBB. The principal component analysis (PCA) and hierarchical clustering analysis (HCA) were applied to cluster examined drugs based on their chromatographic, electrophoretic and molecular properties. Significant positive correlations were obtained between the slope (S) at pH 7.4 and logBB in A/B cluster and between the logKw at pH 9.1 and logPS in C/D cluster. CONCLUSIONS. Results of the QSPR, clustering and correlation studies could be used as novel tool for evaluation of blood-brain barrier permeation of related α-adrenergic/imidazoline receptor ligands. This article is open to POST-PUBLICATION REVIEW. Registered readers (see “For Readers”) may comment by clicking on ABSTRACT on the issue’s contents page.

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Toropova, Alla P., and Andrey A. Toropov. "QSPR and nano-QSPR: What is the difference?" Journal of Molecular Structure 1182 (April 2019): 141–49. http://dx.doi.org/10.1016/j.molstruc.2019.01.040.

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Meraz, Md, Arfa Malik, Wenhong Yang, and Wen-Hua Sun. "Catalytic Performance of Cycloalkyl-Fused Aryliminopyridyl Nickel Complexes toward Ethylene Polymerization by QSPR Modeling." Catalysts 11, no. 8 (July 29, 2021): 920. http://dx.doi.org/10.3390/catal11080920.

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Quantitative structure–property relationship (QSPR) modeling is performed to investigate the role of cycloalkyl-fused rings on the catalytic performance of 46 aryliminopyridyl nickel precatalysts. The catalytic activities for nickel complexes in ethylene polymerization are well-predicted by the obtained 2D-QSPR model, exploring the main contribution from the charge distribution of negatively charged atoms. Comparatively, 3D-QSPR models show better predictive and validation capabilities than that of 2D-QSPR for both catalytic activity (Act.) and the molecular weight of the product (Mw). Three-dimensional contour maps illustrate the predominant effect of a steric field on both catalytic properties; smaller sizes of cycloalkyl-fused rings are favorable to Act.y, whereas they are unfavorable to Mw. This study may provide assistance in the design of a new nickel complex with high catalytic performance.

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Fiorella, Cravero, Martínez M. Jimena, Vazquez Gustavo, Mónica F. Díaz, and Ponzoni Ignacio. "Feature Learning applied to the Estimation of Tensile Strength at Break in Polymeric Material Design." Journal of Integrative Bioinformatics 13, no. 2 (June 1, 2016): 15–29. http://dx.doi.org/10.1515/jib-2016-286.

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Summary Several feature extraction approaches for QSPR modelling in Cheminformatics are discussed in this paper. In particular, this work is focused on the use of these strategies for predicting mechanical properties, which are relevant for the design of polymeric materials. The methodology analysed in this study employs a feature learning method that uses a quantification process of 2D structural characterization of materials with the autoencoder method. Alternative QSPR models inferred for tensile strength at break (a well-known mechanical property of polymers) are presented. These alternative models are contrasted to QSPR models obtained by feature selection technique by using accuracy measures and a visual analytic tool. The results show evidence about the benefits of combining feature learning approaches with feature selection methods for the design of QSPR models.

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Osipov, Alexander Leonidovich, and Veronica Pavlovna Trushina. "ПРОГНОЗИРОВАНИЕ ЛИПОФИЛЬНЫХ СВОЙСТВ ПРОИЗВОДНЫХ АДАМАНТАНА." Siberian Journal of Life Sciences and Agriculture 12, no. 5 (September 13, 2020): 11. http://dx.doi.org/10.12731/2658-6649-2020-12-5-11-15.

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В статье исследуются QSPR модели предсказания липофильности химических веществ семейства адамантанов. Исследование параметра липофильности осуществляется с помощью разработанных нелинейных моделей с использованием абсолютной энтропии. Проведены вычислительные эксперименты, показывающие высокую эффективность предложенных QSPR зависимостей.

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Kuz'min, Victor E., Liudmila N. Ognichenko, Viktor F. Zinchenko, Anatoly G. Artemenko, Angela O. Shyrykalova, and Anna V. Kozhukhar. "QSPR Models for Predicting of the Melting Points and Refractive Indices for Inorganic Substances." International Journal of Quantitative Structure-Property Relationships 5, no. 1 (January 2020): 1–21. http://dx.doi.org/10.4018/ijqspr.2020010101.

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The QSPR methodology is very promising for the creation of new materials, including materials based on inorganic compounds. However, the majority of QSPR descriptor systems are applicable only for organic molecules. In this work the 1D - QSPR descriptor system is proposed for analysis of the properties of various inorganic compounds. These descriptors are easily accessible, as they describe the most fundamental atom properties. The combinatorial schemes for computing these descriptors provide for their wide variety. The effectiveness of the proposed approach has been demonstrated to study the refractive indices and melting points of various inorganic compounds - components of potential optical film-forming materials. The developed QSPR models are suitable for the evaluative virtual screening of inorganic compounds; the mean relative error of prediction is 6 - 15%. The interpretation of the developed models reflects the nature of interatomic interactions in compounds with ionic structure.

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Zuas, Oman. "WHIM-3D-QSPR APPROACH FOR PREDICTING AQUEOUS SOLUBILITY OF CHLORINATED HYDROCARBONS." Indonesian Journal of Chemistry 8, no. 1 (June 17, 2010): 65–71. http://dx.doi.org/10.22146/ijc.21650.

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The weighted holistic invariant molecular-three dimensional-quantitative structure property relationship (WHIM-3D-QSPR) approach has been applied to the study of the aqueous solubility (- log Sw) of chlorinated hydrocarbon compounds (CHC's). The obtained QSPR model is predictive and only requires four WHIM-3D descriptors in the calculation. The correlation equation of the model that is based on a training set of 50 CHC's compound has statistical parameters: standard coefficient correlation (R2) = 0.948; cross-validated correlation coefficients (Q2) = 0.935; Standard Error of Validation (SEV) = 0.35; and average absolute error (AAE) = 0.31. The application of the best model to a testing set of 50 CHC's demonstrates a reliable result with good predictability. Besides, it was possible to construct new model by applying WHIM-3D-QSPR approach without require any experimental physicochemical properties in the calculation of aqueous solubility. Keywords: WHIM-3D; QSPR; aqueous solubility; - Log Sw, chlorinated hydrocarbons, CHC's

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Osipov, Alexander Leonidovich, Veronica Pavlovna Trushina, and Fedor Leonidovich Osipov. "QSPR МОДЕЛИРОВАНИЕ ТЕПЛОЕМКОСТИ АЛЬДЕГИДОВ." Siberian Journal of Life Sciences and Agriculture 12, no. 1 (March 12, 2020): 92. http://dx.doi.org/10.12731/2658-6649-2020-12-1-92-97.

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В статье исследуются QSPR модели предсказания теплоемкости химических веществ семейства альдегидов. Исследование параметра теплоемкости осуществляется с помощью разработанных моделей с использованием следующих факторов: топологических индексов; структурных дескрипторов; информационного индекса, связанного с функцией Шеннона. Проведены вычислительные эксперименты, показывающие высокую эффективность предложенных QSPR зависимостей.

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Faramarzi, Zohreh, Fatemeh Abbasitabar, Jalali Jahromi, and Maziar Noei. "New structure-based models for the prediction of normal boiling point temperature of ternary azeotropes." Journal of the Serbian Chemical Society 86, no. 7-8 (2021): 685–98. http://dx.doi.org/10.2298/jsc210218035f.

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Recently, development of the QSPR models for mixtures has received much attention. The QSPR modelling of mixtures requires the use of the appropriate mixture descriptors. In this study, 12 mathematical equations were considered to compute mixture descriptors from the individual components for the prediction of normal boiling points of 78 ternary azeotropic mixtures. Multiple linear regression (MLR) was employed to build all QSPR models. Memorized_ ACO algorithm was employed for subset variable selection. An ensemble model was also constructed using averaging strategy to improve the predictability of the final QSAR model. The models have been validated by a test set comprised of 24 ternary azeotropes and by different statistical tests. The resulted ensemble QSPR model had R2 training, R2 test and q2 of 0.97, 0.95, and 0.96, respectively. The mean absolute error (MAE), as a good indicator of model performance, were found to be 3.06 and 3.52 for training and testing sets, respectively.

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Hosamani, Sunilkumar M., Bhagyashri B. Kulkarni, Ratnamma G. Boli, and Vijay M. Gadag. "QSPR Analysis of Certain Graph Theocratical Matrices and Their Corresponding Energy." Applied Mathematics and Nonlinear Sciences 2, no. 1 (April 25, 2017): 131–50. http://dx.doi.org/10.21042/amns.2017.1.00011.

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AbstractIn QSAR/QSPR study, topological indices are utilized to guess the bioactivity of chemical compounds. In this paper, we study the QSPR analysis of certain graph theocratical matrices and their corresponding energy. Our study reveals some important results which helps to characterize the useful topological indices based on their predicting power.

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Shirakol, Shailaja, Manjula Kalyanshetti, and Sunilkumar M. Hosamani. "QSPR Analysis of certain Distance Based Topological Indices." Applied Mathematics and Nonlinear Sciences 4, no. 2 (September 27, 2019): 371–86. http://dx.doi.org/10.2478/amns.2019.2.00032.

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AbstractIn QSAR/QSPR study, topological indices are utilized to guess the bioactivity of chemical compounds. In this paper, we study the QSPR analysis of selected distance and degree-distance based topological indices. Our study reveals some important results which help us to characterize the useful topological indices based on their predicting power.

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Nguyen Minh, Quang, An Tran Nguyen Minh, and Tat Pham Van. "Development of new metal-thiosemicarbazone complexes using visual screening methods and in silico models." Vietnam Journal of Catalysis and Adsorption 10, no. 1S (October 15, 2021): 302–10. http://dx.doi.org/10.51316/jca.2021.096.

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The stability constants (logβ11) of forty-two new metal-thiosemicarbazone complexes were predicted based on the results of the quantitative structure-property relationship (QSPR). The QSPR models were developed from 88 logb11 values of experimental complexes by using the multivariate linear regression (QSPRMLR) and artificial neural network (QSPRANN). Four descriptors such as xch9, xv0, core-core repulsion and cosmo area were found out in the best of the linear model QSPRMLR which was harshly evaluated by the statistical values: R2train = 0.864, Q2LOO = 0.840, SE = 0.711, Fstat = 131,355 and PRESS = 49.31. Furthermore, the artificial neural network model QSPRANN with architecture I(4)-HL(5)-O(1) was discovered with the same variables of the QSPRMLR model that the statistical results were extremely impressive as R2train = 0.970, Q2CV = 0.984 and Q2test = 0.974. Also, both of the QSPR models were externally validated on the data set of 18 logb11 values of independently experimental complexes. As a consequence, the results from the QSPR models could be used to calculate the stability constants of other new metal-thiosemicarbazones.

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Mostashari-Rad, Tahereh, Roya Arian, Houri Sadri, Alireza Mehridehnavi, Marzieh Mokhtari, Fahimeh Ghasemi, and Afshin Fassihi. "Study of CXCR4 chemokine receptor inhibitors using QSPR and molecular docking methodologies." Journal of Theoretical and Computational Chemistry 18, no. 04 (June 2019): 1950018. http://dx.doi.org/10.1142/s0219633619500184.

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CXCR4 is involved in inflammation, cancer metastasis and also HIV-1 entry into immune host cells. In the present research, it was decided to investigate the efficacy of some CXCR4 inhibitors from both pharmacokinetics and pharmacodynamics points of view. Quantitative structure–property relationship (QSPR) approach was applied to model the metabolic stability and instability of the compounds. Using QSPR modeling, it was tried to predict the metabolic stability using new hybrid algorithm which consisted of three different steps: descriptor reduction (PCA), stable–instable classification (KNN) and biological stability prediction (PLS). In the QSPR step, it is shown that the descriptor reduction (PCA) affects the result of the classification procedure (KNN). Besides, the obtained QSPR model can predict the metabolic stability of the stable compounds with [Formula: see text] of 0.98 for train data and of 0.64 for test data. In other words, increment and decrement of stability were followed by the model. Molecular docking simulation was exploited to define the essential interactions of an effective inhibitor with CXCR4 receptor.

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Begum, Sanija, and P. Ganga Raju Achary. "Optimal Descriptor Based QSPR Models for Catalytic Activity of Propylene Polymerization." International Journal of Quantitative Structure-Property Relationships 3, no. 2 (July 2018): 36–48. http://dx.doi.org/10.4018/ijqspr.2018070103.

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A heterogeneous Ziegler–Natta (ZN) catalyst is an important catalyst in the field of the polypropylene polymerization industry. The role of electron donors has been crucial in the ZN catalyzed polypropylene polymerization process. In this article, quasi-SMILES-based QSPR models are elaborated for the prediction of catalytic activities. The representations of the molecular structure by quasi-simplified molecular input line entry system were the basis to build the desired QSPR model. These models were developed by means of the Monte Carlo optimization involving the available methods classic scheme (CS), balance of correlations (BC) and balance of correlation with ideal slopes (BCIS). The best QSPR model showed r2 = 0.813 (for external validation set), rm2 (avg)=0.73 and ∆rm2= 0.03.

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Toropov, Andrey A., and Alla P. Toropova. "QSPR/QSAR: State-of-Art, Weirdness, the Future." Molecules 25, no. 6 (March 12, 2020): 1292. http://dx.doi.org/10.3390/molecules25061292.

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Ability of quantitative structure–property/activity relationships (QSPRs/QSARs) to serve for epistemological processes in natural sciences is discussed. Some weirdness of QSPR/QSAR state-of-art is listed. There are some contradictions in the research results in this area. Sometimes, these should be classified as paradoxes or weirdness. These points are often ignored. Here, these are listed and briefly commented. In addition, hypotheses on the future evolution of the QSPR/QSAR theory and practice are suggested. In particular, the possibility of extending of the QSPR/QSAR problematic by searching for the “statistical similarity” of different endpoints is suggested and illustrated by an example for relatively “distanced each from other” endpoints, namely (i) mutagenicity, (ii) anticancer activity, and (iii) blood–brain barrier.

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Fu, Li Ya, Jin Luo, and Ji Wei Hu. "A Quantitative Structure-Property Relationship Study on Photodegradation of Polybrominated Diphenyl Ethers." Advanced Materials Research 546-547 (July 2012): 48–53. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.48.

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Quantitative structure-property relationship (QSPR) models were developed in the present work for photodegradation rate constants (kp) of fifteen individual polybrominated diphenyl ethers (PBDEs) in methanol/water (8:2) by UV light in the sunlight region. The molecular descriptors used in the QSPR models were calculated by the two semi-empirical quantum mechanical methods, RM1 and PM6, respectively. Both multiple linear regression (MLR) and artificialneural network (ANN) were applied in this study. The statistic qualities of the MLR models based on the molecular parameters obtained by RM1 and PM6 calculations were both good with the R values of 0.987 and 0.990, respectively. The QSPR model built by the ANN method with the molecular parameters calculated with PM6 is slightly better than that with RM1.

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Asok, Anjusha, and Joseph Varghese Kureethara. "The QSPR Study of Butane derivatives: (A Mathematical Approach)." Oriental Journal of Chemistry 34, no. 4 (July 31, 2018): 1842–46. http://dx.doi.org/10.13005/ojc/3404018.

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The QSPR analysis provides a significant structural insight into the physiochemical properties of Butane derivatives. We study some physiochemical properties of fourteen Butane derivatives and develop a QSPR model using four topological indices and Butane derivatives. Here we analyze how closely the topological indices are related to the physiochemical properties of Butane derivatives. For this we compute analytically the topological indices of Butane derivatives and plot the graphs between each of these topological indices to the properties of Butane derivatives using Origin. This QSPR model exhibits a close correlation between Heavy atomic count, Complexity, Hydrogen bond acceptor count, and Surface tension of Butane derivatives with the Redefined first Zagreb index, the Redefined third Zagreb index, the Sum connectivity index and the Reformulated first Zagreb index, respectively.

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Rücker, Christoph, Markus Meringer, and Adalbert Kerber. "QSPR Using MOLGEN-QSPR: The Challenge of Fluoroalkane Boiling Points." Journal of Chemical Information and Modeling 45, no. 1 (January 2005): 74–80. http://dx.doi.org/10.1021/ci0497298.

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Rücker, Christoph, Markus Meringer, and Adalbert Kerber. "QSPR Using MOLGEN-QSPR: The Example of Haloalkane Boiling Points." Journal of Chemical Information and Computer Sciences 44, no. 6 (November 2004): 2070–76. http://dx.doi.org/10.1021/ci049802u.

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LU, GUI-NING, ZHI DANG, XUE-QIN TAO, PING-AN PENG, and DE-CONG ZHANG. "QSPR STUDY ON DIRECT PHOTOLYSIS HALF-LIVES OF PAHs IN WATER SURFACE." Journal of Theoretical and Computational Chemistry 04, no. 03 (September 2005): 811–22. http://dx.doi.org/10.1142/s0219633605001817.

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Quantitative structure-property relationship (QSPR) modeling is a helpful approach used to correlate the properties of pollutants with their structure descriptors. In this paper a QSPR model for direct photolysis half-lives of polycyclic aromatic hydrocarbons (PAHs) under sunlight on the water surface was developed using density functional theory (DFT) and direct photolysis half-lives of seven PAHs without reported observed values were predicted. The quantum chemical descriptors used in this study were computed at the level of B3LYP/6–311+G(d) and analyzed by partial least squares (PLS) method. The obtained QSPR model with a correlation coefficient of 0.963 was more significant than that derived from semi-empirical molecular orbital algorithm in literatures. It was found that the eigenvalues of the frontier molecular orbital (E HOMO , E LUMO , E NLUMO and E NHOMO ) are important in governing the photolysis half-lives of PAHs in water surface, while the molecular weight (MW) and molecular total energy (TE) also have great effects on photolysis half-lives. The importance of E NLUMO and E NHOMO in the model complicates the photolytic mechanism of PAHs and they might become two useful descriptors in QSPR study on photolysis.

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Raevsky, Oleg A., Veniamin Y. Grigorev, Daniel E. Polianczyk, Olga E. Raevskaja, and John C. Dearden. "Aqueous Drug Solubility: What Do We Measure, Calculate and QSPR Predict?" Mini-Reviews in Medicinal Chemistry 19, no. 5 (February 21, 2019): 362–72. http://dx.doi.org/10.2174/1389557518666180727164417.

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Detailed critical analysis of publications devoted to QSPR of aqueous solubility is presented in the review with discussion of four types of aqueous solubility (three different thermodynamic solubilities with unknown solute structure, intrinsic solubility, solubility in physiological media at pH=7.4 and kinetic solubility), variety of molecular descriptors (from topological to quantum chemical), traditional statistical and machine learning methods as well as original QSPR models.

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Poulin, Patrick, and Kannan Krishnan. "Molecular Structure-Based Prediction of the Toxicokinetics of Inhaled Vapors in Humans." International Journal of Toxicology 18, no. 1 (January 1999): 7–18. http://dx.doi.org/10.1080/109158199225756.

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The objectives of the present study were: (1) to evaluate the adequacy of setting hepatic extraction ratio (E) equal to 0 or 1 in physiologically based toxicokinetic (PBTK) models to generate the theoretically plausible envelope of venous blood concentration (Cv) profiles, and (2) to couple this approach with molecular structure-based estimation of blood:air and tissue: blood partition coefficients (PCs) to predictthe Cv profiles of volatile organic chemicals (VOCs) in humans. Setting E= 0 or 1 in PBTK models provided simulations of Cv envelopes that contained the Cv values determined in humans exposed to low concentrations of dichloromethane(DCM), ethylbenzene (EBZ), toluene (TOL), m-xylene(XYL), trichloroethy-lene (TCE), and 1,1,1-trichloroethane(TRI). Following the validation of using E= 0 or 1 in conventional PBTK models to predict the theoretically plausible envelope of Cv, a quantitative structure-toxicokinetic relationship (QSTkR) model was constructed. The QSTkR model used molecular structure information as the sole input to predict the PCs and considered E= 0 or 1 to generate simulations of the envelope of Cv. The experimental data on Cv were in most cases within the envelopes simulated using QSTkR models for DCM, EBZ, TOL, and XYL, but were outside the envelopes for TCE and TRI. The discrepancy observed between the Cv envelopes obtained using PBTK and QSTkR models can be explained by the fact that blood:air PCs of some VOCs were under-predicted while using molecular structure information. The modeling framework presented in this article represents the first animal-replacement tool that can provide a priori predictions of the toxicokinetic profiles of VOCs prior to laboratory experiments.

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Pei, J. F., C. Z. Cai, X. J. Zhu, G. L. Wang, and B. Yan. "Prediction of Glass Transition Temperature of Polymer by Support Vector Regression." Advanced Materials Research 455-456 (January 2012): 436–42. http://dx.doi.org/10.4028/www.scientific.net/amr.455-456.436.

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. Based on two quantum chemical descriptors (the thermal energy Ethermal and the total energy of the whole system EHF) calculated from the structures of the repeat units of polyacrylamides by density functional theory (DFT), the support vector regression (SVR) approach combined with particle swarm optimization (PSO), is proposed to establish a model for prediction of the glass transition temperature (Tg) of polyacrylamides. The prediction performance of SVR was compared with that of multivariate linear regression (MLR). The results show that the mean absolute error (MAE=4.65K), mean absolute percentage error (MAPE=1.28%) and correlation coefficient (R2=0.9818) calculated by leave-one–out cross validation (LOOCV) via SVR models are superior to those achieved by QSPR (MAE=14.25K, MAPE=4.39% and R2=0.9211) and QSPR-LOO (MAE=17.01K, MAPE=5.66% and R2=0.8823) models for the identical samples, respectively. The prediction results strongly demonstrate that the modeling and generalization abilities of SVR model consistently surpass those of QSPR and QSPR-LOO models. It is revealed that the established SVR model is more suitable to be used for prediction of the Tg values for unknown polymers possessing similar structure than the conventional MLR approach. These suggest that SVR is a promising and practical methodology to predict the glass transition temperature of polyacrylamides.

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Palaz, Selami, Baki Türkkan, and Erol Eroğlu. "A QSPR Study for the Prediction of the pKa of N-Base Ligands and Formation Constant Kc of Bis(2,2′-bipyridine)Platinum(II)-N-Base Adducts Using Quantum Mechanically Derived Descriptors." ISRN Physical Chemistry 2012 (October 15, 2012): 1–11. http://dx.doi.org/10.5402/2012/260171.

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Quantitative structure-property relationship (QSPR) study on the acid dissociation constant, pKa of various 22 N-base ligands including pyridines, pyrimidines, purines, and quinolines has been carried out using Codessa Pro methodology and software. In addition, the formation constant, Kc of these ligands with Pt(II)(bpy)2 2+ (bpy = 2,2′-bipyridine) ion has also been modelled with the same methodology. Linear regression QSPR models of pKa and Kc were established with descriptors derived from AM1 calculations. Among the obtained QSPR models of pKa presented in the study, statistically the most significant one is a four parameters linear equation with the squared correlation coefficient, R2 values of ca. 0.95 and the squared cross-validated correlation coefficient, Rcv2 values of ca. 0.89, and external the squared correlation coefficient, Rext.2 values of ca. 0.97. Statistically the most significant QSPR model of Kc is also a four parameters linear equation with the squared correlation coefficient, R2 values of ca. 0.75 and the squared cross-validated correlation coefficient, Rcv2 values of ca. 0.55, and external the squared correlation coefficient, Rext.2 values of ca. 0.81. An analysis of descriptors that involved in the pKa models indicate that reactivity index and charge distribution related descriptors play major roles to model acid dissociation constant of ligands of N bases.

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Jiao, Long. "QSPR Studies on n-Octanol/Water Partition Coefficient of Polychlorinated Biphenyls by Using Artificial Neural Network." Advanced Materials Research 455-456 (January 2012): 925–29. http://dx.doi.org/10.4028/www.scientific.net/amr.455-456.925.

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Quantitative structure property relationship (QSPR) model for predicting the n-octanol/water partition coefficient, Kow, of 21 polychlorinated biphenyls (PCBs) was investigated. The structure of the investigated PCBs is mathematically characterized by using molecular distance-edge vector (MDEV) index, a topological index which is developed based on the topological method. The calibration model of Kow was developed by using radial basis function artificial neural network (RBF ANN). Leave one out cross validation was carried out to assess the predictive ability of the developed QSPR model. The R2 between the predicted and experimental logKow is 0.9793. The prediction RMS%RE for the 21 PCBs is 1.92. It is demonstrated that there is a quantitative relationship between the MDEV index and the Kow of the 21 PCBs. RBF ANN is shown to practicable for developing the QSPR model for Kow of PCBs.

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Nikolic, Katarina, Mara Aleksic, Vera Kapetanovic, and Danica Agbaba. "Voltammetric and theoretical studies of electrochemical behavior of cephalosporins at the mercury electrode." Journal of the Serbian Chemical Society 80, no. 8 (2015): 1035–49. http://dx.doi.org/10.2298/jsc150129019n.

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Study of the adsorption and electroreduction behavior of cefpodoxime proxetil, cefotaxime, desacetylcefotaxime, cefetamet, ceftriaxone, ceftazidime, and cefuroxime axetile at the mercury electrode surface has been performed using Cyclic (CV), Differential Pulse (DPV), and Adsorptive Stripping Differential Pulse Voltammetry (AdSDPV). The Quantitative Structure Property Relationship (QSPR) study of the seven cephalosporins adsorption at the mercury electrode has been based on the density functional theory DFT-B3LYP/6-31G (d,p) calculations of molecular orbitals, partial charges and electron densities of analytes. The DFT-parameters and QSPR model explain well the process of adsorption of the examined cephalosporins. QSPR study defined that cefalosporins with lower charge of sulphur in the thiazine moiety, lower electron density on the nitrogen atom of the N-O bond, higher number of hydrogen bond accepting groups, and higher principal moment of inertia should express high adsorption on the mercury electrode.

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Costa, Paulo C. S., Joel S. Evangelista, Igor Leal, and Paulo C. M. L. Miranda. "Chemical Graph Theory for Property Modeling in QSAR and QSPR—Charming QSAR & QSPR." Mathematics 9, no. 1 (December 29, 2020): 60. http://dx.doi.org/10.3390/math9010060.

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Quantitative structure-activity relationship (QSAR) and Quantitative structure-property relationship (QSPR) are mathematical models for the prediction of the chemical, physical or biological properties of chemical compounds. Usually, they are based on structural (grounded on fragment contribution) or calculated (centered on QSAR three-dimensional (QSAR-3D) or chemical descriptors) parameters. Hereby, we describe a Graph Theory approach for generating and mining molecular fragments to be used in QSAR or QSPR modeling based exclusively on fragment contributions. Merging of Molecular Graph Theory, Simplified Molecular Input Line Entry Specification (SMILES) notation, and the connection table data allows a precise way to differentiate and count the molecular fragments. Machine learning strategies generated models with outstanding root mean square error (RMSE) and R2 values. We also present the software Charming QSAR & QSPR, written in Python, for the property prediction of chemical compounds while using this approach.

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ALEKSEEV, K. S., N. M. BARBIN, A. V. KALACH, and E. V, KALACH. "APPLICATION QSPR FOR PREDICTING FLASH POINTS OF ALCOHOLS." ПОЖАРОВЗРЫВОБЕЗОПАСНОСТЬ 23, no. 1 (2014): 21–24. http://dx.doi.org/10.18322/pvb.2014.23.1.21-24.

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Lu, Gui-Ning, Xue-Qin Tao, Zhi Dang, Xiao-Yun Yi, and Chen Yang. "Estimation of n-octanol/water partition coefficients of polycyclic aromatic hydrocarbons by quantum chemical descriptors." Open Chemistry 6, no. 2 (June 1, 2008): 310–18. http://dx.doi.org/10.2478/s11532-008-0010-y.

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AbstractQuantitative structure-property relationship (QSPR) modeling is a powerful approach for predicting environmental behavior of organic pollutants with their structure descriptors. This study reports an optimal QSPR model for estimating logarithmic n-octanol/water partition coefficients (log K OW) of polycyclic aromatic hydrocarbons (PAHs). Quantum chemical descriptors computed with density functional theory at B3LYP/6-31G(d) level and partial least squares (PLS) analysis with optimizing procedure were used for generating QSPR models for log K OW of PAHs. The squared correlation coefficient (R 2) of the optimal model was 0.990, and the results of crossvalidation test (Q 2cum=0.976) showed this optimal model had high fitting precision and good predictability. The log K OW values predicted by the optimal model are very close to those observed. The PLS analysis indicated that PAHs with larger electronic spatial extent and lower total energy values tend to be more hydrophobic and lipophilic.

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Moussaoui, Mohammed, Maamar Laidi, Salah Hanini, and Mohamed Hentabli. "Artificial Neural Network and Support Vector Regression Applied in Quantitative Structure-property Relationship Modelling of Solubility of Solid Solutes in Supercritical CO2." Kemija u industriji 69, no. 11-12 (2020): 611–30. http://dx.doi.org/10.15255/kui.2020.004.

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In this study, the solubility of 145 solid solutes in supercritical CO2 (scCO2) was correlated using computational intelligence techniques based on Quantitative Structure-Property Relationship (QSPR) models. A database of 3637 solubility values has been collected from previously published papers. Dragon software was used to calculate molecular descriptors of 145 solid systems. The genetic algorithm (GA) was implemented to optimise the subset of the significantly contributed descriptors. The overall average absolute relative deviation MAARD of about 1.345 % between experimental and calculated values by support vector regress SVR-QSPR model was obtained to predict the solubility of 145 solid solutes in supercritical CO2, which is better than that obtained using ANN-QSPR model of 2.772 %. The results show that the developed SVR-QSPR model is more accurate and can be used as an alternative powerful modelling tool for QSAR studies of the solubility of solid solutes in supercritical carbon dioxide (scCO2). The accuracy of the proposed model was evaluated using statistical analysis by comparing the results with other models reported in the literature.

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Khabzina, Y., C. Laroche, J. Pérez-Pellitero, and D. Farrusseng. "Quantitative structure–property relationship approach to predicting xylene separation with diverse exchanged faujasites." Physical Chemistry Chemical Physics 20, no. 36 (2018): 23773–82. http://dx.doi.org/10.1039/c8cp04042g.

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Hirjani, Hirjani, Mudasir Mudasir, and Harno Dwi Pranowo. "Prediction of High Performance Liquid Chromatography Retention Time for Some Organic Compounds Based on Ab initio QSPR Study." Acta Chimica Asiana 1, no. 1 (January 10, 2018): 24. http://dx.doi.org/10.29303/aca.v1i1.6.

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Analysis of the quantitative relationship between structure and characteristics of 18 polyaromatic hydrocarbons has been done by quantum Ab initio Quantitative Structure-Property Relationship (QSPR) study at Hartree Fock level of theory. Moment dipole was used as the quantum chemical descriptors, whereas molecular weight and number of rings were applied for constitutional descriptor and the valence connectivity index as steric descriptors. The compound’s electronic structure was studied by molecular modeling and retention time (Tr) data were obtained from the literature. Multi-linear regression analysis has been performed by randomly splitting the initial data set into on fitting data set and a test data set. The best result provided by QSPR analysis is the following model equation: log tR = 1.276 + 0.016MW+0.323Rc-0.423χ1-0.147χ2 with n =18 r=0.917 r2 =0.841 SE=0.182 Fcalc/Ftable = 5.408. The retention times of PAH compounds with two and three rings were successfully predicted by QSPR models.

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FATEMI, MOHAMMAD H., and PARISA IZADIAN. "IN SILICO PREDICTION OF MELTING POINTS OF IONIC LIQUIDS BY USING MULTILAYER PERCEPTRON NEURAL NETWORKS." Journal of Theoretical and Computational Chemistry 11, no. 01 (February 2012): 127–41. http://dx.doi.org/10.1142/s0219633612500083.

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Quantitative structure–property relationship (QSPR) was used to predict melting points of 62 ionic liquids (ILs), which include ammonium, pyrrolidiniu, imidazolium, pyridiniu, piperidiniu, phosphonium ionic liquid salts. The structures of ionic liquids were optimized by Hyperchem software and MOPAC program, and stepwise multiple linear regression method was applied to select the relevant structural descriptors. The predicting models correlating selected descriptors and melting points were set up using multiple linear regressions (MLR) and multilayer perceptron neural network (MLP NN), separately. The obtained linear and nonlinear QSPR models were validated by internal and external test sets. According to the obtained results, the correlation coefficients between predicted and experimental melting points for training, test and validation sets were; 0.91, 0.86 and 0.79 for MLR model. These values for MLP NN model were; 0.97, 0.96 and 0.85, respectively. The results of this study revealed the high applicability of QSPR approach to melting point prediction of ILs.

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Sharma, Charu, Thirumurthy Velpandian, Nihar Ranjan Biswas, Niranjan Nayak, Rasik Bihari Vajpayee, and Supriyo Ghose. "Development of NovelIn SilicoModel to Predict Corneal Permeability for Congeneric Drugs: A QSPR Approach." Journal of Biomedicine and Biotechnology 2011 (2011): 1–11. http://dx.doi.org/10.1155/2011/483869.

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This study was undertaken to determinein vivopermeability coefficients for fluoroquinolones and to assess its correlation with the permeability derived using reported models in the literature. Further, the aim was to develop novel QSPR model to predict corneal permeability for fluoroquinolones and test its suitability on other training sets. Thein vivopermeability coefficient was determined using cassette dosing (N-in-One) approach for nine fluoroquinolones (norfloxacin, ciprofloxacin, lomefloxacin, ofloxacin, levofloxacin, sparfloxacin, pefloxacin, gatifloxacin, and moxifloxacin) in rabbits. The correlation between corneal permeability derived usingin vivostudies with that derived from reported models was determined. Novel QSPR-based model was developed usingin vivocorneal permeability along with other molecular descriptors. The suitability of developed model was tested onβ-blockers (n=15). The model showed better prediction of corneal permeability for fluoroquinolones(r2>0.9)as well asβ-blockers(r2>0.6). The newly developed QSPR model based uponin vivogenerated data was found suitable to predict corneal permeability for fluoroquinolones as well as other sets of compounds.

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Journal articles on the topic 'QSPkR'

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