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Journal articles on the topic 'QSPR'

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Author: Grafiati

Published: 4 June 2021

Last updated: 22 February 2023

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1

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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2

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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3

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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Zhang, Xiujun, H. G. Govardhana Reddy, Arcot Usha, M. C. Shanmukha, Mohammad Reza Farahani, and Mehdi Alaeiyan. "A study on anti-malaria drugs using degree-based topological indices through QSPR analysis." Mathematical Biosciences and Engineering 20, no. 2 (2022): 3594–609. http://dx.doi.org/10.3934/mbe.2023167.

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<abstract> <p>The use of topological descriptors is the key method, regardless of great advances taking place in the field of drug design. Descriptors portray the chemical characteristic of a molecule in numerical form, that is used for QSAR/QSPR models. The numerical values related with chemical constitutions that correlates the chemical structure with the physical properties referto topological indices. The study of chemical structure with chemical reactivity or biological activity is termed as quantitative structure activity relationship, in which topological index play a significant role. Chemical graph theory is one such significant branches of science which play a key role in QSAR/QSPR/QSTR studies. This work is focused on computing various degree-based topological indices and regression model of nine anti-malaria drugs. Regression models are fitted for computed indices values with 6 physicochemical properties of the anti-malaria drugs are studied. Based on the results obtained, an analysis is carried out for various statistical parameters for which conclusions are drawn.</p> </abstract>

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5

Rasulev, Bakhtiyor, and Gerardo Casanola-Martin. "QSAR/QSPR in Polymers." International Journal of Quantitative Structure-Property Relationships 5, no. 1 (January 2020): 80–88. http://dx.doi.org/10.4018/ijqspr.2020010105.

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Predictive modeling of the properties of polymers and polymeric materials is getting more attention, while it is still very complicated due to complexity of these materials. In this review, we discuss main applications of quantitative structure-property/activity relationships (QSPR/QSAR) methods for polymers published recently. The most relevant publications are discussed covering this field highlighting the main advantages and drawbacks of the obtained predictive models. Examples dealing with refractive index, glass transition temperatures, intrinsic viscosity, thermal decomposition and flammability properties are shown, together with a fouling-release activity study. Finally, some considerations are discussed in order to give some clues that could lead to the improvement in the efficient computational design and/or optimization of new polymers with enhanced properties/activities.

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6

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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7

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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8

Karelson, Mati, Uko Maran, Yilin Wang, and Alan R. Katritzky. "QSPR and QSAR Models Derived Using Large Molecular Descriptor Spaces. A Review of CODESSA Applications." Collection of Czechoslovak Chemical Communications 64, no. 10 (1999): 1551–71. http://dx.doi.org/10.1135/cccc19991551.

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An overview on the development of QSPR/QSAR equations using various descriptor-mining techniques and multilinear regression analysis in the framework of the CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) program is given. The description of the methodologies applied in CODESSA is followed by the presentation of the QSAR and QSPR models derived for eighteen molecular activities and properties. The properties cover single molecular species, interactions between different molecular species, properties of surfactants, complex properties and properties of polymers. A review with 54 references.

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9

Jorgensen, William L. "QSAR/QSPR and Proprietary Data." Journal of Chemical Information and Modeling 46, no. 3 (May 2006): 937. http://dx.doi.org/10.1021/ci0680079.

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10

Li, Yi-Xia, Abdul Rauf, Muhammad Naeem, Muhammad Ahsan Binyamin, and Adnan Aslam. "Valency-Based Topological Properties of Linear Hexagonal Chain and Hammer-Like Benzenoid." Complexity 2021 (April 22, 2021): 1–16. http://dx.doi.org/10.1155/2021/9939469.

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Topological indices are quantitative measurements that describe a molecule’s topology and are quantified from the molecule’s graphical representation. The significance of topological indices is linked to their use in QSPR/QSAR modelling as descriptors. Mathematical associations between a particular molecular or biological activity and one or several biochemical and/or molecular structural features are QSPRs (quantitative structure-property relationships) and QSARs (quantitative structure-activity relationships). In this paper, we give explicit expressions of two recently defined novel ev-degree- and ve-degree-based topological indices of two classes of benzenoid, namely, linear hexagonal chain and hammer-like benzenoid.

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11

Toropov, Andrey A., and Alla P. Toropova. "The Monte Carlo Method as a Tool to Build up Predictive QSPR/QSAR." Current Computer-Aided Drug Design 16, no. 3 (June 2, 2020): 197–206. http://dx.doi.org/10.2174/1573409915666190328123112.

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Background: The Monte Carlo method has a wide application in various scientific researches. For the development of predictive models in a form of the quantitative structure-property / activity relationships (QSPRs/QSARs), the Monte Carlo approach also can be useful. The CORAL software provides the Monte Carlo calculations aimed to build up QSPR/QSAR models for different endpoints. Methods: Molecular descriptors are a mathematical function of so-called correlation weights of various molecular features. The numerical values of the correlation weights give the maximal value of a target function. The target function leads to a correlation between endpoint and optimal descriptor for the visible training set. The predictive potential of the model is estimated with the validation set, i.e. compounds that are not involved in the process of building up the model. Results: The approach gave quite good models for a large number of various physicochemical, biochemical, ecological, and medicinal endpoints. Bibliography and basic statistical characteristics of several CORAL models are collected in the present review. In addition, the extended version of the approach for more complex systems (nanomaterials and peptides), where behaviour of systems is defined by a group of conditions besides the molecular structure is demonstrated. Conclusion: The Monte Carlo technique available via the CORAL software can be a useful and convenient tool for the QSPR/QSAR analysis.

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12

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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13

Toropova, Alla P., and Andrey A. Toropov. "Evolution of Optimal Descriptors." International Journal of Quantitative Structure-Property Relationships 1, no. 2 (July 2016): 52–71. http://dx.doi.org/10.4018/ijqspr.2016070103.

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The quantitative structure - property / activity relationships (qsprs/qsars) analysis of different substances is an important area in mathematical and medicinal chemistry. The evolution and logic of optimal descriptors which are based on the monte carlo technique in the role of a tool of the qspr/qsar analysis is discussed. A group of examples of application of the optimal descriptors which are calculated with the coral software (http://www.insilico.eu/coral) for prediction of physicochemical and biochemical endpoints of potential therapeutical agents are presented. The perspectives and limitations of the optimal descriptors are listed. The attempt of the systematization of the models calculated with the coral software is the aim of this work.

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14

Mauri, Andrea, and Matteo Bertola. "Alvascience: A New Software Suite for the QSAR Workflow Applied to the Blood–Brain Barrier Permeability." International Journal of Molecular Sciences 23, no. 21 (October 25, 2022): 12882. http://dx.doi.org/10.3390/ijms232112882.

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Quantitative structure–activity relationship (QSAR) and quantitative structure–property relationship (QSPR) are established techniques to relate endpoints to molecular features. We present the Alvascience software suite that takes care of the whole QSAR/QSPR workflow necessary to use models to predict endpoints for untested molecules. The first step, data curation, is covered by alvaMolecule. Features such as molecular descriptors and fingerprints are generated by using alvaDesc. Models are built and validated with alvaModel. The models can then be deployed and used on new molecules by using alvaRunner. We use these software tools on a real case scenario to predict the blood–brain barrier (BBB) permeability. The resulting predictive models have accuracy equal or greater than 0.8. The models are bundled in an alvaRunner project available on the Alvascience website.

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15

Sizochenko, Natalia, and Jerzy Leszczynski. "Review of Current and Emerging Approaches for Quantitative Nanostructure-Activity Relationship Modeling." Journal of Nanotoxicology and Nanomedicine 1, no. 1 (January 2016): 1–16. http://dx.doi.org/10.4018/jnn.2016010101.

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Quantitative structure-activity/property relationships (QSAR/QSPR) approaches that have been applied with success in a number of studies are currently used as indispensable tools in the computational analysis of nanomaterials. Evolution of nano-QSAR methodology to the ranks of novel field of knowledge has resulted in the development of new so-called “nano-descriptors” and extension of the statistical approaches domain. This brief review focuses on the critical analysis of advantages and disadvantages of existing methods of nanoparticles' representation and their analysis in framework of structure-activity relationships. It summarizes recent QSAR/QSPR studies on inorganic nanomaterials. Here the authors describe practices for the QSAR modeling of inorganic nanoparticles, existing datasets, and discuss applicable descriptors and future perspectives of this field. About 50 different (Q)SAR/SPR models for inorganic nanomaterials have been developed during the past 5 years. An analysis of these peer reviewed publications shows that the most popular property of nanoparticles modeled via QSAR is their toxicity towards different bacteria, cell lines, and microorganisms. It has been clearly shown how nano-QSAR can contribute to the elucidation of toxicity mechanisms and different physical properties of inorganic nanomaterials.

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16

Chen, Xuan, Chang Ming Nie, and Song Nian Wen. "QSPR/QSAR Study of Mercaptans by Quantum Topological Method." Advanced Materials Research 233-235 (May 2011): 2536–40. http://dx.doi.org/10.4028/www.scientific.net/amr.233-235.2536.

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A new molecular quantum topological index QT was constructed by molecular topological methods and quantum mechanics (QM), which together with Gibbs free energy(G), Constant volume mole hot melting(CV) that were calculated by density functional theory (DFT) at the B3LYP/6-31G(d) level of theory for mercaptans. Index QT can not only efficiently distinguish molecular structures of mercaptans, but also possess good applications of QSPR/QSAR (quantitative structure-property/activity relationships). And most of the correlation coefficients of the models were over 0.99. The LOO CV (leave-one-out cross-validation) method was used to testify the stability and predictive ability of the models. The validation results verified the good stability and predictive ability of the models employing the cross-validation parameters: RCV, SCVand FCV, which demonstrated the wide potential of the index QT for applications to QSPR/ QSAR.

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17

Xu, Peng, Mehran Azeem, Muhammad Mubashir Izhar, Syed Mazhar Shah, Muhammad Ahsan Binyamin, and Adnan Aslam. "On Topological Descriptors of Certain Metal-Organic Frameworks." Journal of Chemistry 2020 (November 12, 2020): 1–12. http://dx.doi.org/10.1155/2020/8819008.

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Topological indices are numerical numbers that represent the topology of a molecule and are calculated from the graphical depiction of the molecule. The importance of topological indices is due to their use as descriptors in QSPR/QSAR modeling. QSPRs (quantitative structure-property relationships) and QSARs (quantitative structure-activity relationships) are mathematical correlations between a specified molecular property or biological activity and one or more physicochemical and/or molecular structural properties. In this paper, we give explicit expressions of some degree-based topological indices of two classes of metal-organic frameworks (MOFs), namely, butylated hydroxytoluene- (BHT-) based metal-organic ( M = Co , Fe, Mn, Cr) (MBHT) frameworks and M 1 TPyP − M 2 (TPyP = 5,10,15,20 -tetrakis(4-pyridyl)porphyrin and M 1 , M 2 = Fe and Co) MOFs.

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18

Соснин, С. Б., Е. В. Радченко, В. А. Палюлин, and Н. С. Зефиров. "Обобщенный фрагментный подход в исследованиях QSAR/QSPR." Доклады Академии наук 463, no. 3 (2015): 297–300. http://dx.doi.org/10.7868/s0869565215210112.

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19

Karelson, Mati, Victor S. Lobanov, and Alan R. Katritzky. "Quantum-Chemical Descriptors in QSAR/QSPR Studies." Chemical Reviews 96, no. 3 (January 1996): 1027–44. http://dx.doi.org/10.1021/cr950202r.

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20

Estrada, Ernesto, and Enrique Molina. "3D Connectivity Indices in QSPR/QSAR Studies." Journal of Chemical Information and Computer Sciences 41, no. 3 (May 2001): 791–97. http://dx.doi.org/10.1021/ci000156i.

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21

Senese, Craig L., J. Duca, D. Pan, A. J. Hopfinger, and Y. J. Tseng. "4D-Fingerprints, Universal QSAR and QSPR Descriptors." Journal of Chemical Information and Computer Sciences 44, no. 5 (September 2004): 1526–39. http://dx.doi.org/10.1021/ci049898s.

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22

Lu, Chunhui, Weimin Guo, Xiaofang Hu, Yang Wang, and Chunsheng Yin. "A Lu index for QSAR/QSPR studies." Chemical Physics Letters 417, no. 1-3 (January 2006): 11–15. http://dx.doi.org/10.1016/j.cplett.2005.09.051.

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23

Sosnin, S. B., E. V. Radchenko, V. A. Palyulin, and N. S. Zefirov. "Generalized fragmental approach in QSAR/QSPR studies." Doklady Chemistry 463, no. 1 (July 2015): 185–88. http://dx.doi.org/10.1134/s0012500815070071.

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24

Miyao, Tomoyuki, Masamoto Arakawa, and Kimito Funatsu. "Exhaustive Structure Generation for Inverse-QSPR/QSAR." Molecular Informatics 29, no. 1-2 (January 12, 2010): 111–25. http://dx.doi.org/10.1002/minf.200900038.

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25

Hemmateenejad, Bahram, and Mahmood Sanchooli. "Substituent electronic descriptors for fast QSAR/QSPR." Journal of Chemometrics 21, no. 3-4 (2007): 96–107. http://dx.doi.org/10.1002/cem.1039.

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26

Nesměrák, Karel. "Medicinal Chemistry Meets Electrochemistry: Redox Potential in the Role of Endpoint or Molecular Descriptor in QSAR/QSPR." Mini-Reviews in Medicinal Chemistry 20, no. 14 (September 1, 2020): 1341–56. http://dx.doi.org/10.2174/1389557520666200204121806.

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Many biochemical reactions are based on redox reactions. Therefore, the redox potential of a chemical compound may be related to its therapeutic or physiological effects. The study of redox properties of compounds is a domain of electrochemistry. The subject of this review is the relationship between electrochemistry and medicinal chemistry, with a focus on quantifying these relationships. A summary of the relevant achievements in the correlation between redox potential and structure, therapeutic activity, resp., is presented. The first part of the review examines the applicability of QSPR for the prediction of redox properties of medically important compounds. The second part brings the exhaustive review of publications using redox potential as a molecular descriptor in QSAR of biological activity. Despite the complexity of medicinal chemistry and biological reactions, it is possible to employ redox potential in QSAR/QSPR. In many cases, this electrochemical parameter plays an essential but rarely absolute role.

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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 CO<sub>2</sub> (scCO<sub>2</sub>) 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 CO<sub>2</sub>, 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 (scCO<sub>2</sub>). 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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Iqbal, Zahid, Muhammad Ishaq, Adnan Aslam, Muhammad Aamir, and Wei Gao. "The measure of irregularities of nanosheets." Open Physics 18, no. 1 (August 3, 2020): 419–31. http://dx.doi.org/10.1515/phys-2020-0164.

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AbstractNanosheets are two-dimensional polymeric materials, which are among the most active areas of investigation of chemistry and physics. Many diverse physicochemical properties of compounds are closely related to their underlying molecular topological descriptors. Thus, topological indices are fascinating beginning points to any statistical approach for attaining quantitative structure–activity (QSAR) and quantitative structure–property (QSPR) relationship studies. Irregularity measures are generally used for quantitative characterization of the topological structure of non-regular graphs. In various applications and problems in material engineering and chemistry, it is valuable to be well-informed of the irregularity of a molecular structure. Furthermore, the estimation of the irregularity of graphs is helpful for not only QSAR/QSPR studies but also different physical and chemical properties, including boiling and melting points, enthalpy of vaporization, entropy, toxicity, and resistance. In this article, we compute the irregularity measures of graphene nanosheet, H-naphtalenic nanosheet, {\text{SiO}}_{2} nanosheet, and the nanosheet covered by {C}_{3} and {C}_{6}.

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Khatri, Naveen, Harish Jangra, and A. K. Madan. "Path Pendeccentric Connectivity Indices: Detour Matrix Based Molecular Descriptors for QSAR/QSPR Studies, Part 1." International Journal of Quantitative Structure-Property Relationships 2, no. 2 (July 2017): 62–74. http://dx.doi.org/10.4018/ijqspr.2017070106.

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In the present study, highly sensitive detour matrix based molecular descriptors (MDs) termed as path pendeccentric connectivity indices 1-4 as well as their topochemical variants have been conceptualized. Proposed MDs are unique because they simultaneously take into consideration the cyclicity, path pendenticity, path eccentricity and augmented adjacency of each vertex in a hydrogen depleted molecular structure. An in-house computer program was also developed to calculate values of proposed MDs. Proposed MDs were evaluated for degeneracy, discriminating power, sensitivity towards relative position of substituent(s) in cyclic structures, branching and correlation with existing MDs. Highly encouraging results offer proposed MDs a vast potential for similarity/dissimilarity studies, characterization of structures, combinatorial library design, lead identification/optimization, pharmacokinetic relationship studies and QSAR/QSPR/QSTR studies. Proposed MDs will also take due care of shape factor, relative position (s) and presence of hetero atoms in chemical structures.

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Rybińska-Fryca, Anna, Anita Sosnowska, and Tomasz Puzyn. "Representation of the Structure—A Key Point of Building QSAR/QSPR Models for Ionic Liquids." Materials 13, no. 11 (May 30, 2020): 2500. http://dx.doi.org/10.3390/ma13112500.

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The process of encoding the structure of chemicals by molecular descriptors is a crucial step in quantitative structure-activity/property relationships (QSAR/QSPR) modeling. Since ionic liquids (ILs) are disconnected structures, various ways of representing their structure are used in the QSAR studies: the models can be based on descriptors either derived for particular ions or for the whole ionic pair. We have examined the influence of the type of IL representation (separate ions vs. ionic pairs) on the model’s quality, the process of the automated descriptors selection and reliability of the applicability domain (AD) assessment. The result of the benchmark study showed that a less precise description of ionic liquid, based on the 2D descriptors calculated for ionic pairs, is sufficient to develop a reliable QSAR/QSPR model with the highest accuracy in terms of calibration as well as validation. Moreover, the process of a descriptors’ selection is more effective when the possible number of variables can be decreased at the beginning of model development. Additionally, 2D descriptors usually demand less effort in mechanistic interpretation and are more convenient for virtual screening studies.

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Estrada, Ernesto, and Lissette Rodríguez. "Edge-Connectivity Indices in QSPR/QSAR Studies. 1. Comparison to Other Topological Indices in QSPR Studies." Journal of Chemical Information and Computer Sciences 39, no. 6 (November 1999): 1037–41. http://dx.doi.org/10.1021/ci990030p.

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32

Duchowicz, Pablo, and Eduardo Castro. "Partial Order Theory Applied to QSPR-QSAR Studies." Combinatorial Chemistry & High Throughput Screening 11, no. 10 (December 1, 2008): 783–93. http://dx.doi.org/10.2174/138620708786734316.

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33

Andrey A. Toropov and Emilio Benfenati. "SMILES in QSPR/QSAR Modeling: Results and Perspectives." Current Drug Discovery Technologies 4, no. 2 (August 1, 2007): 77–116. http://dx.doi.org/10.2174/157016307781483432.

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34

Liu, Peixun, and Wei Long. "Current Mathematical Methods Used in QSAR/QSPR Studies." International Journal of Molecular Sciences 10, no. 5 (April 29, 2009): 1978–98. http://dx.doi.org/10.3390/ijms10051978.

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35

Završnik, Davorka, Samija Muratović, and Selma Špirtović. "QSAR and QSPR study of derivatives 4-arylaminocoumarin." Bosnian Journal of Basic Medical Sciences 3, no. 3 (August 20, 2003): 59–63. http://dx.doi.org/10.17305/bjbms.2003.3531.

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Coumarin and its derivatives are reactive compounds, suitable for many syntheses. They are used as anticoagulants, antibacterial, animistic compounds. The interest in coumarins has increased because it was found that they reduce the HIV virus activity. The synthesis of 4-arylaminocoumarin derivatives from 4-hydroxycoumarin, has been carried out, and their antimycotic effects were tested. In the QSAR (quantitative structure-activity relationship) QSPR (quantitativestructure-property-activity relationship) study we have used physicochemical properties and topological indices (Balaban index J(G), Wiener index W(G), information-theoretical index I(G), and valence connectivity index (G), to predict bioactivity of the newly synthesized coumarin compounds. By using methods of molecular modelling, the relationships between structure, properties and activity of coumarin compounds have been investigated. The best QSPR models were obtained using valence connectivity index or combination indices. According Rekker's method the best correlation of calculated values log P, has been obtained with the model based on the inhibition zone (I) 4-arylaminocoumarin derivatives expressed in mm. The results obtained in this study enable further synthesis of new coumarin derivatives and predict their biological activity and properties.

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36

Rücker, Christoph, Gerta Rücker, and Markus Meringer. "y-Randomization and Its Variants in QSPR/QSAR." Journal of Chemical Information and Modeling 47, no. 6 (September 20, 2007): 2345–57. http://dx.doi.org/10.1021/ci700157b.

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Luke, B. T. "Comparison of three different QSAR/QSPR generation techniques." Journal of Molecular Structure: THEOCHEM 468, no. 1-2 (August 1999): 13–20. http://dx.doi.org/10.1016/s0166-1280(98)00492-8.

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Clark, Timothy. "QSAR and QSPR based solely on surface properties?" Journal of Molecular Graphics and Modelling 22, no. 6 (July 2004): 519–25. http://dx.doi.org/10.1016/j.jmgm.2004.03.012.

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Darvas, Ferenç, Oliver Kappe, Gisbert Schneider, Michael Wiese, and Hugo Kubinyi. "QSAR/QSPR Modelling – Finding Rules in Noisy Data?" QSAR & Combinatorial Science 25, no. 10 (October 2006): 811–12. http://dx.doi.org/10.1002/qsar.200690026.

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Kiralj, Rudolf, and Márcia M. C. Ferreira. "Is your QSAR/QSPR descriptor real or trash?" Journal of Chemometrics 24, no. 11-12 (October 21, 2010): 681–93. http://dx.doi.org/10.1002/cem.1331.

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Duchowicz, Pablo R., Francisco M. Fernandez, and Eduardo A. Castro. "ChemInform Abstract: Orthogonalization Methods in QSPR - QSAR Studies." ChemInform 44, no. 29 (July 1, 2013): no. http://dx.doi.org/10.1002/chin.201329275.

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Sheikh, Umber, Sidra Rashid, Cenap Ozel, and Richard Pincak. "On Hosoya Polynomial and Subsequent Indices of C4C8(R) and C4C8(S) Nanosheets." Symmetry 14, no. 7 (June 30, 2022): 1349. http://dx.doi.org/10.3390/sym14071349.

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Chemical structures are mathematically modeled using chemical graphs. The graph invariants including algebraic polynomials and topological indices are related to the topological structure of molecules. Hosoya polynomial is a distance based algebraic polynomial and is a closed form of several distance based topological indices. This article is devoted to compute the Hosoya polynomial of two different atomic configurations (C4C8(R) and C4C8(S)) of C4C8 Carbon Nanosheets. Carbon nanosheets are the most stable, flexible structure of uniform thickness and admit a vast range of applications. The Hosoya polynomial is used to calculate distance based topological indices including Wiener, hyper Wiener and Tratch–Stankevitch–Zafirov Indices. These indices play their part in determining quantitative structure property relationship (QSPR) and quantitative structure activity relationship (QSAR) of chemical structures. The three dimensional presentation of Hosoya polynomial and related distance based indices leads to the result that though the chemical formula for both the sheets is same, yet they possess different Hosoya Polynomials presenting distinct QSPR and QSAR corresponding to their atomic configuration.

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Qiao, Shu, Kun Xie, Chuan Fu, and Jie Pan. "QSPR Study on n-Octanol/Water Partition Coefficient of PCDD/Fs by Three-Dimensional Holographic Vector of Atomic Interaction Field." Advanced Materials Research 356-360 (October 2011): 83–88. http://dx.doi.org/10.4028/www.scientific.net/amr.356-360.83.

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Polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) are a group of important persistent organic pollutants. Quantitative structure–property relationship (QSPR) modeling is a powerful approach for predicting the properties of environmental organic pollutants from their structure descriptors. In this study, a QSPR model is established for estimating n-octanol/water partition coefficient (log KOW) of PCDD/Fs. Three-dimensional holographic vector of atomic interaction field (3D-HoVAIF) is used to describe the chemical structures, SMR-PLS QSAR model has been created and good correlation coefficients and cross-validated correlation coefficient is obtained. Predictive capability of the models has also been demonstrated by leave-one-out cross-validation. Moreover, the estimated values have been presented for those PCDD/Fs which are lack of experimentally data by the optimum model.

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Kirmani, Syed Ajaz K., Parvez Ali, and Jawed Ahmad. "Topological Coindices and Quantitative Structure-Property Analysis of Antiviral Drugs Investigated in the Treatment of COVID-19." Journal of Chemistry 2022 (March 4, 2022): 1–15. http://dx.doi.org/10.1155/2022/3036655.

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SARS-CoV-2 is a new strain of coronavirus family that has never been previously detected in humans. This has grown into a huge public health issue that has affected people all around the world. Presently, there is no specific antiviral treatment for COVID-19. To tackle the outbreak, a number of drugs are being explored or have been utilized based on past experience. A molecular descriptor (or topological index) is a numerical value that describes a compound’s molecular structure and has been successfully employed in many QSPR/QSAR investigations to represent several physicochemical attributes. In order to determine topological characteristics of graphs, coindices (topological) take nonadjacent pair of vertices into account. In this study, we introduced CoM-polynomial and numerous degree-based topological coindices for several antiviral medicines such as lopinavir, ritonavir remdesivir, hydroxychloroquine, chloroquine, theaflavin, thalidomide, and arbidol which were studied using the CoM-polynomial approach. In the QSPR model, the linear regression approach is used to analyze the relationships between physicochemical properties and topological coindices. The findings show that the topological coindices under investigation have a substantial relationship with the physicochemical properties of possible antiviral medicines in question. As a result, topological coindices may be effective tools for studying antiviral drugs in the future for QSPR analyses.

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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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Rakhimbekova, Assima, Timur I. Madzhidov, Ramil I. Nugmanov, Timur R. Gimadiev, Igor I. Baskin, and Alexandre Varnek. "Comprehensive Analysis of Applicability Domains of QSPR Models for Chemical Reactions." International Journal of Molecular Sciences 21, no. 15 (August 3, 2020): 5542. http://dx.doi.org/10.3390/ijms21155542.

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Nowadays, the problem of the model’s applicability domain (AD) definition is an active research topic in chemoinformatics. Although many various AD definitions for the models predicting properties of molecules (Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) models) were described in the literature, no one for chemical reactions (Quantitative Reaction-Property Relationships (QRPR)) has been reported to date. The point is that a chemical reaction is a much more complex object than an individual molecule, and its yield, thermodynamic and kinetic characteristics depend not only on the structures of reactants and products but also on experimental conditions. The QRPR models’ performance largely depends on the way that chemical transformation is encoded. In this study, various AD definition methods extensively used in QSAR/QSPR studies of individual molecules, as well as several novel approaches suggested in this work for reactions, were benchmarked on several reaction datasets. The ability to exclude wrong reaction types, increase coverage, improve the model performance and detect Y-outliers were tested. As a result, several “best” AD definitions for the QRPR models predicting reaction characteristics have been revealed and tested on a previously published external dataset with a clear AD definition problem.

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Przybyłek, Maciej, Tomasz Jeliński, and Piotr Cysewski. "Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Hansen Solubility Parameters Based on 1D and 2D Molecular Descriptors Computed from SMILES String." Journal of Chemistry 2019 (January 10, 2019): 1–15. http://dx.doi.org/10.1155/2019/9858371.

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A new method of Hansen solubility parameters (HSPs) prediction was developed by combining the multivariate adaptive regression splines (MARSplines) methodology with a simple multivariable regression involving 1D and 2D PaDEL molecular descriptors. In order to adopt the MARSplines approach to QSPR/QSAR problems, several optimization procedures were proposed and tested. The effectiveness of the obtained models was checked via standard QSPR/QSAR internal validation procedures provided by the QSARINS software and by predicting the solubility classification of polymers and drug-like solid solutes in collections of solvents. By utilizing information derived only from SMILES strings, the obtained models allow for computing all of the three Hansen solubility parameters including dispersion, polarization, and hydrogen bonding. Although several descriptors are required for proper parameters estimation, the proposed procedure is simple and straightforward and does not require a molecular geometry optimization. The obtained HSP values are highly correlated with experimental data, and their application for solving solubility problems leads to essentially the same quality as for the original parameters. Based on provided models, it is possible to characterize any solvent and liquid solute for which HSP data are unavailable.

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Sun, Tian-Le, Haidar Ali, Bilal Ali, Usman Ali, Jia-Bao Liu, and Parvez Ali. "On Computation of Degree-Based Entropy of Planar Octahedron Networks." Journal of Function Spaces 2022 (March 9, 2022): 1–9. http://dx.doi.org/10.1155/2022/1220208.

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Chemical graph theory is the combination of mathematical graph theory and chemistry. To analyze the biocompatibility of the compounds, topological indices are used in the research of QSAR/QSPR studies. The degree-based entropy is inspired by Shannon’s entropy. The connectivity pattern such as planar octahedron network is used to predict physiochemical activity. In this article, we present some degree-based entropies of planar octahedron network.

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Deng, Bo, Chengfu Ye, Weilin Liang, Yalan Li, and Xueli Su. "Several Asymptotic Bounds on the Balaban Indices of Trees." Mathematical Problems in Engineering 2020 (November 6, 2020): 1–6. http://dx.doi.org/10.1155/2020/2081241.

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The Balaban index (also called the J index) of a connected graph G is a distance-based topological index, which has been successfully used in various QSAR and QSPR modeling. Although the index was introduced 30 years ago, there are few results on the asymptotic relations. In this paper, several asymptotic bounds on the Balaban indices of trees with diameters 3 and 4 are shown, respectively.

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T, Vinaya Prasad, Sharan Hegde, and Afshan Tarannum. "Second Redefined Zagreb Index of Generalized Transformation Graph." International Journal of Science, Engineering and Management 9, no. 2 (February 28, 2022): 42–47. http://dx.doi.org/10.36647/ijsem/09.02.a007.

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The topological indices are useful part in the investigations of quantitative structure property relationship (QSPR) and quantitative structure activity relationship (QSAR) in mathematical chemistry. During this paper, the expressions for the Second Redefined Zagreb Index of the Generalized Transformation Graphs Gxy and its supplement graphs are acquired. Keywords: Second Redefined Zagreb index; Redefined Zagreb index; generalized transformation graphs Mathematics Subject Classification: 05C76, 05C07, 92E10

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