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Journal articles on the topic 'QSAR descriptors'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Papa, Ester, Alessandro Sangion, Olivier Taboureau, and Paola Gramatica. "Quantitative Prediction of Rat Hepatotoxicity by Molecular Structure." International Journal of Quantitative Structure-Property Relationships 3, no. 2 (2018): 49–60. http://dx.doi.org/10.4018/ijqspr.2018070104.

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In this article, Quantitative Structure Activity Relationships (QSAR) were generated to link the structure of over 120 heterogeneous drugs to rat hepatotoxicity. Existing studies, performed on the same data set, could not highlight relevant structure-activity relationships, and suggested models for the prediction of hepatotoxicity based on genomic data. Binary activity responses were used for the development of classification QSARs using theoretical molecular descriptors calculated with the software PaDEL-Descriptor. A statistically powerful QSAR based on six descriptors was generated by using

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2

Berdnyk, M. I., A. B. Zakharov, and V. V. Ivanov. "Application Of L1- Regularization Approach In QSAR Problem. Linear Regression And Artificial Neural Networks." Methods and Objects of Chemical Analysis 14, no. 2 (2019): 79–90. http://dx.doi.org/10.17721/moca.2019.79-90.

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One of the primary tasks of analytical chemistry and QSAR/QSPR researches is building of prognostic regression equations based on descriptors sets. The one of the most important problems here is to decrease the number of descriptors in the initial descriptor set which is usually way too big. In current investigation the descriptor set is proposed to be reduced employing the least absolute shrinkage and selection operator (LASSO) approach. Decreased descriptor sets were used for calculations with application of the following QSAR/QSPR methods: ordinary least squares (OLS), the least absolute de

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3

Toropova, Alla P., and Andrey A. Toropov. "Evolution of Optimal Descriptors." International Journal of Quantitative Structure-Property Relationships 1, no. 2 (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 limita

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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 re

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Mishra, Durgesh Kumar, Ashutosh Singh, Sunil Kumar Mishra, Priti Singh, Abhishek Singh, and Jitendra Kumar. "PM3 Method based QSAR Study of the Derivatives of Thiadiazole and Quinoxaline for Antiepileptic Activity using Quantum Mechanical and Energy Descriptors." Asian Journal of Organic & Medicinal Chemistry 7, no. 1 (2022): 111–22. http://dx.doi.org/10.14233/ajomc.2022.ajomc-p371.

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QSAR analysis of the derivatives of thiadiazole and quinoxaline has been made for antiepileptic activity (pED50) using quantum mechanical and energy descriptors. The descriptors ionization potential, HOMO energy, LUMO energy, electron affinity, total energy, conformation minimum energy and log P have been used for QSAR analysis. The PM3 method has been employed for the calculation of descriptors. The best QSAR model has been obtained by using the descriptors electron affinity, total energy, conformation minimum energy and log P in which regression coefficient is 0.836651 and cross-validation c

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Singh, Dr Anamika, and Dr Rajeev Singh. "QSAR and its Role in Target-Ligand Interaction." Open Bioinformatics Journal 7, no. 1 (2013): 63–67. http://dx.doi.org/10.2174/1875036201307010063.

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Each molecule has its own specialty, structure and function and when these molecules are combined together they form a compound. Structure and function of a molecule are related to each other and QSARs (Quantitative Structure– Activity relationships) are based on the criteria that the structure of a molecule must contain the features responsible for its physical, chemical, and biological properties, and on the ability to represent the chemical by one, or more, numerical descriptor(s). By QSAR models, the biological activity of a new or untested chemical can be inferred from the molecular struc

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7

Gupta, Ashish, Virender Kumar, and Polamarasetty Aparoy. "Role of Topological, Electronic, Geometrical, Constitutional and Quantum Chemical Based Descriptors in QSAR: mPGES-1 as a Case Study." Current Topics in Medicinal Chemistry 18, no. 13 (2018): 1075–90. http://dx.doi.org/10.2174/1568026618666180719164149.

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Quantitative Structure Activity Relationship (QSAR) is one of the widely used ligand based drug design strategies. Although a number of QSAR studies have been reported, debates over the limitations and accuracy of QSAR models are at large. In this review the applicability of various classes of molecular descriptors in QSAR has been explained. Protocol for QSAR model development and validation is presented. Here we discuss a case study on 7-Phenyl-imidazoquinolin-4(5H)-one derivatives as potent mPGES-1 inhibitors to identify crucial physicochemical properties responsible for mPGES-1 inhibition.

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8

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 (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

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9

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 signif

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10

KUMAR, PAWAN, PRITI SINGH, R. K. SINGH, M. ANSARI, and MOHD ADIL KHAN. "Quantum mechanical parameters-based study of aryl sulphonamides as 5-HT6 serotonin ligand using DFT methods." Romanian Journal of Biophysics 34, no. 1 (2024): 13–26. http://dx.doi.org/10.59277/rjb.2024.1.02.

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In the present work, quantum mechanical descriptors have been used for the development of quantitative structure activity relationship (QSAR) models for the thirty-two derivatives of aryl sulphonamide and sulfone based 5-HT6 antagonists. Among several classes of serotonin 5-HT6 receptor ligands, aryl sulphonamides reported better affinity towards the receptor. Drugs acting as serotonin ligands are useful in the treatment of a variety of mental disorders. The descriptors that have been used in our study are total energy, log P, molecular weight, dipole moment, heat of formation, LUMO energy, HO

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11

SHAO, ZEHUI, HUIQIN JIANG, and ZAHID RAZA. "Inequalities Among Topological Descriptors." Kragujevac Journal of Mathematics 47, no. 5 (2023): 661–72. http://dx.doi.org/10.46793/kgjmat2305.661s.

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A topological index is a type of molecular descriptor that is calculated based on the molecular graph of a chemical compound. Topological indices are used for example in the development of QSAR QSPR in which the biological activity or other properties of molecules are correlated with their chemical structure. In this paper, we establish several inequalities among the molecular descriptors such as the generalized version of the ﬁrst Zagreb index, the Randić index, the ABC index, AZI index, and the redeﬁned ﬁrst, second and third Zagreb indices.

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12

Hughes-Oliver, Jacqueline M., Atina D. Brooks, William J. Welch, et al. "ChemModLab: A Web-Based Cheminformatics Modeling Laboratory." In Silico Biology: Journal of Biological Systems Modeling and Multi-Scale Simulation 11, no. 1-2 (2012): 61–81. https://doi.org/10.3233/ci-2008-0016.

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ChemModLab, written by the ECCR @ NCSU consortium under NIH support, is a toolbox for fitting and assessing quantitative structure-activity relationships (QSARs). Its elements are: a cheminformatic front end used to supply molecular descriptors for use in modeling; a set of methods for fitting models; and methods for validating the resulting model. Compounds may be input as structures from which standard descriptors will be calculated using the freely available cheminformatic front end PowerMV; PowerMV also supports compound visualization. In addition, the user can directly input their own cho

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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 (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 struct

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14

Widiakongko, Priyagung Dhemi, and Karisma Triatmaja. "Toward Novel Antioxidant Drugs: Quantitative Structure-Activity Relationship Study of Eugenol Derivatives." Walisongo Journal of Chemistry 4, no. 2 (2021): 147–54. http://dx.doi.org/10.21580/wjc.v4i2.9228.

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The study of the Quantitative Structure-Activity Relationship (QSAR) of eugenol compound and its derivatives towards antioxidant activities was conducted using electronic and molecular descriptors. These descriptors were generated from semi-empirical chemical computation with PM3 level of theory. The QSAR model in this research could be used to predict novel antioxidant compounds which are more potent. The activity of the compound determined based on the IC50 value (Inhibition Concentration 50%) was linked with the descriptor results that had been calculated in a QSAR equation. The data showed

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15

Sarkar, Bikash Kumar. "DFT Based QSAR Studies of Phenyl Triazolinones of Protoporphyrinogen Oxidase Inhibitors." Asian Journal of Organic & Medicinal Chemistry 5, no. 4 (2020): 307–11. http://dx.doi.org/10.14233/ajomc.2020.ajomc-p280.

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The quantitative structure activity relationships (QSARs) have been investigated on a series of substituted phenyl triazolinones having protoporphyrinogen oxidase (PPO) inhibition activities. The density functional theory (DFT) method is applied to calculate the quantum chemical descriptors. The derived QSAR model is based on three molecular descriptors namely highest occupied molecular orbital (HOMO) energy, electrophilic group frontier electron density (Fg E) and nucleus independent chemical shift (NICS). The best QSAR model has a square correlation coefficient r2 =0.886 and cross-validated

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16

Chen, Jing, Yunjing Gao, Xiaoyan Hu, Dongdong Qin, and Xiaoquan Lu. "Descriptor selection based on variable stability for predicting inhibitor activity." Journal of Theoretical and Computational Chemistry 16, no. 08 (2017): 1750074. http://dx.doi.org/10.1142/s0219633617500742.

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Quantitative structure-activity relationship (QSAR) has been a technique to study the relationship between chemical structures and properties, and variable selection is an important problem for finding the informative variables and building reliable models. A variable selection method based on variable stability is proposed and used for selecting the informative descriptors in the QSAR model of inhibitors. In the method, a series of models are built by leave-one-out cross validation (LOOCV), and variable stability is defined as the ratio of the absolute mean value and standard deviation of the

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17

Mulla, J. A. S., M. B. Palkar, V. S. Maddi, and I. A. M. Khazi. "2D-QSAR Study of Thienopyrimidine Derivatives: An Approach to Design Effective Anti-bacterial Agents." International Journal of Drug Design and Discovery 3, no. 2 (2025): 784–97. https://doi.org/10.37285/ijddd.3.2.7.

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QSAR studies were performed on a set of 35 analogs of thienopyrimidine using V-Life Molecular Design Suite (MDS 3.5)QSAR plus module by using Multiple Linear Regression (MLR)and Partial Least Squares (PLS) Regression methods against a gram positive (B.subtilis) and a gram negative (P.aeruginosa) bacteria. MLR and PLS methods have shown a very promising prediction resultsin B.subtilisand P.aeruginosa respectively. QSAR model was generated by a training set of 27 molecules with correlation coefficient (r2) of 0.9886,0.8671, significant cross validated correlation coefficient (q2) of 0.9020, 0.73

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18

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

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19

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 (2004): 1526–39. http://dx.doi.org/10.1021/ci049898s.

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20

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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21

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 in

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22

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 (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

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23

Xia, Liang-Yong, Qing-Yong Wang, Zehong Cao, and Yong Liang. "Descriptor Selection Improvements for Quantitative Structure-Activity Relationships." International Journal of Neural Systems 29, no. 09 (2019): 1950016. http://dx.doi.org/10.1142/s0129065719500163.

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Molecular descriptor selection is an essential procedure to improve a predictive quantitative structure–activity relationship (QSAR) model. However, within the QSAR model, there are a number of redundant, noisy and irrelevant descriptors. In this study, we propose a novel descriptor selection framework using self-paced learning (SPL) via sparse logistic regression (LR) with Logsum penalty (SPL-Logsum), which can simultaneously adaptively identify the simple and complex samples and avoid over-fitting. SPL is inspired by the learning process of humans or animals gradually learned from simple and

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Nazarova, Antonina L., and Aiichiro Nakano. "VLA-SMILES: Variable-Length-Array SMILES Descriptors in Neural Network-Based QSAR Modeling." Machine Learning and Knowledge Extraction 4, no. 3 (2022): 715–37. http://dx.doi.org/10.3390/make4030034.

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Machine learning represents a milestone in data-driven research, including material informatics, robotics, and computer-aided drug discovery. With the continuously growing virtual and synthetically available chemical space, efficient and robust quantitative structure–activity relationship (QSAR) methods are required to uncover molecules with desired properties. Herein, we propose variable-length-array SMILES-based (VLA-SMILES) structural descriptors that expand conventional SMILES descriptors widely used in machine learning. This structural representation extends the family of numerically code

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Mishra, Durgesh Kumar, Ashutosh Singh, Sunil Mishra, Priti Singh, and Abhishek Singh. "PM3 Method based QSAR Study of the Derivatives of Thiadiazole and Quinoxaline for Antiepileptic Activity using Topological Descriptors." Asian Journal of Organic & Medicinal Chemistry 7, no. 1 (2022): 99–110. http://dx.doi.org/10.14233/ajomc.2022.ajomc-p370.

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QSAR study of the derivatives of thiadiazole and quinoxaline has been performed for the antiepileptic activity using the topological descriptors viz., molar refractivity, shape index (basic kappa, order 1), shape index (basic kappa, order 2), shape index (basic kappa, order 3), valence connectivity index (order 0, standard), valence connectivity index (order 1, standard) and valence connectivity index (order 2, standard). In the best QSAR model, the descriptors are molar refractivity, shape index (basic kappa, order 1), shape index (basic kappa, order 3) and valence connectivity index (order 0

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Somesh Kumar Saxena, Somesh Kumar Saxena. "QSAR and docking study: A review." International journal of therapeutic innovation 3, no. 2 (2025): 01–05. https://doi.org/10.55522/ijti.v3i2.0107.

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Quantitative structure–activity relationship models (QSAR models) are regression or classification models used in the chemical and biological sciences and engineering. Like other regression models, QSAR regression models relate a set of "predictor" variables (X) to the potency of the response variable(Y), while classification QSAR models relate the predictor variables to a categorical value of the response variable. In QSAR modeling, the predictors consist of physico-chemical properties or theoretical molecular descriptors of chemicals; the QSAR response-variable could be a biological activity

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Nabila Aziz, Nor Fatin, Norfadzlia Mohd Yusof, Yogan Jaya Kumar, Siti Haryanti Hj Hairol Anuar, and Farhad Soleimanian Gharehchopogh. "Enhancing QSAR Model Accuracy for Biodegradability Prediction Using Chaotic Adaptive Binary Manta Ray Foraging Optimization." Journal of Physics: Conference Series 2998, no. 1 (2025): 012024. https://doi.org/10.1088/1742-6596/2998/1/012024.

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Abstract Choosing descriptors is a crucial aspect of improving QSAR (Quantitative Structure-Activity Relationship) models, especially when it comes to precisely forecasting chemical biodegradability. As cheminformatics advances, the handling of large molecular datasets introduces challenges due to the high dimensionality created by numerous molecular descriptors. This study presents the Chaotic Adaptive Somersault Factor Binary Manta Ray Foraging Optimization (CASF-BMRFO) algorithm, designed to optimize descriptor selection and boost QSAR model performance. By integrating innovative techniques

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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 (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.

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Chakraborty, Tanmoy, and Dulal C. Ghosh. "Correlation of the Drug Activities of Some Anti-Tubercular Chalcone Derivatives in Terms of the Quantum Mechanical Reactivity Descriptors." International Journal of Chemoinformatics and Chemical Engineering 1, no. 2 (2011): 53–65. http://dx.doi.org/10.4018/ijcce.2011070104.

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Under the QSPR/QSAR paradigm, a comparative study is made of the known drug activity of as many as 15 anti-tubercular drugs vis-à-vis the computed quantum mechanical global reactivity descriptors like global hardness, global softness and global electrophilicity index. The comparative study reveals that the experimentally determined activity of drug molecules, including its variation with side substitution on the parent moiety, correlate nicely with the theoretical descriptors. The global electrophilicity index of a molecule may be useful in predicting the mechanism of the drug receptor interac

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Çolakoğlu, Özge. "NM-polynomials and Topological Indices of Some Cycle-Related Graphs." Symmetry 14, no. 8 (2022): 1706. http://dx.doi.org/10.3390/sym14081706.

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Topological indices (molecular descriptors) are numerical values of a chemical structure and represented by a graph. Molecular descriptors are used in QSPR/QSAR modeling to determine a chemical structure’s physical, biological, and chemical properties. The cycle graphs are symmetric graphs for any number vertices. In this paper, recently defined neighborhood degree sum-based molecular descriptors and polynomials are studied. NM-polynomials and molecular descriptors of some cycle-related graphs, which consist of the wheel graph, gear graph, helm graph, flower graph, and friendship graph, are co

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Duchowicz, Pablo Roman, Silvina Fioressi, Gustavo Romanelli, and Daniel E. Bacelo. "Alternative QSAR Study for Unsymmetrical Aromatic Disulfide Anti-SARS Inhibitors." International Journal of Quantitative Structure-Property Relationships 6, no. 2 (2021): 47–57. http://dx.doi.org/10.4018/ijqspr.2021040104.

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This work applied the quantitative structure-activity relationships (QSAR) theory to predict the inhibitory activity exhibited by 40 unsymmetrical aromatic disulfide compounds against the SARS-CoV main protease. Different freely available molecular descriptor programs provided 67,116 independent non-conformational molecular descriptors. This great number of descriptors contained multidimensional representations of the chemical structure and was analyzed through multivariable linear regressions and the replacement method variable subset selection technique. The developed QSAR model achieved an

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Crippen, Gordon. "Chirality Descriptors in QSAR." Current Computer Aided-Drug Design 4, no. 4 (2008): 259–64. http://dx.doi.org/10.2174/157340908786786001.

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Siddiqui, Muhammad Kamran, Yu-Ming Chu, Muhammad Nasir, Muhammad Faisal Nadeem, and Muhammad Farhan Hanif. "On topological descriptors of ceria oxide and their applications." Main Group Metal Chemistry 44, no. 1 (2021): 103–16. http://dx.doi.org/10.1515/mgmc-2021-0015.

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Abstract A topological descriptor is a mathematical illustration of a molecular construction that relates particular physicochemical properties of primary molecular structure as well its mathematical depiction. Topological co-indices are usually applied for quantitative structure actions relationships (QSAR) and quantitative structures property relationships (QSPR). Topological co-indices are topological descriptors which are considered the noncontiguous vertex set. We study the accompanying some renowned topological co-indices: first and second Zagreb co-indices, first and second multiplicati

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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-degre

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Patel, D. K., and N. M. Patel. "QSAR Analysis of Aminoquinoline Analogues as MCH1 Receptor Antagonist." Journal of Scientific Research 1, no. 3 (2009): 594–605. http://dx.doi.org/10.3329/jsr.v1i3.2126.

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Quantitative structure activity relationship (QSAR) has been established for 2-aminoquinoline-6-carboxamide melanin-concentrating hormone (MCH) 1R antagonists. The multiple linear regressions were used to generate the relationship between biological activity and calculated descriptors. From the 100 of models with r2 > 0.700 was developed. Final selected model was prepared using four descriptors (DMXC, KChiV4, Rcom, and IM1L) which are belong to topological, steric, spatial and electrotopological class descriptor. The validation of the model was done by cross validation; randomization and ex

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Alrowaili, Dalal, Faraha Ashraf, Rifaqat Ali, et al. "Computation of Vertex-Based Topological Descriptors of Organometallic Monolayers of TM 3 C 12 S 12." Journal of Mathematics 2021 (October 21, 2021): 1–7. http://dx.doi.org/10.1155/2021/8572049.

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Topological descriptors are mathematical values related to chemical structures which are associated with different physicochemical properties. The use of topological descriptors has a great contribution in the field of quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) modeling. These are mathematical relationships between different molecular properties or biological activity and some other physicochemical or structural properties. In this article, we calculate few vertex degree-based topological indices/descriptors of the organometallic

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Dinesh Kumar Meena, Brij Kishore Sharma, and Raghuraj Parihar. "Quantitative structure-activity relationship study on the CDK2 inhibitory activity of 6-substituted 2-arylaminopurines." GSC Biological and Pharmaceutical Sciences 20, no. 3 (2022): 107–19. http://dx.doi.org/10.30574/gscbps.2022.20.3.0344.

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QSAR study has been carried out on the CDK2 inhibitory activity of 6-substituted 2-arylaminopurines in 0D- to 2D-Dragon descriptors. The derived QSAR models have revealed that the reciprocal hyper-detour index (descriptor Rww) and path/walk 5 Randic shape index (descriptor PW5) played a pivotal role in rationalization of CDK2 inhibition activity of titled compounds. Molecular weight (MW), mean atomic volume scaled on Carbon atom (Mv) and atomic properties such as mass and atomic Sanderson electronegativity in terms of atomic properties weighted descriptors MATS1m, MATS3e, MATS4e, GATS3e and GA

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Dinesh, Kumar Meena, Kishore Sharma Brij, and Parihar Raghuraj. "Quantitative structure-activity relationship study on the CDK2 inhibitory activity of 6-substituted 2-arylaminopurines." GSC Biological and Pharmaceutical Sciences 20, no. 3 (2022): 107–19. https://doi.org/10.5281/zenodo.7142279.

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QSAR study has been carried out on the CDK2 inhibitory activity of 6-substituted 2-arylaminopurines in 0D- to 2D-Dragon descriptors. The derived QSAR models have revealed that the reciprocal hyper-detour index (descriptor Rww) and path/walk 5 Randic shape index (descriptor PW5) played a pivotal role in rationalization of CDK2 inhibition activity of titled compounds. Molecular weight (MW), mean atomic volume scaled on Carbon atom (Mv) and atomic properties such as mass and atomic Sanderson electronegativity in terms of atomic properties weighted descriptors MATS1m, MATS3e, MATS4e, GATS3e and GA

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Afsar Jahan, Brij Kishore Sharma, and Vishnu Dutt Sharma. "Quantitative structure-activity relationship study on the MMP-13 inhibitory activity of fused pyrimidine derivatives possessing a 1,2,4-Triazol-3-yl group as a ZBG." GSC Biological and Pharmaceutical Sciences 16, no. 1 (2021): 251–65. http://dx.doi.org/10.30574/gscbps.2021.16.1.0199.

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QSAR study has been carried out on the MMP-13 inhibitory activity of fused pyrimidine derivatives possessing a1,2,4-triazol-3-yl group as a ZBG in 0D- to 2D-Dragon descriptors. The derived QSAR models have revealed that the number of Sulfur atoms (descriptor nS), Balaban mean square distance index (descriptor MSD), molecular electrotopological variation (descriptor DELS), structural information content index of neighborhood symmetry of 2nd and 3rd order (descriptors SIC2 and SIC3), average valence connectivity index chi-4 (descriptor X4Av) in addition to 1st order Galvez topological charge ind

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Afsar, Jahan, Kishore Sharma Brij, and Dutt Sharma Vishnu. "Quantitative structure-activity relationship study on the MMP-13 inhibitory activity of fused pyrimidine derivatives possessing a 1,2,4-Triazol-3-yl group as a ZBG." GSC Biological and Pharmaceutical Sciences 16, no. 1 (2021): 251–65. https://doi.org/10.5281/zenodo.5168660.

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QSAR study has been carried out on the MMP-13 inhibitory activity of fused pyrimidine derivatives possessing a1,2,4-triazol-3-yl group as a ZBG in 0D- to 2D-Dragon descriptors. The derived QSAR models have revealed that the number of Sulfur atoms (descriptor nS), Balaban mean square distance index (descriptor MSD), molecular electrotopological variation (descriptor DELS), structural information content index of neighborhood symmetry of 2<sup>nd</sup> and 3<sup>rd</sup> order (descriptors SIC2 and SIC3), average valence connectivity index chi-4 (descriptor X4Av) in addition to 1<sup>s

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NAZİB ALİAS, Ahmad, and Zubainun MOHAMED ZABİDİ. "QSAR Studies on Nitrobenzene Derivatives using Hyperpolarizability and Conductor like Screening model as Molecular Descriptors." Journal of the Turkish Chemical Society Section A: Chemistry 9, no. 3 (2022): 953–68. http://dx.doi.org/10.18596/jotcsa.1083840.

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Quantitative structure-activity relationship (QSAR) models were useful in understanding how chemical structure relates to the toxicology of chemicals. In the present study, we report quantum molecular descriptors using conductor like screening model (COs) area, the linear polarizability, first and second order hyperpolarizability for modelling the toxicology of the nitro substituent on the benzene ring. All the molecular descriptors were performed using semi-empirical PM6 approaches. The QSAR model was developed using stepwise multiple linear regression. We found that the stable QSAR modelling

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Roy, Partha Pratim, Jagadish Singh, and Supratim Ray. "Exploring QSAR of Some Antitubercular Agents." International Journal of Quantitative Structure-Property Relationships 3, no. 1 (2018): 25–42. http://dx.doi.org/10.4018/ijqspr.2018010102.

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The in vitro vero cell cytotoxicity of 93 antitubercular compounds belonging to the classes of chiral pentaamines, bis-cyclic guanidines, bis-cyclic thioureas, bis-cyclic piperazines, and quinolylhydrazones has been modeled in the present quantitative structure-activity relationship (QSAR) study. Genetic function approximation followed by multiple linear regression (GFA-MLR) based on the mean absolute error (MAE) based criteria was used as the chemometric tool for the model development using 2D descriptors available from open source PaDEL-Descriptor. The developed model was statistically robus

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Golik, Mykola, Tetiana Titko, Angelina Shaposhnyk, et al. "QSAR analysis and molecular docking study of pyrrolo- and pyridoquinolinecarboxamides with diuretic activity." ScienceRise: Pharmaceutical Science, no. 3(31) (June 30, 2021): 19–27. http://dx.doi.org/10.15587/2519-4852.2021.234493.

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The aim. The aim of the study was to reveal QSAR and ascertain the possible mechanism of action via docking study in the row of tricyclic quinoline derivatives with diuretic activity. Materials and methods. Pyrrolo- and pyridoquinolinecarboxamides with proven diuretic activity were involved in the study. Molecular descriptors were calculated using HyperChem and GRAGON software, and QSAR models were built using BuildQSAR software. For receptor-oriented flexible docking, the Autodock 4.2 software package was used. Results. Multivariate linear QSAR models were built on two datasets of

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Drapak, I. V. "QSAR-ANALYSIS OF POLYSUBSTITUTED FUNCTIONALIZED AMINOTHIAZOLES WITH ANTIHYPERTENSIVE ACTIVITY." International Journal of Medicine and Medical Research 5, no. 2 (2020): 98–104. http://dx.doi.org/10.11603/ijmmr.2413-6077.2019.2.10898.

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Background. QSAR analysis is an important tool for the identification of pharmacophore fragments in biologically active substances and helps optimize the search for new effective drugs. Objective. The aim of the study was to determine the molecular descriptors for QSAR analysis of polysubstituted functionalized aminothiazoles as a theoretical basis for purposeful search de novo of potential antihypertensive drugs among the investigated compounds. Methods. Calculation of molecular descriptors and QSAR-models creation was carried out using the Hyper-Chem 7.5 and BuildQSAR packages.&#

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Dureja, Harish. "Superaugmented Pendentic Indices: Novel Topological Descriptors for QSAR/QSPR." Scientia Pharmaceutica 77, no. 3 (2009): 521–37. http://dx.doi.org/10.3797/scipharm.0903-07.

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Kubinyi, Hugo. "Buchbesprechung: Molecular Descriptors in QSAR/ QSPR. Von Mati Karelson." Angewandte Chemie 113, no. 6 (2001): 1172–73. http://dx.doi.org/10.1002/1521-3757(20010316)113:6<1172::aid-ange1172>3.0.co;2-k.

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KARELSON, M., V. S. LOBANOV, and A. R. KATRITZKY. "ChemInform Abstract: Quantum-Chemical Descriptors in QSAR/QSPR Studies." ChemInform 27, no. 35 (2010): no. http://dx.doi.org/10.1002/chin.199635327.

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Kuz'min, Victor E., Liudmila N. Ognichenko, Natalia Sizochenko, et al. "Combining Features of Metal Oxide Nanoparticles." International Journal of Quantitative Structure-Property Relationships 4, no. 1 (2019): 28–40. http://dx.doi.org/10.4018/ijqspr.2019010103.

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A poor applicability of classic 2D descriptors for representation of metal oxide nanoparticles is briefly discussed. The combination of 1D descriptors with previously calculated size-dependent descriptors is utilized to represent the structural features of nanoparticles in QSAR modeling. For this purpose, descriptors based on the fundamental characteristics of atoms (nuclear charge, oxidation level, electronegativity, ionic radius, ionic refraction etc.) were combined with those derived from structural formula (doublets –A2, AB, …; triplets – A3, A2B, ABC, … etc.) and “liquid drop model” deriv

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Tan, Shiow Jin, Mahasin Alam Sk, Peter Peng Foo Lee, Yaw Kai Yan, and Kok Hwa Lim. "Structural requirements of salicylaldehyde benzoylhydrazones and their Cu(II) complexes for anticancer activity." Canadian Journal of Chemistry 90, no. 9 (2012): 762–75. http://dx.doi.org/10.1139/v2012-053.

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Salicylaldehyde benzoylhydrazone (H2sb) has a variety of biological activities including anticancer activity. The Cu(II) complexes of H2sbs possess enhanced anticancer activity as compared with their free ligands. A quantitative structure–activity relationship (QSAR) analysis was performed on a series of H2sb ligands and their corresponding Cu(II) complexes to capture the structural requirements that are responsible for the bioactivity. The predictive QSAR models were developed using statistical techniques such as multiple linear regression (MLR) and principal component regression analysis (PC

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Azizah, Munaya, Arry Yanuar, and Firdayani Firdayani. "Dimensional Reduction of QSAR Features Using a Machine Learning Approach on the SARS-Cov-2 Inhibitor Database." Jurnal Penelitian Pendidikan IPA 8, no. 6 (2022): 3095–101. http://dx.doi.org/10.29303/jppipa.v8i6.2432.

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Quantitative Structure-Activity Relationship (QSAR) is a method that relates the chemical composition of a molecule to its biochemical, pharmaceutical and biological activities. The characteristics of a molecule's chemical constituents, such as chemical descriptors and fingerprints, are necessary to create a good QSAR model. Dimensionality reduction can alleviate the issue of several unnecessary and redundant chemical descriptors and chemical fingerprints in a high-dimensional feature-number data set by shrinking the high-dimensional original space to a low-dimensional intrinsic space. There a

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