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Journal articles on the topic 'Quantitative structure-activity relationship (QSAR)'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

NAKAGAWA, Yoshiaki. "Quantitative Structure-Activity Relationship." Japanese Journal of Pesticide Science 38, no. 1 (2013): 1. http://dx.doi.org/10.1584/jpestics.w12-39.

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2

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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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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Amisha G, Govindarao Kamala, Chandrika D, et al. "Quantitative Structure-Activity Relationship (QSAR) in Drug Discovery and Development." Journal of Pharma Insights and Research 3, no. 1 (2025): 241–51. https://doi.org/10.69613/d091zy53.

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Quantitative structure-activity relationship (QSAR) analysis represents a cornerstone approach in modern drug discovery and development. QSAR methodologies establish mathematical correlations between molecular structures and their biological activities, enabling the prediction of compound properties and behaviors. Recent advances in computational capabilities, coupled with the emergence of sophisticated machine learning algorithms, have revolutionized traditional QSAR approaches. The integration of deep learning architectures, including graph neural networks and convolutional neural networks,

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Mudasir, Mudasir, Iqmal Tahir, and Ida Puji Astuti Maryono Putri. "QUANTITATIVE STRUCTURE AND ACTIVITY RELATIONSHIP ANALYSIS OF 1,2,4-THIADIAZOLINE FUNGICIDES BASED ON MOLECULAR STRUCTURE CALCULATED BY AM1 METHOD." Indonesian Journal of Chemistry 3, no. 1 (2010): 39–47. http://dx.doi.org/10.22146/ijc.21904.

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Quantitative structure-Activity relationship (QSAR) analysis of fungicides having 1,2,4-thiadiazoline structure based on theoretical molecular properties have been done. Calculation of the properties was conducted by semiempirical method AM1 and the activity of the compounds was taken from literature. Relationship analysis between fungicides activity (pEC50) and molecular properties was done using SPSS program. The QSAR analysis gave the best model as follows: pEC50 = 3.842 + (1.807x10-4) ET + (5.841x10-3) Eb - (5.689x10-2) DHf -0.770 log P + 1.144 a - 0.671 m + 9.568 GLOB - (5.54x10-2) MR. n=

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6

Dunn, W. J. "Quantitative structure—activity relationships (QSAR)." Chemometrics and Intelligent Laboratory Systems 6, no. 3 (1989): 181–90. http://dx.doi.org/10.1016/0169-7439(89)80083-8.

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7

Gupta, Satya P. "Quantitative Structure-Activity Relationships of Antiarrhythmic Drugs." Current Pharmaceutical Design 4, no. 6 (1998): 455–68. http://dx.doi.org/10.2174/138161280406221011112729.

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Abstract: A Comprehensive review of quantitative structure-activity relationship (QSAR) studies on antiarrhythmic agents is presented. From the discussion point of view, the antiarrhythmic agents have been put into two broad classes: specific and nonspecific. While the main members of the former class can be -adrenergic blocking agents ( -blockers), any chemical that can act directly on the myocardial cell membrane, producing a cardiodepressant effect via changes in basic electrophysiological properties of the membrane, such as automaticity, excitability, conductivity, and refractoriness. has

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8

Buchwald, Fabian, Tobias Girschick, Eibe Frank, and Stefan Kramer. "Fast Conditional Density Estimation for Quantitative Structure-Activity Relationships." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 1268–73. http://dx.doi.org/10.1609/aaai.v24i1.7494.

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Many methods for quantitative structure-activity relationships (QSARs) deliver point estimates only, without quantifying the uncertainty inherent in the prediction. One way to quantify the uncertainy of a QSAR prediction is to predict the conditional density of the activity given the structure instead of a point estimate. If a conditional density estimate is available, it is easy to derive prediction intervals of activities. In this paper, we experimentally evaluate and compare three methods for conditional density estimation for their suitability in QSAR modeling. In contrast to traditional m

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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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10

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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Dixit, Nandan, Chirag Patel, Mansi Bhavsar, Saumya Patel, Rakesh Rawal, and Hitesh Solanki. "QUANTITATIVE STRUCTURE–ACTIVITY RELATIONSHIP (QSAR) STUDY OF LIVER TOXIC DRUGS." International Association of Biologicals and Computational Digest 1, no. 1 (2022): 63–71. http://dx.doi.org/10.56588/iabcd.v1i1.17.

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Drug-induced liver injury (DILI) is one of the most severe adverse effects (AEs) causing life-threatening conditions, such as acute liver failure. t has also been recognized as the single most common cause of safety- related post-market withdrawals or warnings Due to the nature and idiosyncrasy of clinical forms of DILI, attempts to develop new predictive approaches to evaluate the risk of a medication being a hepatotoxicant have been difficult. The FDA Adverse Event Reporting System (AERS) provides post-market data illustrating AE morbidity. A quantitative structure –activity relationship (QS

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Matsuzaka, Yasunari, and Yoshihiro Uesawa. "Computational Models That Use a Quantitative Structure–Activity Relationship Approach Based on Deep Learning." Processes 11, no. 4 (2023): 1296. http://dx.doi.org/10.3390/pr11041296.

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In the toxicological testing of new small-molecule compounds, it is desirable to establish in silico test methods to predict toxicity instead of relying on animal testing. Since quantitative structure–activity relationships (QSARs) can predict the biological activity from structural information for small-molecule compounds, QSAR applications for in silico toxicity prediction have been studied for a long time. However, in recent years, the remarkable predictive performance of deep learning has attracted attention for practical applications. In this review, we summarize the application of deep l

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Sethi, Navdeep Singh. "A Review on Computational Methods in Developing Quantitative Structure-Activity Relationship (QSAR)." International Journal of Drug Design and Discovery 3, no. 3 (2025): 815–36. https://doi.org/10.37285/ijddd.3.3.1.

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Virtual filtering and screening of combinatorial libraries have recently gained attention as methods complementing the high-throughput screening and combinatorial chemistry. These chemoinformatic techniques rely heavily on quantitative structure-activity relationship (QSAR) analysis, a field with established methodology and successful history. In this review, we discuss the computational methods for building QSAR models. The review starts with general introduction and theories of QSAR and identifying the general scheme of a QSAR model. Following, the review focus on the methodologies in constr

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Ramapraba, Palayanoor Seethapathy, Bellam Ravindra Babu, Nallathampi Rajamani Rejin Paul, et al. "Implementing cloud computing in drug discovery and telemedicine for quantitative structure-activity relationship analysis." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 1 (2025): 1132–41. https://doi.org/10.11591/ijece.v15i1.pp1132-1141.

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This work aims to use cutting-edge machine learning methods to improve quantitative structure-activity relationship (QSAR) analysis, which is used in drug development and telemedicine. The major goal is to examine the performance of several predictive modeling approaches, including random forest, deep learning-based QSAR models, and support vector machines (SVM). It investigates the potential of feature selection techniques developed in chemoinformatics for enhancing model accuracy. The innovative aspect is using cloud computing resources to strengthen c

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15

Worth, Andrew P. "ECVAM's Activities on Computer Modelling and Integrated Testing." Alternatives to Laboratory Animals 30, no. 2_suppl (2002): 133–37. http://dx.doi.org/10.1177/026119290203002s22.

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This paper introduces the basic concepts of quantitative structure–activity relationship (QSAR), expert system and integrated testing strategy, and explains how the analogy between QSARs and prediction models leads naturally to criteria for the validation of QSARs. ECVAM's in-house research programme on QSAR modelling and integrated testing is summarised, along with plans for the validation of QSAR models and expert system rulebases at the European Union level.

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16

Ungwitayatorn, J., M. Pickert, and A. W. Frahm. "Quantitative structure-activity relationship (QSAR) study of polyhydroxyxanthones." Pharmaceutica Acta Helvetiae 72, no. 1 (1997): 23–29. http://dx.doi.org/10.1016/s0031-6865(96)00043-x.

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17

Toropov, Andrey A., and Alla P. Toropova. "QSPR/QSAR: State-of-Art, Weirdness, the Future." Molecules 25, no. 6 (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 searchin

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18

A., K. Srivastava, Archana, and Jaiswal Meetu. "Quantitative structure activity relationship studies on a series of 1,3-diaryl-4,5,6, 7-tetrahydro-2H-isoindole derivatives as potent and selective Cox-2 inhibitors." Journal of Indian Chemical Society Vol. 84, Mar 2007 (2007): 260–62. https://doi.org/10.5281/zenodo.5816460.

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Department of Chemistry, University of Allahabad, Allahabad-211 002, Uttar Pradesh, India <em>E-mail </em>: qsarlab_am@rediffmail.com <em>Manuscript received 7 November 2006, accepted 10 January 2007</em> The quantitative structure activity relationship (QSAR) of analogues of diary! tetrahydroisoindole focusing on the modification of the ring fused to pyrrole nucleus, is discussed. The anti-inflammatory activity of these compounds is found to be dominantly controlled by electronic and steric factors, Xeq and <sup>1</sup>א<sup>v</sup> in combination with indicator parameters.

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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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20

Ram Sirvi, Sakha. "A Review on Quantitative Structure-Activity and Relationships (QSAR) Methods." International Journal of Scientific Research and Management 10, no. 04 (2022): 624–28. http://dx.doi.org/10.18535/ijsrm/v10i04.mp04.

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QSAR is an analytical application that can be used to interpret the quantitative relationship between the biological activities of a particular molecule and its structure. The product of QSAR will then produce useful equations, images or models in either 2D or 3D form that would relate their biological responses or physical properties to their molecular structure. Hologram QSAR (HQSAR) uses molecular holograms and PLS to generate fragment-based structure-activity relationships. Unlike other 3D-QSAR methods, HQSAR does not require alignment of molecules, allowing automated analysis of very larg

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21

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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22

Nesterkina, Mariia, Viacheslav Muratov, Luidmyla Ognichenko, Iryna Kravchenko, and Victor Kuz’min. "Quantitative structure–activity relationship study on prolonged anticonvulsant activity of terpene derivatives in pentylenetetrazole test." Open Chemistry 19, no. 1 (2021): 1184–92. http://dx.doi.org/10.1515/chem-2021-0108.

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Abstract Quantitative structure–activity relationship (QSAR) study has been conducted on 36 terpene derivatives with anticonvulsant activity in timed pentylenetetrazole (PTZ) infusion test. QSAR models for anticonvulsant activity prediction of hydrazones and esters of some monocyclic/bicyclic terpenoids were developed using simplex representation of molecular structure (SiRMS; informational field [IF]) approach based on the SiRMS and the IF of molecule. Four 2D partial least squares QSAR consensus models were developed with the coefficient of determination for test sets R test 2 > 0.62 {R}_

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Wang, Hui, Mingyue Jiang, Shujun Li, et al. "Design of cinnamaldehyde amino acid Schiff base compounds based on the quantitative structure–activity relationship." Royal Society Open Science 4, no. 9 (2017): 170516. http://dx.doi.org/10.1098/rsos.170516.

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Cinnamaldehyde amino acid Schiff base (CAAS) is a new class of safe, bioactive compounds which could be developed as potential antifungal agents for fungal infections. To design new cinnamaldehyde amino acid Schiff base compounds with high bioactivity, the quantitative structure–activity relationships (QSARs) for CAAS compounds against Aspergillus niger ( A. niger ) and Penicillium citrinum (P. citrinum) were analysed. The QSAR models ( R 2 = 0.9346 for A. niger , R 2 = 0.9590 for P. citrinum, ) were constructed and validated. The models indicated that the molecular polarity and the Max atomic

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Padmakar, V. Khadikar, T. Supuran Claudiu, Das Manikpuri Anju, Singb Shalini, and Lakhwan Meenakshi. "Quantitative Structure-Activity Relationship (QSAR) studies of carbonic anhydrase inhibitors and activators." Journal of Indian Chemical Society Vol. 88, Jan 2011 (2011): 25–85. https://doi.org/10.5281/zenodo.5762844.

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Research Division, Laxmi Fumigation and Pest Control, Pvt. Ltd., 3, Khatipura, lndore-452 007, Madhya Pradesh, India <em>E-mail</em> : pvkhadikar@rediffmail.com Laboratorio di Chimica Bioinorganica, Departmento di Chimica, University of Florence, via della Lastruccia, 3, RM-188, Polo Scientifico, 50019 Sesto Fioventinol, Fireze, Italy <em>E-mail</em> : claudiu.supuran@unifi.it Department of Chemistry, ISLE, IPS Academy, Knowledge City, Rajendranagar, Indore-452 001, Madhya Pradesh, India <em>E-mail</em> : anju.dm@rediffmail.com Department of Chemistry, Barely College, Barely, Uttar Pradesh, In

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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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Gandhi, Ajaykumar, Vijay Masand, Magdi E. A. Zaki, Sami A. Al-Hussain, Anis Ben Ghorbal, and Archana Chapolikar. "Quantitative Structure–Activity Relationship Evaluation of MDA-MB-231 Cell Anti-Proliferative Leads." Molecules 26, no. 16 (2021): 4795. http://dx.doi.org/10.3390/molecules26164795.

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In the present endeavor, for the dataset of 219 in vitro MDA-MB-231 TNBC cell antagonists, a (QSAR) quantitative structure–activity relationships model has been carried out. The quantitative and explicative assessments were performed to identify inconspicuous yet pre-eminent structural features that govern the anti-tumor activity of these compounds. GA-MLR (genetic algorithm multi-linear regression) methodology was employed to build statistically robust and highly predictive multiple QSAR models, abiding by the OECD guidelines. Thoroughly validated QSAR models attained values for various stati

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Chang, Hyun-Joo, Hyun Jung Kim, and Hyang Sook Chun. "Quantitative structure−activity relationship (QSAR) for neuroprotective activity of terpenoids." Life Sciences 80, no. 9 (2007): 835–41. http://dx.doi.org/10.1016/j.lfs.2006.11.009.

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Shang, Zhongrui. "Quantitative structure-activity relationships modelling in antimicrobial peptides design." Theoretical and Natural Science 44, no. 1 (2024): 94–101. http://dx.doi.org/10.54254/2753-8818/44/20240443.

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Antimicrobial peptides are antimicrobial agents with high bioactivity and low bacterial resistance, which inspire scientists to recognize AMPs as appealing candidates for microbial infection treatments. QSAR modeling is one of the most important techniques in computer-aided drug design that is typically beneficial to promoting the design of AMPs. This paper presents an overview about breakthroughs and achievements in AMPs designed by QSAR modeling in recent 10 years, and primarily talks about 6 successful AMPs discoveries covering different QSAR models and solving various antimicrobial problem

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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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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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Kimani, Njogu M., Josphat C. Matasyoh, Marcel Kaiser, Mauro S. Nogueira, Gustavo H. G. Trossini, and Thomas J. Schmidt. "Complementary Quantitative Structure–Activity Relationship Models for the Antitrypanosomal Activity of Sesquiterpene Lactones." International Journal of Molecular Sciences 19, no. 12 (2018): 3721. http://dx.doi.org/10.3390/ijms19123721.

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Three complementary quantitative structure–activity relationship (QSAR) methodologies, namely, regression modeling based on (i) “classical” molecular descriptors, (ii) 3D pharmacophore features, and (iii) 2D molecular holograms (HQSAR) were employed on the antitrypanosomal activity of sesquiterpene lactones (STLs) toward Trypanosoma brucei rhodesiense (Tbr), the causative agent of the East African form of human African trypanosomiasis. In this study, an extension of a previous QSAR study on 69 STLs, models for a much larger and more diverse set of such natural products, now comprising 130 STLs

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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 (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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Paramasivam, Murugarajan. "A note on SDD invariants of clump graphs with Girth size at most three." Asia Mathematika 6, no. 3 (2023): 24——28. https://doi.org/10.5281/zenodo.7551551.

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The symmetric division deg invariant  is one of the 200 discrete Adriatic indices introduced several years ago. This $SDD$ invariant has been already  proved a valuable invariant in the QSAR(Quantitative Structure Activity Relationship) and QSPR(Quantitative Structure Property Relationship) studies. In this article, we present on exact values of $SDD$ invariants of  inorganic Clump graphs with girth size at most three.  

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Gupta, S. P. "QSAR (quantitative structure-activity relationship) studies on local anesthetics." Chemical Reviews 91, no. 6 (1991): 1109–19. http://dx.doi.org/10.1021/cr00006a001.

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Veena, M. G., and V. H. Narendra. "Investigation of Lower and Upper Bounds of a Jump Graph Using Topological Indices." Journal of Advances in Mathematics and Computer Science 38, no. 9 (2023): 105–14. http://dx.doi.org/10.9734/jamcs/2023/v38i91808.

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Topological indices are a type of mathematical measure that relate to the atomic composition of any straight forward finite graph. For quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) analyses [1]. The main aim of this paper is to find new bounds of a jump graph using some topological indices like Hyper Zagreb index, Nirmala Index, VL Index and Forgotten topological index.The Topological indices are mathematical techniques used to mathematically correlate the relationship between the chemical structure and various physical attributes,

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Matusevičiūtė, Ramona, Eglė Ignatavičiūtė, Rokas Mickus, Sergio Bordel, Vytenis Arvydas Skeberdis, and Vytautas Raškevičius. "Evaluation of Cx43 Gap Junction Inhibitors Using a Quantitative Structure-Activity Relationship Model." Biomedicines 11, no. 7 (2023): 1972. http://dx.doi.org/10.3390/biomedicines11071972.

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Gap junctions (GJs) made of connexin-43 (Cx43) are necessary for the conduction of electrical impulses in the heart. Modulation of Cx43 GJ activity may be beneficial in the treatment of cardiac arrhythmias and other dysfunctions. The search for novel GJ-modulating agents using molecular docking allows for the accurate prediction of binding affinities of ligands, which, unfortunately, often poorly correlate with their potencies. The objective of this study was to demonstrate that a Quantitative Structure-Activity Relationship (QSAR) model could be used for more precise identification of potent

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Noviandy, Teuku Rizky, Aga Maulana, Ghazi Mauer Idroes, et al. "Ensemble Machine Learning Approach for Quantitative Structure Activity Relationship Based Drug Discovery: A Review." Infolitika Journal of Data Science 1, no. 1 (2023): 32–41. http://dx.doi.org/10.60084/ijds.v1i1.91.

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This comprehensive review explores the pivotal role of ensemble machine learning techniques in Quantitative Structure-Activity Relationship (QSAR) modeling for drug discovery. It emphasizes the significance of accurate QSAR models in streamlining candidate compound selection and highlights how ensemble methods, including AdaBoost, Gradient Boosting, Random Forest, Extra Trees, XGBoost, LightGBM, and CatBoost, effectively address challenges such as overfitting and noisy data. The review presents recent applications of ensemble learning in both classification and regression tasks within QSAR, sh

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Dearden, J. C., M. T. D. Cronin, and K. L. E. Kaiser. "How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR)." SAR and QSAR in Environmental Research 20, no. 3-4 (2009): 241–66. http://dx.doi.org/10.1080/10629360902949567.

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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 signif

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Yang, Qing, and Xin Qiu. "Quantitative Structure-Activity Relationship between Compound Molecular Characteristics and Nanofiltration Separation Efficiency." Advanced Materials Research 168-170 (December 2010): 1185–88. http://dx.doi.org/10.4028/www.scientific.net/amr.168-170.1185.

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The aim of this study is to establish a certain Quantitative Structure-Activity Relationship (QSAR) between compound molecular characteristics and nanofiltration (NF) separation efficiency. Measurements were carried out in a crossflow NF unit and using ten organic compounds (ethanol, butyl alcohol, glycerin, phenol, glucose, sorbitolum, dodecanoic acid, Imidacloprid, sucrose and Dimethomorph) in aqueous solution and two commercial NF membranes (DK and NF90). Four kind compound characteristics of Molecular weight (Mw), Octanol-Water Partition Coefficient (logP), Molar Refraction (CMR), Henry’s

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Stankova, Ivanka, Radoslav Chayrov, Michaela Schmidtke, et al. "Quantitative structure-activity relationship modelling of influenza M2 ion channels inhibitors." Journal of the Serbian Chemical Society 86, no. 7-8 (2021): 625–37. http://dx.doi.org/10.2298/jsc200509036s.

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A series of adamantane derivatives (rimantadine and amantadine) incorporating amino-acid residues are investigated by simplex representation of molecular structure (SiRMS) approach in order to found correlation between chemical structures of investigated compounds and obtained data for antiviral activity and cytotoxicity. The obtained data from QSAR analysis show that adamantane derivatives containing amino acids with short aliphatic non-polar residues in the lateral chain will have good antiviral activity against the tested virus A/H3N2, strain Hong Kong/68 with low cytotoxicity. QSAR experim

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Naik, Pradeep Kumar, Abhishek Dubey, and Rishay Kumar. "Development of Predictive Quantitative Structure-Activity Relationship Models of Epipodophyllotoxin Derivatives." Journal of Biomolecular Screening 15, no. 10 (2010): 1194–203. http://dx.doi.org/10.1177/1087057110380743.

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Epipodophyllotoxins are the most important anticancer drugs used in chemotherapy for various types of cancers. To further, improve their clinical efficacy a large number of epipodophyllotoxin derivatives have been synthesized and tested over the years. In this study, a quantitative structure-activity relationship (QSAR) model has been developed between percentage of cellular protein-DNA complex formation and structural properties by considering a data set of 130 epipodophyllotoxin analogues. A systematic stepwise searching approach of zero tests, missing value test, simple correlation test, mu

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Nasution, Hasmalina, Nur Enizan, Nurlaili Nurlaili, and Jufrizal Syahri. "Design of Trolox Compounds as Antioxidant and Their Analysis Using Quantitative Structure Activity Relationship." Acta Chimica Asiana 3, no. 2 (2020): 181. http://dx.doi.org/10.29303/aca.v3i2.40.

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Antioxidant compound can inhibit the oxidation of lipids and other biomolecules. The role of antioxidants is very important in neutralizing and destroying free radicals that can cause the damage to cells in the body. This research was carried out to design trolox derivate compounds as antioxidants using the QSAR method. The semi empirical AM1(Austin Model 1)method was used to generate the QSAR model. The statistical analysis result using multiple linier regression methods revealed thet antioxidant activity was influenced by the descriptors of qC1, qC4, qO7, qC13 and qO18. The QSAR equation mod

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Dearden, John C. "Quantitative structure-activity relationships (QSAR) and odour." Food Quality and Preference 5, no. 1-2 (1994): 81–86. http://dx.doi.org/10.1016/0950-3293(94)90011-6.

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Pranowo, Harno Dwi, Iqmal Tahir, and Ajidarma Widiatmoko. "QUANTITATIVE RELATIONSHIP OF ELECTRONIC STRUCTURE AND INHIBITION ACTIVITY OF CURCUMIN ANALOGS ON ETHOXYRESORUFIN o-DEALKYLATION (EROD) REACTION." Indonesian Journal of Chemistry 7, no. 1 (2010): 78–82. http://dx.doi.org/10.22146/ijc.21717.

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Electronic structure and inhibition activity relationship study of curcumin analogs has been established for 29 curcumin analogs on Ethoxyresorufin O-Dealkylation (EROD) reaction using atomic net charge descriptor based on AM1 semiempirical calculations. The QSAR (Quantitative Structure and Activities Relationships) equation model was determined by statistical parameter from multiple regression analysis and leave-one-out cross validation method. The best QSAR equation was described: Keywords: curcumin, QSAR, descriptor, atomic net charge, semiempirical methods.

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Gupta, Satya P., Veena Mulchandani, Subharanjan Das, Arun Subbiah, D. Narsimha Reddy, and Jyoti Sinha. "A Quantitative Structure-Activity Relationship Study on Some Cholecystokinin Antagonists." Quantitative Structure-Activity Relationships 14, no. 5 (1995): 437–43. http://dx.doi.org/10.1002/qsar.19950140505.

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Alvarez-Ginarte, Yoanna María, Rachel Crespo-Otero, Yovani Marrero-Ponce та ін. "Quantitative Structure–Activity Relationship of the 4,5α-Dihydrotestosterone Steroid Family". QSAR & Combinatorial Science 25, № 10 (2006): 881–94. http://dx.doi.org/10.1002/qsar.200510162.

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Sushil, kumar sah*and Birendra Shrivasatava. "QSAR MODELLING OF NEW TRIAZOLOTHIADIAZOLE DERIVATIVES AS ANTIMICROBIALS." Indo American Journal of Pharmaceutical Sciences 05, no. 01 (2018): 42–51. https://doi.org/10.5281/zenodo.1135271.

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In this research, twenty nine analogues having variable inhibition of E.coli were subjected to quantitative structure activity relationship analysis. Various thermodynamic, electronic and steric parameters were calculated using Chem 3D package of molecular modeling software Chemoffice 8.0. QSAR models were generated employing sequential multiple regression method using in–house statistical program VALSTAT. Statistically significant models with R–values 0.90 were obtained. Models were validated using leave one out and bootstrapping methods. Results obtained shows that stretch energy

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Veerasamy, Ravichandran, Harish Rajak, Abhishek Jain, Shalini Sivadasan, Christapher P. Varghese, and Ram Kishore Agrawal. "Validation of QSAR Models - Strategies and Importance." International Journal of Drug Design and Discovery 2, no. 3 (2011): 511–19. https://doi.org/10.37285/ijddd.2.3.1.

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Quantitative Structure-Activity Relationship (QSAR) is based on the hypothesis that changes in molecular structure reflect changes in the observed response or biological activity. The success of any quantitative structure–activity relationship model depends on the accuracy of the input data, selection of appropriate descriptors, statistical tools and the validation of the developed model. Validation is a crucial aspect of QSAR modeling. Validation is the process by which the reliability and significance of a procedure are established for a specific purpose. Hence in this review we focus on the

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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 (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 a

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