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Relevant bibliographies by topics / Quantitative structure-activity relationship (QSAR)

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Academic literature on the topic 'Quantitative structure-activity relationship (QSAR)'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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

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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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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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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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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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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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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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Dissertations / Theses on the topic "Quantitative structure-activity relationship (QSAR)"

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Reddy, Badinehal Asrith. "COMMERCIALIZATION OF A QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP TOOL - SARCHITECT." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1295637833.

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Smith, Mark David. "A quantitative structure-activity relationship (QSAR) study of the Ames mutagenicity assay." Thesis, University of Portsmouth, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343333.

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In-vitro mutagenicity assays have traditionally been used for first line identification of potential genotoxic hazard, purporting to chemical carcinogenesis and heritable genetic damage. The recent advances m combinatorial chemistry and high throughput screening technologies have led to a massive explosion in numbers of possible therapeutic candidates being produced at the early stages of drug discovery. This rapid increase in the number of chemicals to be classified results in a greater need for to acquire alternative methods for the prediction of toxicity. Quantitative StructureActivity Rela

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Lanevskij, Kiril. "Absorption and Tissue Distribution of Drug-Like Compounds: Quantitative Structure-Activity Relationship Analysis." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2011. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2011~D_20111003_114235-89858.

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The objective of this work was to develop mechanistic quantitative structure activity relationship models that would facilitate the assessment of drug properties related to their absorption and distribution in the body. The analysis involved several parameters reflecting the rate of passive diffusion across brain endothelium and intestinal epithelium, and thermodynamic constants related to drug distribution between plasma and tissues. Permeation through cellular transport barriers was modeled by nonlinear equations relating the passive diffusion rate to physicochemical properties of drugs: lip

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Diaz-Perez, Maria-Jose. "Quantitative structure-activity relationship (QSAR) study of the effect of steroids on DNA replication." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/NQ50291.pdf.

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Ahlberg, Helgee Ernst. "Improving drug discovery decision making using machine learning and graph theory in QSAR modeling." Göteborg : Dept. of Chemistry, University of Gothenburg, 2010. http://gupea.ub.gu.se/dspace/handle/2077/21838.

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Stanforth, Robert William. "Extending K-Means clustering for analysis of quantitative structure activity relationships (QSAR)." Thesis, Birkbeck (University of London), 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.500005.

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A Quantitative Structure-Activity Relationship (QSAR) study is an attempt to model some biological activity over a collection of chemical compounds in terms of their structural properties A QSAR model may be constructed through (typically linear) multivariate regression analysis of the biological activity data against a number of features or 'descriptors' of chemical structure. As with any regression model, there are a number of issues emerging in real applications, including (a) domain of applicability of the model, (b) validation of the model within its domain of applicability, and (c) possi

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Ruark, Christopher Daniel. "Quantitative Structure-Activity Relationships for Organophosphates Binding to Trypsin and Chymotrypsin." Wright State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=wright1278010674.

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Wang, Fang. "Chlorine Contribution to Quantitative Structure and Activity Relationship Models of Disinfection By-Products' Quantum Chemical Descriptors and Toxicities." FIU Digital Commons, 2009. http://digitalcommons.fiu.edu/etd/174.

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Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: 1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbi

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Andersson, Patrik. "Physico-chemical characteristics and quantitative structure-activity relationships of PCBs." Doctoral thesis, Umeå University, Chemistry, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-17.

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<p>The polychlorinated biphenyls (PCBs) comprise a group of 209 congeners varying in the number of chlorine atoms and substitution patterns. These compounds tend to be biomagnified in foodwebs and have been shown to induce an array of effects in exposed organisms. The structural characteristics of the PCBs influence their potency as well as mechanism of action. In order to assess the biological potency of these compounds a multi-step quantitative structure-activity relationship (QSAR) procedure was used in the project described in this thesis.</p><p>The ultraviolet absorption (UV) spectra were

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Peng, Xiaoling. "Methods of variable selection and their applications in quantitative structure-property relationship (QSPR)." HKBU Institutional Repository, 2005. http://repository.hkbu.edu.hk/etd_ra/594.

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Books on the topic "Quantitative structure-activity relationship (QSAR)"

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1956-, Devillers J., and Balaban Alexandru T, eds. Topological indices and related descriptors in QSAR and QSPR. Gordon and Breach, 1999.

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Romualdo, Benigni, ed. Quantitative structure-activity relationship (QSAR) models of mutagens and carcinogens. CRC Press, 2003.

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name, No. Quantitative structure-activity relationship (QSAR) models of mutagens and carcinogens. CRC Press, 2002.

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International Workshop on Quantitative Structure-Activity Relationships in Environmental Sciences (7th 1996 Elsinore, Denmark). Quantitative structure-activity relationships in environmental sciences, VII. SETAC Press, 1997.

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Smith, Mark David. A quantitative structure-activity relationship (QSAR) study of the Ames mutagenicity assay. [University of Portsmouth?], 2000.

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Mager, Peter P. Multivariate chemometrics in QSAR (quantitative structure-activity relationships): A dialogue. Research Studies Press, 1988.

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Martin, Yvonne Connolly. Quantitative drug design: A critical introduction. 2nd ed. CRC Press/Taylor & Francis, 2010.

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Martin, Yvonne Connolly. Quantitative drug design: A critical introduction. 2nd ed. Taylor & Francis, 2010.

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Martin, Yvonne Connolly. Quantitative drug design: A critical introduction. 2nd ed. Taylor & Francis, 2010.

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Prakash, Gupta Satya, and Bahal R, eds. QSAR and molecular modeling studies in heterocyclic drugs. Springer-Verlag, 2006.

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Book chapters on the topic "Quantitative structure-activity relationship (QSAR)"

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Dastmalchi, Siavoush, Maryam Hamzeh-Mivehroud, and Babak Sokouti. "QSAR at a Glance." In Quantitative Structure–Activity Relationship. CRC Press, 2018. http://dx.doi.org/10.1201/9781351113076-1.

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Dastmalchi, Siavoush, Maryam Hamzeh-Mivehroud, and Babak Sokouti. "Validation of QSAR Models." In Quantitative Structure–Activity Relationship. CRC Press, 2018. http://dx.doi.org/10.1201/9781351113076-6.

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Kumar, T. Durai Ananda. "Quantitative-Structure Activity Relationship (QSAR)." In Drug Design: A Conceptual Overview. CRC Press, 2022. http://dx.doi.org/10.1201/9781003298755-5.

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Schmidli, Heinz. "Quantitative Structure Activity Relationships (QSAR)." In Contributions to Statistics. Physica-Verlag HD, 1995. http://dx.doi.org/10.1007/978-3-642-50015-2_2.

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Sabljic, Aleksandar, and Yoshiaki Nakagawa. "Biodegradation and Quantitative Structure-Activity Relationship (QSAR)." In ACS Symposium Series. American Chemical Society, 2014. http://dx.doi.org/10.1021/bk-2014-1174.ch004.

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Sabljic, Aleksandar, and Yoshiaki Nakagawa. "Sorption and Quantitative Structure-Activity Relationship (QSAR)." In ACS Symposium Series. American Chemical Society, 2014. http://dx.doi.org/10.1021/bk-2014-1174.ch005.

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Lombardo, Anna, Onofrio Schifanella, Alessandra Roncaglioni, and Emilio Benfenati. "Quantitative Structure-Activity Relationship (QSAR) in Ecotoxicology." In Encyclopedia of Aquatic Ecotoxicology. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5704-2_86.

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Gombar, Vijay K. "Quantitative Structure-Activity Relationship Studies: Acute Toxicity of Environmental Contaminants." In QSAR in Environmental Toxicology - II. Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3937-0_11.

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Winkler, David A. "Overview of Quantitative Structure-Activity Relationships (QSAR)." In Molecular Analysis and Genome Discovery. John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470020202.ch16.

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Passino, Dora R. May, and Stephen B. Smith. "Quantitative Structure-Activity Relationships (QSAR) and Toxicity Data in Hazard Assessment." In QSAR in Environmental Toxicology - II. Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3937-0_20.

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Conference papers on the topic "Quantitative structure-activity relationship (QSAR)"

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Durnie, W. H. "Modelling the Functional Behaviour of Corrosion Inhibitors." In CORROSION 2004. NACE International, 2004. https://doi.org/10.5006/c2004-04401.

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Abstract The inhibition efficiency of organic compounds is dependent on many factors, some of which include the molecular size, charge density, number of adsorption sites, and mode of interaction between the inhibitor and the surface. Adsorption at surfaces is linked predominantly to the electronic structure of the inhibitor molecules, and it is well known that slight changes in the chemistry of an inhibitor can instigate dramatic changes on its efficacy. Many different forms of quantitative structure activity relationships (QSAR’s) have been developed to describe the efficacy of corrosion inh

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Agarwal, Abhishek, Pradeep Rathore, Vinay Jain, and Beena Rai. "In-silico Model for Predicting the Corrosion Inhibition Efficiency of Steel Inhibitors." In CORROSION 2019. NACE International, 2019. https://doi.org/10.5006/c2019-13329.

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Abstract Quantitative Structure-Activity Relationships (QSAR) based models have been widely used for predicting corrosion inhibition performance of metals. However, one of the major limitations in these studies is that the authors have restricted themselves to use only a single class of molecules having similar molecular structure. In this study, a computational end-to-end framework was developed to investigate the properties of organic corrosion inhibitors which are responsible for inhibition of steel in acidic solution. The framework consists of modules like data preprocessing, descriptor se

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Bendiksen, B., and J. S. Gill. "Molecular Modeling in Scale Control - An Experimental Verification and ITS Limitations." In CORROSION 1996. NACE International, 1996. https://doi.org/10.5006/c1996-96164.

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Abstract This work centers on the use of molecular modeling to study scale inhibition of alkaline earth scales such as calcium carbonate and calcium sulfate. The molecular modeling studies have shown the interaction of phosphonates with varied crystal faces of the scales. The software makes it possible to visualize the important crystal faces and match the geometry of the inhibitor to the crystal planes that determine the morphology of the resulting solid. Molecular Dynamics (MD) calculations can reveal specific interactions of inhibitors with scale surfaces. Quantitative Structure Activity Re

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Du, Zhenjiao, and Yonghui Li. "Quantitative Structure-activity Relationship Study on Antioxidant Dipeptides." In 2022 AOCS Annual Meeting & Expo. American Oil Chemists' Society (AOCS), 2022. http://dx.doi.org/10.21748/cpyc1755.

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Antioxidative peptides have attracted increasing interest of researchers and consumers. Compared to wet chemistry methods, quantitative structure-activity relationship (QSAR) analysis as a in silicon method can be more efficient and cost effective and has been successfully applied to activity prediction of angiotensin I-converting enzyme inhibitory activity and bitterness of peptides. However, there are only few QSAR studies on antioxidative activity, particularly for dipeptides which have demonstrated ideal absorption ability in intestinal compared to larger peptides. This study aimed to cond

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Triatmaja, K., SY Prabawati, and PD Widiakongko. "QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP (QSAR) STUDY OF EUGENOL DERIVATIVES AS ANTIOXIDANT COMPOUNDS." In International conference on food, nutrition, health and lifestyle. The International Institute of Knowledge Management, 2022. http://dx.doi.org/10.17501/26827026.2022.1102.

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S, Sushma, Nithish Sundaram, and N. Jayapandian. "Machine learning based Unique Perfume Flavour Creation Using Quantitative Structure-Activity Relationship (QSAR)." In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2021. http://dx.doi.org/10.1109/iccmc51019.2021.9418246.

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Ridzuan, M. S. M., M. Z. Jaafar, and M. M. Zain. "Quantitative structure-activity relationship (QSAR) modelling of N-aryl derivatives as cholinesterase inhibitors." In 2012 IEEE Symposium on Humanities, Science and Engineering Research (SHUSER). IEEE, 2012. http://dx.doi.org/10.1109/shuser.2012.6269006.

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Beljkas, Milan, Jelena Rebić, Milica Radan, Teodora Đikić, Slavica Oljačić, and Katarina Nikolic. "3D-Quantitative Structure-Activity Relationship and design of novel Rho-associated protein kinases-1 (ROCK1) inhibitors." In 2nd International Conference on Chemo and Bioinformatics. Institute for Information Technologies, University of Kragujevac, 2023. http://dx.doi.org/10.46793/iccbi23.584b.

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Rho-associated coiled-coil kinases (ROCKs) are involved in essential cellular functions such as adhesion, contraction, motility, proliferation, and cell survival/apoptosis. Four ROCK inhibitors have already been approved by the FDA and are used to treat glaucoma (ripasudil and netarsudil), cerebral vasospasm (fasudil), and graft-versus-host disease (belumosudil). Recent studies have focused on exploring the role of ROCK kinase inhibitors in cancer treatment and the development of new ROCK inhibitors. The main objective of this study was to identify critical structural features relevant to the

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Fanelli, F., M. C. Menziani, M. Cocchi, A. Carotti, and P. G. De Benedetti. "Theoretical approaches to quantitative structure-activity relationship (QSAR) analysis of M1-muscarinic receptor-ligand complexes." In The first European conference on computational chemistry (E.C.C.C.1). AIP, 1995. http://dx.doi.org/10.1063/1.47825.

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Edgar, Julie A., and Susie Hurley. "The Use of Quantitative Structure Activity Relationships (QSAR) in Traction Fluid Design." In 2004 SAE Fuels & Lubricants Meeting & Exhibition. SAE International, 2004. http://dx.doi.org/10.4271/2004-01-2009.

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Reports on the topic "Quantitative structure-activity relationship (QSAR)"

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Ruangpornvisuti, Vithaya. A Study of conformational equilibrium of semicarbazone derivatives and their complexes with cations : research report. Chulalongkorn University, 2006. https://doi.org/10.58837/chula.res.2006.36.

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The structure optimizations of picolinaldehyde N-oxide thiosemicarbazone (Hpiotsc), 2-benzoylpyridine semicarbazone (H2BzPS), their imino tautomers and their complexes with Ni(II), Cu(II) and Zn(II) were carried out using DFT calculations at the B3LYP/LANL2DZ level of theory. Thermodynamic properties of tautomerizations of Hpiotsc and H2BzPS and complexations of their complexes derived from the frequency calculations at the same level were obtained. The B3LYP/LANL2DZ-optimized geometry parameters for the complexes of [[Ni(Hpiotsc)[subscript 2]][superscript 2+]], [Cu(Hpiotsc).Cl[subscript 2]] a

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Dashtey, Ahmed, Patrick Mormile, Sandra Pedre, Stephany Valdaliso, and Walter Tang. Prediction of PFOA and PFOS Toxicity through Log P and Number of Carbon with CompTox and Machine Learning Tools. Florida International University, 2024. http://dx.doi.org/10.25148/ceefac.2024.00202400.

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Perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS) are two major groups of PFAS will be subjected to the Maximal Contamination Concentration (MCL) of 4 ng/l in drinking water to be implemented by the U.S. EPA by 2025. How to accurately predict toxicity of PFAS with varied carbon chain length is important for treatment and sequential removal from drinking water. This study presents Quantitative Structure and Activity Relationship (QSAR) models developed through both linear regression and two order regression. Log P is compiled from reference and carbon content is counted as

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