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Relevant bibliographies by topics / Quantitative structure property relationship (QSPR)

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Academic literature on the topic 'Quantitative structure property relationship (QSPR)'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Quantitative structure property relationship (QSPR)"

1

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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Manjunath, Muddalapuram, V. Lokesha, Suvarna, and Sushmitha Jain. "Bounds for the Topological Indices of ℘ graph." European Journal of Pure and Applied Mathematics 14, no. 2 (2021): 340–50. http://dx.doi.org/10.29020/nybg.ejpam.v14i2.3715.

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Topological indices are mathematical measure which correlates to the chemical structures of any simple finite graph. These are used for Quantitative Structure-Activity Relationship (QSAR) and Quantitative Structure-Property Relationship (QSPR). In this paper, we define operator graph namely, ℘ graph and structured properties. Also, establish the lower and upper bounds for few topological indices namely, Inverse sum indeg index, Geometric-Arithmetic index, Atom-bond connectivity index, first zagreb index and first reformulated Zagreb index of ℘-graph.

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

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

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Dissertations / Theses on the topic "Quantitative structure property relationship (QSPR)"

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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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Cypcar, Christopher Charles. "Investigation of structure-property relationships of nylon 6-co-7 and linear alkyl model amide compounds and molecular modeling quantitative structure-property relationship (QSPR) for glass temperature predictions." Aix-Marseille 3, 1997. http://www.theses.fr/1997AIX30035.

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Santos, Victor Hugo Jacks Mendes dos. "Uma perspectiva da modelagem QSPR para triagem/desenho de catalisadores para a s?ntese de carbonatos oleoqu?micos." Pontif?cia Universidade Cat?lica do Rio Grande do Sul, 2018. http://tede2.pucrs.br/tede2/handle/tede/8260.

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Submitted by PPG Engenharia e Tecnologia de Materiais (engenharia.pg.materiais@pucrs.br) on 2018-08-27T20:28:46Z No. of bitstreams: 1 Uma perspectiva da modelagem QSPR para triagem-desenho de catalisadores para a s?ntese de carbona.pdf: 5038129 bytes, checksum: cd9bae4ba9eacd711c360bc304996732 (MD5)<br>Approved for entry into archive by Sheila Dias (sheila.dias@pucrs.br) on 2018-08-28T12:42:24Z (GMT) No. of bitstreams: 1 Uma perspectiva da modelagem QSPR para triagem-desenho de catalisadores para a s?ntese de carbona.pdf: 5038129 bytes, checksum: cd9bae4ba9eacd711c360bc304996732 (MD5)<br>Made

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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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Gaudin, Théophile. "Développement de modèles QSPR pour la prédiction et la compréhension des propriétés amphiphiles des tensioactifs dérivés de sucre." Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2318/document.

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Les tensioactifs dérivés de sucres représentent la principale famille de tensioactifs bio-sourcés et constituent de bons candidats pour substituer les tensioactifs dérivés du pétrole puisqu'ils sont issus de ressources renouvelables et peuvent être autant, voire plus performants dans diverses applications, comme la formulation (détergents, cosmétiques,…), la récupération assistée du pétrole ou des minéraux, etc. Différentes propriétés amphiphiles permettent de caractériser la performance des tensioactifs dans de telles applications, comme la concentration micellaire critique, la tension de sur

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Jover, Modrego Jesús. "Aplicació de la metodologia QSPR al càlcul de propietats de compostos inorgànics i de sistemes multicomponents." Doctoral thesis, Universitat de Barcelona, 2008. http://hdl.handle.net/10803/665934.

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En esta tesis se ha utilizado la metodología QSPR para calcular las propiedades de diferentes compuestos y sistemas complejos que no habían estudiados anteriormente. En concreto, se han establecido modelos que permiten el cálculo de la viscosidad y la tensión superficial, en estado líquido, y la entalpía de formación en fase gas para conjuntos de compuestos organometálicos de fórmula general MRnXm, en la que M puede ser un metal, semimetal o no metal de los grupos 12 al 16 de la tabla periódica; los grupos R corresponden a sustituyentes orgánicos alquílicos, arílicos, etc.; y los ligandos term

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Martínez, Brito Izacar Jesús. "Quantitative structure fate relationships for multimedia environmental analysis." Doctoral thesis, Universitat Rovira i Virgili, 2010. http://hdl.handle.net/10803/8590.

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Key physicochemical properties for a wide spectrum of chemical pollutants are unknown. This thesis analyses the prospect of assessing the environmental distribution of chemicals directly from supervised learning algorithms using molecular descriptors, rather than from multimedia environmental models (MEMs) using several physicochemical properties estimated from QSARs. Dimensionless compartmental mass ratios of 468 validation chemicals were compared, in logarithmic units, between: a) SimpleBox 3, a Level III MEM, propagating random property values within statistical distributions of widely reco

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Ruark, Christopher Daniel. "The Guinea Pig Model For Organophosphate Toxicology and Therapeutic Development." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1432890247.

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Wanchana, Suchada. "Quantitative structure/property relationship modeling of pharmacokinetic properties using genetic algorithm-combined partial least squares method." 京都大学 (Kyoto University), 2003. http://hdl.handle.net/2433/148610.

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Mlynarczyk, Paul John. "The nature and determination of the dynamic glass transition temperature in polymeric liquids." Kansas State University, 2014. http://hdl.handle.net/2097/17782.

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Master of Science<br>Department of Chemical Engineering<br>Jennifer L. Anthony<br>A polymer has drastically different physical properties above versus below some characteristic temperature. For this reason, the precise identification of this glass transition temperature, T[subscript]g, is critical in evaluating product feasibility for a given application. The objective of this report is to review the behavior of polymers near their T[subscript]g and assess the capability of predicting T[subscript]g using theoretical and empirical models. It was determined that all polymers begin to undergo st

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Books on the topic "Quantitative structure property relationship (QSPR)"

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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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(Editor), M. Charton, and B. I. Charton (Editor), eds. Advances in Quantitative Structure-Property Relationships, Volume 2 (Advances in Quantative Structure - Property Relationships). JAI Press, 1999.

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Book chapters on the topic "Quantitative structure property relationship (QSPR)"

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Ruby-Figueroa, René. "Quantitative Structure-Property Relationships (QSPR)." In Encyclopedia of Membranes. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-44324-8_2001.

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Ruby-Figueroa, René. "Quantitative Structure-Property Relationships (QSPR)." In Encyclopedia of Membranes. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-40872-4_2001-1.

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Clark, Robert D., and Pankaj R. Daga. "Building a Quantitative Structure-Property Relationship (QSPR) Model." In Methods in Molecular Biology. Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9089-4_8.

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Fioressi, Silvina E., Daniel E. Bacelo, and Pablo R. Duchowicz. "Quantitative Structure–Property Relationships (QSPR) for Materials Science." In Challenges and Advances in Computational Chemistry and Physics. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-78736-2_4.

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Wadhwa, Pankaj, and Amit Mittal. "Quantitative Structure-Property Relationship (QSPR) Modeling Applications in Formulation Development." In Computer Aided Pharmaceutics and Drug Delivery. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5180-9_17.

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Behera, Swayam Aryam, P. Kali Krishna, and P. Ganga Raju Achary. "Quantitative Structure–Property Relationships (QSPR) and Machine Learning (ML) Models for Materials Science." In Challenges and Advances in Computational Chemistry and Physics. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-78736-2_5.

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Pedone, Alfonso, and Maria Cristina Menziani. "Computational Modeling of Silicate Glasses: A Quantitative Structure-Property Relationship Perspective." In Molecular Dynamics Simulations of Disordered Materials. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15675-0_5.

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Rasulev, Bakhtiyor, Ana Lončarić Božić, Dionysios D. Dionysiou, and Hrvoje Kušić. "Modeling of Photooxidative Degradation of Aromatics in Water Matrix: A Quantitative Structure−Property Relationship Approach." In ACS Symposium Series. American Chemical Society, 2019. http://dx.doi.org/10.1021/bk-2019-1331.ch012.

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Feldstein, Mikhail M., and Nicolai A. Platé. "A Structure — Property Relationship and Quantitative Approach to the Development of Universal Transdermal Drug Delivery System." In NBC Risks Current Capabilities and Future Perspectives for Protection. Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4641-8_34.

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Cuadrado, Manuel Urbano, Irene Luque Ruiz, Gonzalo Cerruela García, and Miguel Ángel Gómez-Nieto. "A New Quantitative Structure-Property Relationship (QPRS) Model based on Topological Distances of Non-Isomorphic Subgraphs." In Advances in Computational Methods in Sciences and Engineering 2005 (2 vols). CRC Press, 2022. http://dx.doi.org/10.1201/9780429077166-31.

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Conference papers on the topic "Quantitative structure property relationship (QSPR)"

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Yakimov, B. P., A. A. Rubekina, L. S. Urusova, and E. A. Shirshin. "Discovery of novel fluorophores in the human organism with quantitative structure-property relationship approach." In 2024 International Conference Laser Optics (ICLO). IEEE, 2024. http://dx.doi.org/10.1109/iclo59702.2024.10624249.

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Oukhemanou, F., A. Maldonado, P. Moreau, and B. Creton. "Application of Quantitative Structure-property Relationship (QSPR) Method for Chemical EOR." In IOR 2013 - 17th European Symposium on Improved Oil Recovery. EAGE Publications BV, 2013. http://dx.doi.org/10.3997/2214-4609.20142620.

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Moreau, Patrick, Fanny Oukhemanou, Ana Maldonado, and Benoit Creton. "Application of Quantitative Structure-Property Relationship (QSPR) Method for Chemical EOR Surfactant Selection." In SPE International Symposium on Oilfield Chemistry. Society of Petroleum Engineers, 2013. http://dx.doi.org/10.2118/164091-ms.

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"Application of machine learning models to predict ecotoxicity of ionic liquids (Vibrio fischeri) using VolSurf principal properties." In Sustainable Processes and Clean Energy Transition. Materials Research Forum LLC, 2023. http://dx.doi.org/10.21741/9781644902516-27.

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Abstract. Owing to the rapid growth in IL synthesis due to feasible cation–anion combinations, knowledge of their toxicity is pertinent for their successful application. Toxicity information measurement of various ILs on a broad spectrum of conditions through experimental techniques is way demanding on time, resources, and is at times impractical. Various research works have been performed in Quantitative Structure Activity/Property Relationship (QSAR/QSPR) for IL toxicity prediction. In this study, ML models have been trained and tested on Vibrio fischeri toxicity data set using in silico pri

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Sennott, T., C. Gotianun, R. Serres, M. Ziabasharhagh, J. H. Mack, and R. Dibble. "Artificial Neural Network for Predicting Cetane Number of Biofuel Candidates Based on Molecular Structure." In ASME 2013 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/icef2013-19185.

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The production of next-generation biofuels is being explored through a variety of chemical and biological approaches, all aiming at lowering costs and increasing yields while producing viable alternatives to gasoline or diesel fuel. Chemical synthesis can lead to a huge variety of different fuels and the guidelines from which molecules yield desirable properties as a fuel are largely based on intuition. One such property of interest is the cetane number (CN), a measure of the ignition quality of diesel fuel. The present work improves on existing models and extends them to more oxygenates (prim

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Won, Sang Hee, Dalton Carpenter, Stuart Nates, and Frederick L. Dryer. "Derived Cetane Number As Chemical Potential Indicator for Near-Limit Combustion Behaviors in Gas Turbine Applications." In ASME 2018 Power Conference collocated with the ASME 2018 12th International Conference on Energy Sustainability and the ASME 2018 Nuclear Forum. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/power2018-7414.

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The objective of this paper is to elucidate the recently observed strong correlation between derived cetane number (DCN) and lean blow out (LBO) characteristics for both petroleum-derived and alternative jet fuels, as well as their blends. In order to evaluate the variability of fuel physical and chemical properties for petroleum-derived jet fuels, the fuel property database appearing in the DSIC-PQIS 2013 report are rigorously analyzed and compared against fuel-specific data for 17 petroleum-derived and alternative jet fuels and their blends obtained previously in our works. The global combus

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Yalamanchi, Kiran K., Pinaki Pal, Balaji Mohan, Abdullah S. AlRamadan, Jihad A. Badra, and Yuanjiang Pei. "Development of a Variational Autoencoder Model for Fuel Design." In ASME 2024 ICE Forward Conference. American Society of Mechanical Engineers, 2024. https://doi.org/10.1115/icef2024-141660.

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Abstract In this work, a Variational Autoencoder (VAE) based modeling framework is developed with the overarching goal of fuel design. The VAE model is trained on a large dataset with several chemical species to learn a compressed latent space representation. The molecular structure is fed to the VAE encoder as SMILES string to extract the latent space representation, which is then decoded back to the SMILES string. Long Short-Term Memory (LSTM) networks are employed to encode and decode SMILES strings by virtue of their ability to capture the sequential nature and hierarchical dependencies in

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Shi, X. D., L. Y. Li, J. L. Li, et al. "Reductive Debronimation Pathway and Quantitative Structure Property Relationship Models for Polybrominated Diphenyl Ethers." In The 2015 International Conference on Software Engineering and Information Technology (SEIT2015). WORLD SCIENTIFIC, 2015. http://dx.doi.org/10.1142/9789814740104_0035.

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Atescan, Yagmur, Ricardo Braga Nogueira Branco, Kohei Oyama, Anna Madra, and Namiko Yamamoto. "Quantitative structure-property relationship study of 1D-aligned soft magnetic nanocomposites for fast actuation." In AIAA Scitech 2021 Forum. American Institute of Aeronautics and Astronautics, 2021. http://dx.doi.org/10.2514/6.2021-0539.

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Li, Hui, Yinghua Lu, Ting Gao, Hongzhi Li, and Lihong Hu. "Generalized Regression Neural Network Based Quantitative Structure-Property Relationship for the Prediction of Absorption Energy." In 2012 National Conference on Information Technology and Computer Science. Atlantis Press, 2012. http://dx.doi.org/10.2991/citcs.2012.123.

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Reports on the topic "Quantitative structure property relationship (QSPR)"

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Morrill, Jason A., Robert E. Jensen, Phillip H. Madison, and Cary F. Chabalowski. Prediction of the Formulation Dependence of the Glass Transition Temperature for Amine-Epoxy Copolymers Using a Quantitative Structure-Property Relationship Based on the AM1 Method. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada420986.

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