Relevant bibliographies by topics / Three-Dimensional Quantitative Structure-Activity and Relationship (3D-QSAR)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Three-Dimensional Quantitative Structure-Activity and Relationship (3D-QSAR)'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Three-Dimensional Quantitative Structure-Activity and Relationship (3D-QSAR)"

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Du, Qi-Shi, Ri-Bo Huang, Yu-Tuo Wei, Li-Qin Du, and Kuo-Chen Chou. "Multiple field three dimensional quantitative structure–activity relationship (MF-3D-QSAR)." Journal of Computational Chemistry 29, no. 2 (2007): 211–19. http://dx.doi.org/10.1002/jcc.20776.

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Raunio, Hannu, Laura Korhonen, Miia Turpeinen, et al. "Three-dimensional quantitative structure–activity relationship (3D-QSAR) analysis of CYP2B6 enzyme." Toxicology Letters 164 (September 2006): S66. http://dx.doi.org/10.1016/j.toxlet.2006.06.137.

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Zhao, Manman, Lin Wang, Linfeng Zheng, et al. "2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors." BioMed Research International 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/4649191.

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Epidermal growth factor receptor (EGFR) is an important target for cancer therapy. In this study, EGFR inhibitors were investigated to build a two-dimensional quantitative structure-activity relationship (2D-QSAR) model and a three-dimensional quantitative structure-activity relationship (3D-QSAR) model. In the 2D-QSAR model, the support vector machine (SVM) classifier combined with the feature selection method was applied to predict whether a compound was an EGFR inhibitor. As a result, the prediction accuracy of the 2D-QSAR model was 98.99% by using tenfold cross-validation test and 97.67% b
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Sugumar, Shobana. "VIRTUAL SCREENING, PHARMACOPHORE MODELING, AND QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP STUDIES ON HISTAMINE 4 RECEPTOR." Asian Journal of Pharmaceutical and Clinical Research 10, no. 12 (2017): 150. http://dx.doi.org/10.22159/ajpcr.2017.v10i12.19991.

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Objective: To find out novel inhibitors for histamine 4 receptor (H4R), the target for various allergic and inflammatory pathophysiological conditions.Methods: Homology modeling of H4R was performed using easy modeler and validated using structure analysis and verification server, and with the modeled structure, virtual screening, pharmacophore modeling, and quantitative structure activity relationship (QSAR) studies were performed using the Schrodinger 9.3 software.Results: Among all the synthetic and natural ligands, hesperidin, vitexin, and diosmin were found to have the highest dock score,
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Jagdale, Deepali M., and Ramaa C. S. "QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP ANALYSIS OF NOVEL PYRAZOLINE DERIVATIVES USING K NEAREST NEIGHBOUR MOLECULAR FIELD ANALYSIS METHOD." International Journal of Pharmacy and Pharmaceutical Sciences 9, no. 12 (2017): 87. http://dx.doi.org/10.22159/ijpps.2017v9i12.19401.

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Objective: Malonyl CoA decarboxylase (MCD) enzyme plays important role in fatty acid and glucose oxidation. Inhibition of MCD might turn to a novel approach to treat ischemia. The main objective of this research article was to develop a novel pharmacophore for enhanced activity.Methods: Three-dimensional quantitative structure-activity relationships (3D-QSAR) was performed for pyrazoline derivatives as MCD inhibitors using VLife MDS 4.6 software. The QSAR model was developed using the stepwise 3D-QSAR kNN-MFA method.Results: The statistical results generated from kNN-MFA method indicated the s
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Khalid, Ali Qusay, Vasudeva Rao Avupati, Husniza Hussain, and Tabarek Najeeb Zaidan. "Computational Atom-based Three-Dimensional Quantitative Structure-Activity Relationship (3D QSAR) Model for Predicting Anti-Dengue Agents." Research Journal of Biotechnology 16, no. 10 (2021): 50–58. http://dx.doi.org/10.25303/1610rjbt5058.

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Dengue fever is a viral infection spread by the female mosquito Aedes aegypti. It is a virus spread by mosquitoes found all over the tropics with risk levels varying depending on rainfall, relative humidity, temperature and urbanization. There are no specific medications that can be used to treat the condition. The development of possible bioactive ligands to combat Dengue fever before it becomes a pandemic is a global priority. Few studies on building three-dimensional quantitative structure-activity relationship (3D QSAR) models for anti-dengue agents have been reported. Thus, we aimed at bu
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Bradley, Mary, and Chris L. Waller. "Polarizability Fields for Use in Three-Dimensional Quantitative Structure−Activity Relationship (3D-QSAR)." Journal of Chemical Information and Computer Sciences 41, no. 5 (2001): 1301–7. http://dx.doi.org/10.1021/ci0004659.

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Chandrasekaran, Vasudevan, Georgia B. McGaughey, Chester J. Cavallito, and J. Phillip Bowen. "Three-dimensional quantitative structure–activity relationship (3D-QSAR) analyses of choline acetyltransferase inhibitors." Journal of Molecular Graphics and Modelling 23, no. 1 (2004): 69–76. http://dx.doi.org/10.1016/j.jmgm.2004.04.002.

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Choudhari, Prafulla B., Kundan B. Ingale, Neela M. Bhatia, Manish S. Bhatia, Deepak B. Sangale, and Ramesh L. Sawant. "Two and Three-Dimensional Quantitative Structure-Activity Relationship Analysis on A Series of Anthelmintics." International Journal of Drug Design and Discovery 1, no. 4 (2024): 325–30. https://doi.org/10.37285/ijddd.1.4.6.

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A 2D and 3D quantitative structure activity relationship (QSAR) analysis has been performed on a data set of 29 bezothiazole derivatives as Anthelmintics. Several types of descriptors including topological, spatial, thermodynamic, information content and E-state indices have been used to derive a quantitative relationship between the Anthelmintics activity and structural properties. Statistically significant models was obtained with the descriptors The model was also tested successfully for external validation criteria. The model is not only able to predict the activity of new compounds but al
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B.V.S, Suneel Kumar, Jagarlapudi A. R. P. Sarma, and Lakshmi Narasu. "3D-QSAR studies on Pyrido[2,3-d]pyrimidine Derivatives as Fibroblast Growth Factor Receptor 1 Inhibitors: Application of Molecular Field Analysis (MFA)." International Journal of Drug Design and Discovery 2, no. 4 (2024): 619–32. https://doi.org/10.37285/ijddd.2.4.2.

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Three-dimensional quantitative structure–activity relationship (3D-QSAR) models were developed for 77 pyrido[2,3-d]pyrimidines derivatives, inhibiting fibroblast growth factor receptor 1 (FGFR1). The QSAR model was developed using 56 compounds and its predictive ability was assessed using a test set of 21 compounds. The predictive 3D-QSAR models have conventional r2 values of 0.920 for MFA and the cross-validated coefficient r2cv values of 0.884 for MFA. The results of 3D-QSAR methodologies provide a powerful tool directed to the design of potent and selective pyrido[2,3-d]pyrimidines inhibito
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Dissertations / Theses on the topic "Three-Dimensional Quantitative Structure-Activity and Relationship (3D-QSAR)"

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Ribeiro, Taisa Pereira Piacentini. "Estudo teórico (modelagem molecular e QSAR) de compostos quinolínicos com atividade herbicida." Universidade Estadual do Oeste do Paraná, 2017. http://tede.unioeste.br/handle/tede/2964.

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Submitted by Rosangela Silva (rosangela.silva3@unioeste.br) on 2017-08-30T20:10:51Z No. of bitstreams: 2 TAISA PEREIRA PIACENTINI RIBEIRO.pdf: 3904493 bytes, checksum: 479855c30863d881e3a40de6b85ca548 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)<br>Made available in DSpace on 2017-08-30T20:10:51Z (GMT). No. of bitstreams: 2 TAISA PEREIRA PIACENTINI RIBEIRO.pdf: 3904493 bytes, checksum: 479855c30863d881e3a40de6b85ca548 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-02-09<br>The search for new herbici
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Books on the topic "Three-Dimensional Quantitative Structure-Activity and Relationship (3D-QSAR)"

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author, Panaye Annick, ed. Three dimensional QSAR: Applications in pharmacology and toxicology. CRC Press, 2010.

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Doucet, Jean-Pierre, and Annick Panaye. Three Dimensional Qsar. Taylor & Francis Group, 2010.

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Doucet, Jean-Pierre, and Annick Panaye. Three Dimensional Qsar. Taylor & Francis Group, 2019.

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Doucet, Jean-Pierre, and Annick Panaye. Three Dimensional QSAR: Applications in Pharmacology and Toxicology. Taylor & Francis Group, 2010.

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Book chapters on the topic "Three-Dimensional Quantitative Structure-Activity and Relationship (3D-QSAR)"

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Patrick, Graham L. "Quantitative structure–activity relationships (QSAR)." In An Introduction to Medicinal Chemistry. Oxford University Press, 2023. http://dx.doi.org/10.1093/hesc/9780198866664.003.0027.

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This chapter analyzes quantitative structure-activity relationships (QSARs), which is considered a well-established tool in medicinal chemistry. It details how QSAR attempts to relate the physicochemical properties of compounds to their biological activity in a quantitative fashion by using equations. It also describes traditional QSAR and shows that this typically involves synthesizing a series of analogues with different substituents and studying how the physicochemical properties of the substituents affect the biological activities of the analogues. The chapter covers the hydrophobic, steric, and electronic properties and looks at how these are considered when setting up a QSAR equation. It emphasizes how traditional QSAR studies have been largely superseded by three dimensional QSARs (3D QSAR) where the physicochemical properties of the complete molecule are calculated and then related to biological activity.
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"Chapter 6Spectroscopic QSARs: Quantitative Spectroscopic Data Activity Relationships." In Three Dimensional QSAR. CRC Press, 2010. http://dx.doi.org/10.1201/b10419-12.

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Consonni, Viviana, and Roberto Todeschini. "Structure –Activity Relationships by Autocorrelation Descriptors and Genetic Algorithms." In Chemoinformatics and Advanced Machine Learning Perspectives. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-61520-911-8.ch005.

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Quantitative Structure-Activity Relationships (QSARs) are models relating variation of molecule properties, such as biological activities, to variation of some structural features of chemical compounds. Three main topics take part of the QSAR/QSPR approach to the scientific research: the representation of molecular structure, the definition of molecular descriptors and the chemoinformatics tools. Molecular descriptors are numerical indices encoding some information related to the molecular structure. They can be both experimental physico-chemical properties of molecules and theoretical indices calculated by mathematical formulas or computational algorithms. In the last few decades, much interest has been addressed to studying how to encompass and convert the information encoded in the molecular structure into one or more numbers used to establish quantitative relationships between structures and properties, biological activities or other experimental properties. Autocorrelation descriptors are a class of molecular descriptors based on the statistical concept of spatial autocorrelation applied to the molecular structure. The objective of this chapter is to investigate the chemical information encompassed by autocorrelation descriptors and elucidate their role in QSAR and drug design. After a short introduction to molecular descriptors from a historical point of view, the chapter will focus on reviewing the different types of autocorrelation descriptors proposed in the literature so far. Then, some methodological topics related to multivariate data analysis will be overviewed paying particular attention to analysis of similarity/diversity of chemical spaces and feature selection for multiple linear regressions. The last part of the chapter will deal with application of autocorrelation descriptors to study similarity relationships of a set of flavonoids and establish QSARs for predicting affinity constants, Ki, to the GABAA benzodiazepine receptor site, BzR.
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Saigo, Hiroto, and Koji Tsuda. "Graph Mining in Chemoinformatics." In Chemoinformatics and Advanced Machine Learning Perspectives. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-61520-911-8.ch006.

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In standard QSAR (Quantitative Structure Activity Relationship) approaches, chemical compounds are represented as a set of physicochemical property descriptors, which are then used as numerical features for classification or regression. However, standard descriptors such as structural keys and fingerprints are not comprehensive enough in many cases. Since chemical compounds are naturally represented as attributed graphs, graph mining techniques allow us to create subgraph patterns (i.e., structural motifs) that can be used as additional descriptors. In this chapter, the authors present theoretically motivated QSAR algorithms that can automatically identify informative subgraph patterns. A graph mining subroutine is embedded in the mother algorithm and it is called repeatedly to collect patterns progressively. The authors present three variations that build on support vector machines (SVM), partial least squares regression (PLS) and least angle regression (LARS). In comparison to graph kernels, our methods are more interpretable, thereby allows chemists to identify salient subgraph features to improve the druglikeliness of lead compounds.
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Meena, Grandhi Sai, and Koushik Yetukuri. "Artificial Neural Network Process Used in Global Drug Discovery and Development." In Current Trends in Drug Discovery, Development and Delivery (CTD4-2022). Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/9781837671090-00094.

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One of the branches of Artificial Intelligence (AI) that is most frequently employed in the advancement of medicine is artificial neural networks (ANN). An alternative name for neural networks is ANN. These are the computing systems inclined by the biological nervous system. It is built on a network of interconnected components known as artificial neurons. Similar to a biological neuron, the ANN is also having the interconnection nodes. The ANN is a simple mathematical function. This model has three sets of rules. Those are multiplication, summation and activation. In order to predict the properties of chemical compounds for drug discovery, ANNs are used. It has been demonstrated mathematically that an ANN may be used to roughly predict the link between any chemical property and its structure. To understand the significance of ANN in the drug discovery and development process is the primary goal of this article. Information was acquired from the USFDA’s official website as well as other research and review journals. ANN have been widely applied in a variety of key pharmacy-related fields,?from clinical pharmacy to bio pharmacy to the interpretation of analytical data and the creation of drugs and dosage forms. It is commonly implemented in many areas of the pharmaceutical industry, including quantitative structure-activity relationships (QSAR) research and the analysis of chemicals used in drug discovery and development. ANN is useful for resolving nonlinear problems in multivariate and multi-response systems, including space analysis in QSAR in pharmacokinetic studies and structure prediction in drug development.
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Conference papers on the topic "Three-Dimensional Quantitative Structure-Activity and Relationship (3D-QSAR)"

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Ragno, Rino, and Alessio Ragno. "db.3d-qsar.com. The first 3D QSAR models database." In 2nd International Conference on Chemo and Bioinformatics. Institute for Information Technologies, University of Kragujevac, 2023. http://dx.doi.org/10.46793/iccbi23.051r.

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Field-Based Three-Dimensiona Quantitative Strucutere-Activity Relationships (FB 3D QSAR) comprise computational approaches used in drug design and molecular modeling to analyze the relationship between the three-dimensional structure of a list of molecules (described by molecular interaction fields) and their associated biological activities (BAs). It aims to understand how different structural features of the molecules contribute to enhancing or lowering the biological potency. The process of FB 3D QSAR involves several steps. First, a dataset of structurally diverse molecules with known BAs
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Ramaswamy, Shri, Shuiyuan Huang, Amit Goel, et al. "The 3D Structure of Paper and its Relationship to Moisture Transport in Liquid and Vapor Forms." In The Science of Papermaking, edited by C. F. Baker. Fundamental Research Committee (FRC), Manchester, 2001. http://dx.doi.org/10.15376/frc.2001.2.1289.

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The three dimensional structure of paper materials plays a critical role in the paper manufacturing process especially via its impact on the transport properties for fluids. Dewatering of the wet web, pressing and drying will benefit from knowledge of the relationships between the web structure and its transport coefficients. Among transport, moisture diffusion in paper is central to the understanding and optimal design of paper products for their performance in different environmental conditions. Our recent research of moisture sorption in paper has indicated that diffusion of water vapor thr
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Brown, Ronald, Shannon White, Jennifer Goode, Prachi Pradeep, and Stephen Merrill. "Use of QSAR Modeling to Predict the Carcinogenicity of Color Additives." In ASME 2013 Conference on Frontiers in Medical Devices: Applications of Computer Modeling and Simulation. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/fmd2013-16161.

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Patients may be exposed to potentially carcinogenic color additives released from polymers used to manufacture medical devices; therefore, the need exists to adequately assess the safety of these compounds. The US FDA Center for Devices and Radiological Health (CDRH) recently issued draft guidance that, when final, will include FDA’s recommendations for the safety evaluation of color additives and other potentially toxic chemical entities that may be released from device materials. Specifically, the draft guidance outlines an approach that calls for evaluating the potential for the color addit
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Shigeta, Yuji, Masatoshi Aramaki, Kentaro Kudo, et al. "Understanding The Effect Of Process Parameters On Three-dimensional Pore Configurations And Mechanical Properties Of Laser Additive Manufactured Ti Using Synchrotron X-ray Computed Tomography And Homology." In World Powder Metallurgy 2022 Congress & Exhibition. EPMA, 2022. http://dx.doi.org/10.59499/wp225369761.

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To establish a quantitative relationship between fabrication parameters, pore-configuration, and mechanical properties in porous materials, the pore configurations were visualized by X-ray computed tomography (CT) about pure Ti-AM specimens fabricated under different laser beam conditions. Radius and sphericity of all pores were obtained by image analysis. Furthermore, using the 0th persistent homology, the pore configurations were qualified by the pair of birth and death values, b and d for all pores: |b| means a pore radius and d correspond to a half distance between adjacent pores. The sign
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Petit, Jimmy, Jose Rouillard, and Francois Cabestaing. "Design and study of two applications controlled by a Brain-Computer Interface exploiting Steady-State Somatosensory-Evoked Potentials." In 8th International Conference on Human Interaction and Emerging Technologies. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002787.

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Brain-Computer Interfaces (BCI) allow users to interact with machines without involving muscles. Patients with heavy motor impairment can benefit from these systems. Different states of mind of a user are discriminated to translate them into basic commands (left, right, etc.). But traditional BCI are mainly based on visual attention, and users can be quickly tired (eye fatigue, repetitive tasks, etc.). In some cases, the sight is not available for a relevant BCI, while the sense of touch can remain usable.We have implemented an electroencephalography-based BCI using the user's sense of touch.
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