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Relevant bibliographies by topics / Quantitative Structure and Biological Activity Relationships

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Academic literature on the topic 'Quantitative Structure and Biological Activity Relationships'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Quantitative Structure and Biological Activity Relationships"

1

Kai, Zhen-peng, Juan Huang, Yong Xie, et al. "Synthesis, Biological Activity, and Hologram Quantitative Structure−Activity Relationships of Novel Allatostatin Analogues†." Journal of Agricultural and Food Chemistry 58, no. 5 (2010): 2652–58. http://dx.doi.org/10.1021/jf902156k.

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Dearden, John C. "The History and Development of Quantitative Structure-Activity Relationships (QSARs)." International Journal of Quantitative Structure-Property Relationships 1, no. 1 (2016): 1–44. http://dx.doi.org/10.4018/ijqspr.2016010101.

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It is widely accepted that modern QSAR began in the early 1960s. However, as long ago as 1816 scientists were making predictions about physical and chemical properties. The first investigations into the correlation of biological activities with physicochemical properties such as molecular weight and aqueous solubility began in 1841, almost 60 years before the important work of Overton and Meyer linking aquatic toxicity to lipid-water partitioning. Throughout the 20th century QSAR progressed, though there were many lean years. In 1962 came the seminal work of Corwin Hansch and co-workers, which

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Dearden, John C. "The History and Development of Quantitative Structure-Activity Relationships (QSARs)." International Journal of Quantitative Structure-Property Relationships 2, no. 2 (2017): 36–46. http://dx.doi.org/10.4018/ijqspr.2017070104.

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Following the publication of the history and development of QSAR, it became apparent that a number of matters had not been covered. This addendum is an attempt to rectify that. A very early approach (ca. 60 B.C.) by Lucretius shows that he understood how molecular size and complexity affect liquid viscosity. Comments by Kant (1724-1804) emphasized the necessity of mathematics in science. A claim that the work of von Bibra and Harless in 1847 pre-dated that of Overton and H.H. Meyer is shown not to be correct. K.H. Meyer and Gottlieb-Billroth published in 1920 what is probably the first QSAR eq

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Pasquale, Gustavo, Gustavo P. Romanelli, Juan C. Autino, Javier García, Erlinda V. Ortiz, and Pablo R. Duchowicz. "Quantitative Structure–Activity Relationships of Mosquito Larvicidal Chalcone Derivatives." Journal of Agricultural and Food Chemistry 60, no. 2 (2012): 692–97. http://dx.doi.org/10.1021/jf203374r.

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Reino, José L., Liane Saiz-Urra, Rosario Hernández-Galán, et al. "Quantitative Structure−Antifungal Activity Relationships of Some Benzohydrazides againstBotrytis cinerea." Journal of Agricultural and Food Chemistry 55, no. 13 (2007): 5171–79. http://dx.doi.org/10.1021/jf0704211.

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de Souza, Anacleto S., Leonardo L. G. Ferreira, Aldo S. de Oliveira, and Adriano D. Andricopulo. "Quantitative Structure–Activity Relationships for Structurally Diverse Chemotypes Having Anti-Trypanosoma cruzi Activity." International Journal of Molecular Sciences 20, no. 11 (2019): 2801. http://dx.doi.org/10.3390/ijms20112801.

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Small-molecule compounds that have promising activity against macromolecular targets from Trypanosoma cruzi occasionally fail when tested in whole-cell phenotypic assays. This outcome can be attributed to many factors, including inadequate physicochemical and pharmacokinetic properties. Unsuitable physicochemical profiles usually result in molecules with a poor ability to cross cell membranes. Quantitative structure-activity relationship (QSAR) analysis is a valuable approach to the investigation of how physicochemical characteristics affect biological activity. In this study, artificial neura

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KIRINO, Osamu, Chiyozo TAKAYAMA, Mototsugu YOSHIDA, Satoru INOUE, and Ryo YOSHIDA. "Quantitative structure-activity relationships of the fungicidal and herbicidal thiolcarbamates." Agricultural and Biological Chemistry 52, no. 2 (1988): 561–68. http://dx.doi.org/10.1271/bbb1961.52.561.

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Sunnerheim, Kerstin, Anneli Nordqvist, Göran Nordlander, et al. "Quantitative Structure–Activity Relationships of Pine Weevil Antifeedants, a Multivariate Approach." Journal of Agricultural and Food Chemistry 55, no. 23 (2007): 9365–72. http://dx.doi.org/10.1021/jf070014p.

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Beezer, A. E., P. L. O. Volpe, C. A. Gooch, W. H. Hunter, and R. J. Miles. "Quantitative structure-activity relationships: microcalorimetric determination of a group additivity scheme for biological response." International Journal of Pharmaceutics 29, no. 2-3 (1986): 237–42. http://dx.doi.org/10.1016/0378-5173(86)90121-3.

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Bushelev, S. N., and N. F. Stepanov. "Molecular Orbital Analysis and Quantitative Structure-Activity Relationships for the Anthracycline Antibiotics." Zeitschrift für Naturforschung C 44, no. 3-4 (1989): 212–16. http://dx.doi.org/10.1515/znc-1989-3-407.

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Abstract Semiempirical CNDO/2 calculations of the electronic and molecular structures have been carried out for six antitumor anthracycline antibiotics. The strong correlation between their biological activity and such molecular properties as chromophore dipole moment, energy of frontier orbitals, and steric volum es was found and the proper QSAR equations were constructed. The molecular mechanics model showed the weak dependence the DNA-intercalation energy on differences in chromophore constituents (C4 and C9 positions). The expression for the intercalation energy has also been obtained on

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Dissertations / Theses on the topic "Quantitative Structure and Biological Activity Relationships"

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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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Raminelli, Cristiano. "Estudo da relação quantitativa entre a estrutura química e atividade citotóxica de séries de derivados de bases de Mannich." Universidade de São Paulo, 2001. http://www.teses.usp.br/teses/disponiveis/46/46135/tde-27032019-103221/.

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Bases de Mannich têm sido sintetizadas como pró-fármacos de cetonas α,β-insaturadas, 22, 20 sendo estas importantes no tratamento do câncer. 20 Assim, toma-se de interesse o desenvolvimento de estudos de QSAR envolvendo bases de Mannich com propriedades citotóxicas e/ou anticâncer, contribuindo-se, para o entendimento da(s) interação(ões) destes compostos (agentes alquilantes) no sistema biológico. 40, 5, 36, 53 Neste trabalho, destinado a dissertação de mestrado, foram preparadas e purificadas por métodos triviais descritos na literatura 8 47 duas séries de derivados de bases de Man

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Malvezzi, Alberto. "Estudo comparativo de parâmetros hipidrofóbicos e, relacionados à ionização, de série de derivados da procaína com atividade bloqueadora neuromuscular." Universidade de São Paulo, 2003. http://www.teses.usp.br/teses/disponiveis/46/46135/tde-12082008-140635/.

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O estudo das Relações Quantitativas entre Estrutura química e Atividade biológica (QSAR/QSAR-3D) utilizando diferentes estratégias metodológicas complementares, aplicadas iterativamente, se estende a diferentes áreas de aplicação. Dentre elas destaca-se o planejamento racional de novos fármacos e o estudo de seus mecanismos de ação. Moléculas candidatas a fármacos, geradas em estudos de QSAR-3D, freqüentemente, não se tomam fármacos por apresentarem inadequadas propriedades farmacocinéticas, ou seja, ADME (absorção, distribuição, metabolismo e excreção). Nota-se assim que o conhecimento das pr

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Bruce, Craig L. "Classification and interpretation in quantitative structure-activity relationships." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/11666/.

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A good QSAR model comprises several components. Predictive accuracy is paramount, but it is not the only important aspect. In addition, one should apply robust and appropriate statistical tests to the models to assess their significance or the significance of any apparent improvements. The real impact of a QSAR, however, perhaps lies in its chemical insight and interpretation, an aspect which is often overlooked. This thesis covers three main topics: a comparison of contemporary classifiers, interpretability of random forests and usage of interpretable descriptors. The selection of data mining

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McNeany, T. John. "Non-parametric approaches to quantitative structure-activity relationships." Thesis, University of Nottingham, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.431188.

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Ishiki, Hamilton Mitsugu. "Relações quantitativas entre estrutura química e atividade biológica (QSAR/QSAR-3D) de compostos com potencial atividade antituberculose." Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/46/46135/tde-25042016-172925/.

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A tuberculose (TB) é uma doença causada pelo Mycobacterium tuberculosis. De acordo com estimativas da Organização Mundial da Saúde, a tuberculose é responsável pela morte de ~2 a 3 milhões de pessoas/ano no mundo e nos próximos 15 anos cerca de 1 bilhão de pessoas deverão ser infectadas, e destas, aproximadamente 35 milhões deverão morrer. Apesar de existirem vários medicamentos sendo utilizados no tratamento da doença, constatasse o crescimento no número de casos devido, principalmente, às variedades resistentes do M. tuberculosis. Considerando-se o aparecimento de cepas resistentes em TB, re

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Chen, Jonathan Jun Feng. "Data Mining/Machine Learning Techniques for Drug Discovery: Computational and Experimental Pipeline Development." University of Akron / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=akron1524661027035591.

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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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Cronin, Mark T. D. "Quantitative structure-activity relationships of comparative toxicity to aquatic organisms." Thesis, Liverpool John Moores University, 1990. http://researchonline.ljmu.ac.uk/4989/.

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Quantitative Structure-Activity relationship (QSAR) attempt statistically to relate the physico-chemical properties of a molecule to its biological activity. A QSAR analysis was performed on the toxicities of up to 75 organic chemicals to two aquatic species, Photobacterium phospherum (known as the Microtox test), and the fathead minnow. To model the toxicities 49 physico-chemical and structural parameters were produced including measures of hydrophobicity, molecular size and electronic effects from techniques such as computational chemistry and the use of molecular connectivity indices. These

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Gooch, Carolyn A. "Quantitative structure-activity relationships : a biophysical, chemical and calorimetric study." Thesis, Royal Holloway, University of London, 1988. http://repository.royalholloway.ac.uk/items/26719d55-b208-4995-bef0-92e4f0f80c0e/1/.

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Quantitative structure-activity relationships (QSAR) rationalize interrelation between molecular structure and biological response in terms of either physicochemical parameters, as in linear free energy relationships (LFER), or via purely empirical parameters, as is the case for De Novo schemes. In LFER the leading process is often the partitioning of a compound between two solvent phases, taken to represent the transfer of a drug molecule across a biological membrane. This study has investigated the partitioning behaviour of three series of hydroxybenzoate esters, viz. o-, m- and predominantl

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Books on the topic "Quantitative Structure and Biological Activity Relationships"

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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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Reduced rank regression: With applications to quantitative structure-activity relationships. Physica-Verlag, 1995.

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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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4

Roy, Kunal. Quantitative structure-activity relationships in drug design, predictive toxicology, and risk assessment. Medical Information Science Reference, an imprint of IGI Global, 2015.

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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. 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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Lin, Guo-Qiang. Chiral drugs: Chemistry and biological action. Wiley, 2011.

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

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International Workshop on QSAR in Environmental Toxicology (3rd 1988 Knoxville, Tenn.). QSAR 88: Proceedings of the third International Workshop on Quantitative Structure-Activity Relationships in Environmental Toxicology : May 22-26, 1988, Knoxville, Tennessee. USDOE office of Scientific and Technical Information, 1988.

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Book chapters on the topic "Quantitative Structure and Biological Activity Relationships"

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Klebe, Gerhard. "Quantitative Structure–Activity Relationships." In Drug Design. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-17907-5_18.

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Neu, H. C. "Structure-activity relationships: biological". У The Chemistry of β-Lactams. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2928-2_3.

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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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García-Sánchez, Mario Omar, Maykel Cruz-Monteagudo, and José L. Medina-Franco. "Quantitative Structure-Epigenetic Activity Relationships." In Challenges and Advances in Computational Chemistry and Physics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56850-8_8.

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Davis, Andrew M. "CHAPTER 6. Quantitative Structure–Activity Relationships." In The Handbook of Medicinal Chemistry. Royal Society of Chemistry, 2015. http://dx.doi.org/10.1039/9781782621836-00154.

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Gupta, Satya P. "Quantitative structure-activity relationships of antihypertensive agents." In Progress in Drug Research. Birkhäuser Basel, 1999. http://dx.doi.org/10.1007/978-3-0348-8735-9_2.

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Maguna, F. P., N. B. Okulik, and Eduardo A. Castro. "Quantitative Structure–Activity Relationships of Antimicrobial Compounds." In Handbook of Computational Chemistry. Springer Netherlands, 2016. http://dx.doi.org/10.1007/978-94-007-6169-8_38-2.

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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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Folkers, G., and A. Merz. "Hydrophobic Fields in Quantitative Structure-Activity Relationships." In Lipophilicity in Drug Action and Toxicology. Wiley-VCH Verlag GmbH, 2008. http://dx.doi.org/10.1002/9783527614998.ch13.

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Könemann, Hans. "Quantitative Structure-Activity Relationships in Aquatic Toxicology." In Organic Micropollutants in the Aquatic Environment. Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4660-6_50.

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Conference papers on the topic "Quantitative Structure and Biological Activity Relationships"

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Angelo, Rafaela M., Andreia K. Io, Matheus P. Almeida, et al. "OntoQSAR: an Ontology for Interpreting Chemical and Biological Data in Quantitative Structure-Activity Relationship Studies." In 2020 IEEE 14th International Conference on Semantic Computing (ICSC). IEEE, 2020. http://dx.doi.org/10.1109/icsc.2020.00042.

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Liu, Rong, Robert Rallo, and Yoram Cohen. "Quantitative Structure-Activity-Relationships for cellular uptake of nanoparticles." In 2013 IEEE 13th International Conference on Nanotechnology (IEEE-NANO). IEEE, 2013. http://dx.doi.org/10.1109/nano.2013.6720861.

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Xu, Jingbo, and Tisong Jing. "Quantitative Structure-Activity Relationships for Oryzias Latipes Gill ATPase Endpoint." In 2008 2nd International Conference on Bioinformatics and Biomedical Engineering (ICBBE '08). IEEE, 2008. http://dx.doi.org/10.1109/icbbe.2008.296.

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Butkiewicz, Mariusz, Ralf Mueller, Danilo Selic, Eric Dawson, and Jens Meiler. "Application of machine learning approaches on quantitative structure activity relationships." In 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2009. http://dx.doi.org/10.1109/cibcb.2009.4925736.

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Al-Amood, Hanoy K., Hanan F. Al-Shamsi, and Hayat H. Abbas. "Quantitative structure-activity relationships of some new beta amino-carbonyl compounds." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2019. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0029650.

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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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Kimani, Njogu, Josphat Matasyoh, Marcel Kaiser, Mauro Nogueira, Gustavo Trossini, and Thomas Schmidt. "An extended study on quantitative structure-antitrypanosomal activity relationships of sesquiterpene lactones." In 4th International Electronic Conference on Medicinal Chemistry. MDPI, 2018. http://dx.doi.org/10.3390/ecmc-4-05591.

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"2D-Quantitative Structure Activity Study and the Computational Prediction of Antibacterial Activity for Series of Quinolones Derivatives." In International Conference on Chemical, Agricultural and Biological Sciences. Emirates Research Publishing, 2015. http://dx.doi.org/10.17758/erpub.er915027.

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LABUTE, P. "BINARY QSAR: A NEW METHOD FOR THE DETERMINATION OF QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 1998. http://dx.doi.org/10.1142/9789814447300_0044.

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Stitou, Mourad, Hamid Toufik, Mohammed Bouachrine, Hssain Bih, and Fatima Lamchouri. "Machine learning algorithms used in Quantitative structure-activity relationships studies as new approaches in drug discovery." In 2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS). IEEE, 2019. http://dx.doi.org/10.1109/isacs48493.2019.9068917.

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Reports on the topic "Quantitative Structure and Biological Activity Relationships"

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Turner, J. E. (Quantitative structure-activity relationships in environmental toxicology). Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/6613721.

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