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Thematische Bibliographien / QSAR Model

Inhaltsverzeichnis

Dissertationen

Auswahl der wissenschaftlichen Literatur zum Thema „QSAR Model“

Autor: Grafiati

Veröffentlicht am 18. Mai 2024

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Dissertationen zum Thema "QSAR Model"

1

Spreafico, Morena. "Mixed-model QSAR at the glucocorticoid and liver X receptors /." [S.l.] : [s.n.], 2009. http://edoc.unibas.ch/diss/DissB_8730.

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2

Bagchi, Bhaskar. "Quantum chemical calculation and structure activity relationship of bioactive terpenoids." Thesis, University of North Bengal, 2016. http://ir.nbu.ac.in/handle/123456789/2762.

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3

Raynaud, Isabelle. "Etude des relations structure-activité quantitatives (QSAR) des cytokinines : synthèse et activité biologique de nouvelles molécules actives." Angers, 1996. http://www.theses.fr/1996ANGE0022.

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Un modèle statistique de relations structure-activité de type quantitatif des cytokinines, hormones végétales, a été mis au point. Il a été construit à partir de 26 molécules appartenant aux deux principales classes de cytokinines : les pyridylphenylurées et les purines n#6-substituées. Apres l'analyse conformationnelle de ces 26 molécules, différents alignements ont été construits et testes dans COMFA. Finalement, deux modèles ont été sélectionnés. Ils ont été valides par des échantillons-tests de molécules connues. Le meilleur modèle, ORL1, a ensuite servi pour faire des prédictions d'activi

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4

Mazzatorta, Paolo. "Evaluation of pesticide toxicity : a hierarchical QSAR approach to model the acute aquatic toxicity and avian oral toxicity of pesticides." Thesis, Open University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.424819.

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The thesis aimed to extract information relevant to the hazard and risk assessment of pesticides. In particular, quantitative structure-activity relationship (QSAR) approaches have been used to build up a mathematical model able to predict the aquatic acute toxicity, Leso, and the avian oral toxicity, LDso, for pesticides. Ecotoxicological values were collected from several databases, and screened according to quality criteria. A hierarchical QSAR approach was applied for the prediction of acute aquatic toxicity. Chemical structures were encoded into molecular descriptors by an automated, seam

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5

Malazizi, Ladan. "Development of Artificial Intelligence-based In-Silico Toxicity Models. Data Quality Analysis and Model Performance Enhancement through Data Generation." Thesis, University of Bradford, 2008. http://hdl.handle.net/10454/4262.

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Toxic compounds, such as pesticides, are routinely tested against a range of aquatic, avian and mammalian species as part of the registration process. The need for reducing dependence on animal testing has led to an increasing interest in alternative methods such as in silico modelling. The QSAR (Quantitative Structure Activity Relationship)-based models are already in use for predicting physicochemical properties, environmental fate, eco-toxicological effects, and specific biological endpoints for a wide range of chemicals. Data plays an important role in modelling QSARs and also in result an

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6

Moda, Tiago Luiz. "Desenvolvimento de modelos in silico de propriedades de ADME para a triagem de novos candidatos a fármacos." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/76/76132/tde-22032007-112055/.

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As ferramentas de modelagem molecular e de estudos das relações quantitativas entre a estrutura e atividade (QSAR) ou estrutura e propriedade (QSPR) estão integradas ao processo de planejamento de fármacos, sendo de extremo valor na busca por novas moléculas bioativas com propriedades farmacocinéticas e farmacodinâmicas otimizadas. O trabalho em Química Medicinal realizado nesta dissertação de mestrado teve como objetivo estudar as relações quantitativas entre a estrutura e as propriedades farmacocinéticas biodisponibilidade oral e ligação às proteínas plasmáticas. Para a realização deste trab

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7

MANSOURI, KAMEL. "New molecular descriptors for estimating degradation and fate of organic pollutants by QSAR/QSPR models within reach." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2013. http://hdl.handle.net/10281/45611.

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Organic pollutants that resist degradation in the environment can accumulate in body tissues and cause unavoidable intoxications to organisms in wild life as well as humans. The possible effects, usually increasing with the cumulative exposure to such chemicals, are not always addressed adequately in risk assessment procedures evaluating long and short-term contact hazard. Thus, chemicals accumulation, degradation and environmental fate are of prime concern for REACH when defining side effects due to chronic exposure. Characteristics and behavior of organic pollutants have been investigated e

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8

Dimitriadis, Spyridon. "Multi-task regression QSAR/QSPR prediction utilizing text-based Transformer Neural Network and single-task using feature-based models." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177186.

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With the recent advantages of machine learning in cheminformatics, the drug discovery process has been accelerated; providing a high impact in the field of medicine and public health. Molecular property and activity prediction are key elements in the early stages of drug discovery by helping prioritize the experiments and reduce the experimental work. In this thesis, a novel approach for multi-task regression using a text-based Transformer model is introduced and thoroughly explored for training on a number of properties or activities simultaneously. This multi-task regression with Transformer

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9

Sköld, Christian. "Computational Modeling of the AT₂ Receptor and AT₂ Receptor Ligands : Investigating Ligand Binding, Structure–Activity Relationships, and Receptor-Bound Models." Doctoral thesis, Uppsala University, Organic Pharmaceutical Chemistry, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7823.

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<p>Rational conversion of biologically active peptides to nonpeptide compounds with retained activity is an appealing approach in drug development. One important objective of the work presented in this thesis was to use computational modeling to aid in such a conversion of the peptide angiotensin II (Ang II, Asp-Arg-Val-Tyr-Ile-His-Pro-Phe). An equally important objective was to gain an understanding of the requirements for ligand binding to the Ang II receptors, with a focus on interactions with the AT<sub>2</sub> receptor.</p><p>The bioactive conformation of a peptide can provide important g

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10

Sköld, Christian. "Computational Modeling of the AT2 Receptor and AT2 Receptor Ligands : Investigating Ligand Binding, Structure–Activity Relationships, and Receptor-Bound Models." Doctoral thesis, Uppsala universitet, Avdelningen för organisk farmaceutisk kemi, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7823.

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Rational conversion of biologically active peptides to nonpeptide compounds with retained activity is an appealing approach in drug development. One important objective of the work presented in this thesis was to use computational modeling to aid in such a conversion of the peptide angiotensin II (Ang II, Asp-Arg-Val-Tyr-Ile-His-Pro-Phe). An equally important objective was to gain an understanding of the requirements for ligand binding to the Ang II receptors, with a focus on interactions with the AT2 receptor. The bioactive conformation of a peptide can provide important guidance in peptidomi

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