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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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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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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'activité sur des molécules hypothétiques. Ces molécules ont par la suite été synthétisées et leur activité biologique a été déterminée. Il s'agit d'une guanidine prédite active mais qui s'est révélée instable après des examens complémentaires. Deux pyridylphenylurées portant un groupement sulfinyl ou sulfonyl en 6 sur la pyridine se sont révélées actives, comme le prédisait le modèle. D'autre part, deux pyridylbenzylurées, analogues des deux molécules précédentes, prédites actives par le modèle, possèdent en fait un effet inhibiteur. Le modèle choisi (ORL1) semble fiable pour la prédiction de molécules rigides telles que les pyridylphenylurées ou les styrylpurines.
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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, seamless procedure available within the OpenMolGRID system. Different linear and non-linear regression techniques were used to obtain reliable and thoroughly validated QSARs. The final model was developed by a counter-propagation neural network coupled with genetic algorithms for variable selection. The proposed QSAR is consistent with McFarland's principle for biological activity and makes use of seven molecular descriptors. The model was assessed thoroughly in test (R2 = 0.8) and validation sets (R2 = 0.72), the y-scrambling test and a sensitivity/stability test. The second endpoint considered in this thesis was avian oral toxicity. As previously, the chemical description of chemicals was generated automatically by the OpenMolGRID system. The best classification model was chosen on the basis of the performances on a validation set of 19 data points, and was obtained from a support vector machine using 94 data points and nine variables selected by genetic algorithms (Error Ratetraining = 0.021, Error Ratevaiidalion = 0.158). The model allowed for a mechanistic estimation of the toxicological action. In fact, several descriptors selected for the final classification model encode for the interaction of the pesticides with other molecules. The presence of hetero-atoms, e.g. sulphur atoms, is correlated with the toxicity, and the pool of descriptor selected is generally dependent from the 3D conformation of the structures. These suggest that, in the case of avian oral toxicity, pesticides probably exert their toxic action through the interaction with some macromolecule and/or protein of the biological system.
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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 analysis for toxicity testing processes. This research addresses number of issues in predictive toxicology. One issue is the problem of data quality. Although large amount of toxicity data is available from online sources, this data may contain some unreliable samples and may be defined as of low quality. Its presentation also might not be consistent throughout different sources and that makes the access, interpretation and comparison of the information difficult. To address this issue we started with detailed investigation and experimental work on DEMETRA data. The DEMETRA datasets have been produced by the EC-funded project DEMETRA. Based on the investigation, experiments and the results obtained, the author identified a number of data quality criteria in order to provide a solution for data evaluation in toxicology domain. An algorithm has also been proposed to assess data quality before modelling. Another issue considered in the thesis was the missing values in datasets for toxicology domain. Least Square Method for a paired dataset and Serial Correlation for single version dataset provided the solution for the problem in two different situations. A procedural algorithm using these two methods has been proposed in order to overcome the problem of missing values. Another issue we paid attention to in this thesis was modelling of multi-class data sets in which the severe imbalance class samples distribution exists. The imbalanced data affect the performance of classifiers during the classification process. We have shown that as long as we understand how class members are constructed in dimensional space in each cluster we can reform the distribution and provide more knowledge domain for the classifier.
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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 trabalho, conjuntos padrões de dados foram organizados para as propriedades biodisponibilidade e ligação às proteínas plasmáticas contendo a informação qualificada sobre a estrutura química e a propriedade alvo correspondente. Os conjuntos de dados criados formaram as bases científicas para o desenvolvimento dos modelos preditivos empregando os métodos holograma QSAR e VolSurf. Os modelos finais de HQSAR e VolSurf gerados neste trabalho possuem elevada consistência interna e externa, apresentando bom poder de correlação e predição das propriedades alvo. Devido à simplicidade, robustez e consistência, estes modelos são guias úteis em Química Medicinal nos estágios iniciais do processo de descoberta e desenvolvimento de fármacos.Molecular modeling tools and quantitative structure-activity relantionships (QSAR) or structure-property (QSPR) are integrated into the drug design process in the search for new bioactive molecules with good pharmacokinetic and pharmacodynamic properties. The Medicinal Chemistry work carried out in this Masters dissertation concerned studies of the quantitative relationshisps between chemical structure and the pharmacokinetic properties oral bioavailability and plasma protein binding. In the present work, standard data sets for bioavailability and plasma protein binding were organized encompassing the structural information and corresponding pharmacokinetic data. The created data sets established the scientific basis for the development of predictive models using the hologram QSAR and VolSurf methods. The final HQSAR and VolSurf models posses high internal and external consistency with good correlative and predictive power. Due to the simplicity, robustness and effectivess, these models are useful guides in Medicinal Chemistry in the early stages of the drug discovery and development process.
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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 experimentally during the last decades by use of various methods of trace analysis. However, the available data still contains several gaps. In this aim, REACH promotes the use of alternative methods to reduce the number of animal tests and suggests in-silico methods such as Quantitative Structure-Activity Relationships (QSARs) to fill the lack of knowledge.
The goal of this thesis, in the framework of the ECO-ITN project, was to build QSAR models with high reliability based on good experimental data for optimal estimation of environmental endpoints of interest for REACH. New molecular descriptors and feature selection techniques have been tested paying particular attention to the validation steps and applicability domain definition.
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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 based model is inspired by the field of Natural Language Processing (NLP) which uses prefix tokens to distinguish between each task. In order to investigate our architecture two data categories are used; 133 biological activities from ExCAPE database and three physical chemistry properties from MoleculeNet benchmark datasets. The Transformer model consists of the embedding layer with positional encoding, a number of encoder layers, and a Feedforward Neural Network (FNN) to turn it into a regression problem. The molecules are represented as a string of characters using the Simplified Molecular-Input Line-Entry System (SMILES) which is a ’chemistry language’ with its own syntax. In addition, the effect of Transfer Learning is explored by experimenting with two pretrained Transformer models, pretrained on 1.5 million and on 100 million molecules. The text-base Transformer models are compared with a feature-based Support Vector Regression (SVR) with the Tanimoto kernel where the input molecules are encoded as Extended Connectivity Fingerprint (ECFP), which are calculated features. The results have shown that Transfer Learning is crucial for improving the performance on both property and activity predictions. On bioactivity tasks, the larger pretrained Transformer on 100 million molecules achieved comparable performance to the feature-based SVR model; however, overall SVR performed better on the majority of the bioactivity tasks. On the other hand, on physicochemistry property tasks, the larger pretrained Transformer outperformed SVR on all three tasks. Concluding, the multi-task regression architecture with the prefix token had comparable performance with the traditional feature-based approach on predicting different molecular properties or activities. Lastly, using the larger pretrained models trained on a wide chemical space can play a key role in improving the performance of Transformer models on these tasks.
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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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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 peptidomimetic design. By designing and introducing well-defined secondary structure mimetics into Ang II the bioactive conformation can be addressed. In this work, both γ- and β-turn mimetic scaffolds have been designed and characterized for incorporation into Ang II. Using conformational analysis and the pharmacophore recognition method DISCO, a model was derived of the binding mode of the pseudopeptide Ang II analogues. This model indicated that the positioning of the Arg side chain was important for AT2 receptor binding, which was also supported when the structure–activity relationship of Ang II was investigated by performing a glycine scan.
To further examine ligand binding, a 3D model of the AT2 receptor was constructed employing homology modeling. Using this receptor model in a docking study of the ligands, binding modes were identified that were in agreement with data from point-mutation studies of the AT2 receptor.
By investigating truncated Ang II analogues, small pseudopeptides were developed that were structurally similar to nonpeptide AT2 receptor ligands. For further guidance in ligand design of nonpeptide compounds, three-dimensional quantitative structure–activity relationship models for AT1 and AT2 receptor affinity as well as selectivity were derived.
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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 peptidomimetic design. By designing and introducing well-defined secondary structure mimetics into Ang II the bioactive conformation can be addressed. In this work, both γ- and β-turn mimetic scaffolds have been designed and characterized for incorporation into Ang II. Using conformational analysis and the pharmacophore recognition method DISCO, a model was derived of the binding mode of the pseudopeptide Ang II analogues. This model indicated that the positioning of the Arg side chain was important for AT2 receptor binding, which was also supported when the structure–activity relationship of Ang II was investigated by performing a glycine scan. To further examine ligand binding, a 3D model of the AT2 receptor was constructed employing homology modeling. Using this receptor model in a docking study of the ligands, binding modes were identified that were in agreement with data from point-mutation studies of the AT2 receptor. By investigating truncated Ang II analogues, small pseudopeptides were developed that were structurally similar to nonpeptide AT2 receptor ligands. For further guidance in ligand design of nonpeptide compounds, three-dimensional quantitative structure–activity relationship models for AT1 and AT2 receptor affinity as well as selectivity were derived.
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Holm, Lotta. „The MHC-glycopeptide-T cell interaction in collagen induced arthritis : a study using glycopeptides, isosteres and statistical molecular design in a mouse model for rheumatoid arthritis“. Doctoral thesis, Umeå : Department of Chemistry, Umeå University, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-899.
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12
Alwassil, Osama I. „Elaboration and Design of α7 nAChR Negative Allosteric Modulators“. VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3902.
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α7 Neuronal nicotinic acetylcholine receptors are one of two major classes of receptors responsible for cholinergic neurotransmission in the central nervous system. The existence of α7 neuronal nAChRs in different regions of the nervous system suggests their involvement in certain essential physiological functions as well as in disorders such as Alzheimer’s disease (AD), drug dependence, and depression. This project was aimed toward the discovery and development of small–molecule arylguanidines that modulate α7 nAChR function with improved subtype-selectivity through an allosteric approach. Identifying the required structural features of these small molecules allowed optimization of their negative allosteric modulator (NAM) actions at α7 neuronal nAChRs. MD-354 (3-chlorophenylguanidine) was the first small–molecule NAM at α7 nAChRs; however, it also binds at 5-HT3 receptors. The N-methyl analog of MD-354 appeared to be more selective toward α7 nAChRs than 5-HT3 receptors. Comparative studies using two series of novel compounds based on MD-354 and its N-methyl analog explored the aryl 3-position and investigated whether or not the MD-354 series and the N-methyl series bind in the same manner. Biological potencies of the MD-354 series and the N-methyl series of compounds, obtained from electrophysiological assays with Xenopus laevis oocytes expressing human α7 nAChRs in two-electrode voltage-clamp assays, showed that N-(3-iodophenyl)-N- methylguanidine (28) is the most potent analog at α7 nAChRs. Our comparative study and Hansch analyses indicated different binding modes of the two series.
In addition, we investigated: i) the length/size of the aliphatic side chain at the anilinic nitrogen, ii) the effect of alkylating the guanidine nitrogen atoms, and iii) the necessity of the presence of these nitrogen atoms for the inhibitory effects of arylguanidines at α7 nAChRs.
In efforts to explain the varied functional activity of these arylguanidines, homology models of the extracellular domain and the transmembrane domain of human α7 nAChRs were developed, allosteric sites identified, and docking studies and hydropathic analysis conducted. The 3D quantitative structure-activity relationships for our compounds were also analyzed using CoMFA. A pharmacophore for arylguanidines as α7 nAChR NAMs was identified.
Together, these data should be useful for the subsequent design of novel arylguanidine analogs for their potential treatment of neurological disorders.
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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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CONCU, RICCARDO. „New QSAR models based on Markov Chains to predict protein functions“. Doctoral thesis, Università degli Studi di Cagliari, 2010. http://hdl.handle.net/11584/266281.
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Peterson, Shane. „Improved CoMFA Modeling by Optimization of Settings : Toward the Design of Inhibitors of the HCV NS3 Protease“. Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8140.
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Zhong, Shifa. „Permanganate Reaction Kinetics and Mechanisms and Machine Learning Application in Oxidative Water Treatment“. Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1618686803768471.
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Hobocienski, Bryan Christopher. „Locality-Dependent Training and Descriptor Sets for QSAR Modeling“. The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1577716259011585.
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Simões, Rodolfo da Silva. „Técnicas de transferência de aprendizagem aplicadas a modelos QSAR para regressão“. Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-07062018-120939/.
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Para desenvolver um novo medicamento, pesquisadores devem analisar os alvos biológicos de uma dada doença, descobrir e desenvolver candidatos a fármacos para este alvo biológico, realizando em paralelo, testes em laboratório para validar a eficiência e os efeitos colaterais da substância química. O estudo quantitativo da relação estrutura-atividade (QSAR) envolve a construção de modelos de regressão que relacionam um conjunto de descritores de um composto químico e a sua atividade biológica com relação a um ou mais alvos no organismo. Os conjuntos de dados manipulados pelos pesquisadores para análise QSAR são caracterizados geralmente por um número pequeno de instâncias e isso torna mais complexa a construção de modelos preditivos. Nesse contexto, a transferência de conhecimento utilizando informações de outros modelos QSAR\'s com mais dados disponíveis para o mesmo alvo biológico seria desejável, diminuindo o esforço e o custo do processo para gerar novos modelos de descritores de compostos químicos. Este trabalho apresenta uma abordagem de transferência de aprendizagem indutiva (por parâmetros), tal proposta baseia-se em uma variação do método de Regressão por Vetores Suporte adaptado para transferência de aprendizagem, a qual é alcançada ao aproximar os modelos gerados separadamente para cada tarefa em questão. Considera-se também um método de transferência de aprendizagem por instâncias, denominado de TrAdaBoost. Resultados experimentais mostram que as abordagens de transferência de aprendizagem apresentam bom desempenho quando aplicadas a conjuntos de dados de benchmark e a conjuntos de dados químicosTo develop a new medicament, researches must analyze the biological targets of a given disease, discover and develop drug candidates for this biological target, performing in parallel, biological tests in laboratory to validate the effectiveness and side effects of the chemical substance. The quantitative study of structure-activity relationship (QSAR) involves building regression models that relate a set of descriptors of a chemical compound and its biological activity with respect to one or more targets in the organism. Datasets manipulated by researchers to QSAR analysis are generally characterized by a small number of instances and this makes it more complex to build predictive models. In this context, the transfer of knowledge using information other\'s QSAR models with more data available to the same biological target would be desirable, nince its reduces the effort and cost to generate models of chemical descriptors. This work presents an inductive learning transfer approach (by parameters), such proposal is based on a variation of the Vector Regression method Adapted support for learning transfer, which is achieved by approaching the separately generated models for each task. It is also considered a method of learning transfer by instances, called TrAdaBoost. Experimental results show that learning transfer approaches perform well when applied to some datasets of benchmark and dataset chemical
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Martins, Ulisses Nicola. „Síntese e desenvolvimento de um modelo de QSAR para derivados do (-)-borneol contra larvas de Aedes aegypti“. Universidade Federal de Sergipe, 2016. https://ri.ufs.br/handle/riufs/3945.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESAedes aegypti is the main transmitter of vector-borne diseases such, dengue, chikungunya and zika. Hence no vaccine exists as well as drugs to reduce viremia, major strategy to prevent these diseases is controlling vector spreading. The main larvicides used are organophosphates and pyrethroids, however the indiscriminate use of these compounds gave rise Ae. aegypti resistant strains. A viable alternative to classic insecticides / larvicides is the phytochemical research of molecules, like terpenoids, component of essential oils of several plants that exhibits larvicidal activity. In this context, the aim of this study was synthetize and evaluate the activity of monoterpene (-)-borneol and twelve derivatives against Ae. aegypti larvae, using QSAR as strategy for discovery of new larvicidal agents. Reactions were carried out by esterification with acid chloride in basic medium, (-)-borneol was modificated in hydroxyl group, changing size and type of the side chain. Compounds synthetized were purified in column chromatography and characterized by NMR ¹³C, ¹H, MS and IR. LC50 was evaluated thought larvicidal assay, in each test, twenty third instar larvae were exposed to various concentrations of derivatives to 24h. From mortality data obtained, LC50 was determined though Probit analysis. The bornylchloroacetate derivative exhibited the best activity (21 ppm), but bornyl heptanoate showed no activity. The physicochemical properties of the derivatives were obtained by GAMESS® module Chem3D Ultra 7.0® software from most stable conformation of the molecule. Descriptor chosen for QSAR study was Log P, since best correlation obtained and especially to be a highly informative parameter to measure influence on the larvicidal activity. Equation was obtained by MiniTab16 ™ software by linear regression between derivatives Log P, and the activity expressed in log (1/LC50). QSAR equation without outliers exhibited quality indexes of r² = 0.944; F = 58.71; q² = 0.8442; Spress = 0.0827, indicating high predictability of the model. It was observed influence of lipophilicity on (-)-borneol derivatives larvicidal activity, suggesting that molecules with Log P value around 4.5 have optimized activity. This study may be used as a basis to guide research of new larvicides candidates.
O mosquito Aedes aegypti é o principal transmissor de doenças de origem viral, como a dengue, chikungunya e zika. Na ausência de recursos específicos como a falta de vacinas e medicamentos eficazes, a principal estratégia para o manejo destas infecções se dá pelo controle do vetor. Os principais agentes utilizados no controle químico são os organofosforados e piretroides, contudo o seu uso indiscriminado fez surgir populações do Ae. aegypti resistentes. Uma alternativa viável a estes inseticidas/larvicidas clássicos é a pesquisa de moléculas fitoquímicas, como as da classe dos terpenoides, presentes nos óleos essenciais de diversas espécies vegetais que já possuem atividade larvicida documentada na literatura. Neste contexto, o objetivo deste trabalho foi sintetizar e avaliar a atividade do monoterpeno (-)-borneol e seus doze derivados frente as larvas do Ae. aegypti, utilizando o QSAR como estratégia de descoberta de novos candidatos a agentes larvicidas. Através da reação de esterificação em meio básico, utilizando cloreto de ácido, o (-)-borneol foi modificado a partir de sua hidroxila alcoólica, variando no tamanho e tipo de cadeia lateral. Os compostos sintetizados foram purificados em coluna cromatográfica e caracterizados por RMN ¹³C e ¹H, EM, e IR. A CL50 foi avaliada através do ensaio larvicida, onde a cada teste 20 larvas em terceiro estádio são expostas por 24h a diferentes concentrações (em triplicata) de composto. A partir dos dados de mortalidade das larvas, a CL50 é obtida com IC 95% pela análise Probit. O derivado cloroacetato de bornila exibiu a maior atividade (21 ppm), já o heptanoato de bornila demonstrou-se inativo. As propriedades físico-químicas dos derivados foram obtidas pelo módulo GAMESS® do programa Chem3D Ultra 7.0® a partir da conformação mais estável da molécula. O descritor escolhido para estudo de QSAR foi o Log P, por apresentar a melhor correlação, e principalmente por ser um parâmetro altamente informativo quanto a sua influência na atividade larvicida. A equação foi calculada pelo software MiniTab16™ através da regressão linear entre o Log P dos derivados e a atividade larvicida expressa em Log (1/CL50). O QSAR obtido sem os compostos outliers apresentou índices de qualidade de r² = 0,944; F = 58,71; q²= 0,8442; SPRESS = 0,0827 indicando alta preditibilidade do modelo. Foi observada a influência da lipofilicidade na atividade larvicida dos derivados do (-)-borneol, sugerindo que moléculas com Log P de aproximadamente 4,5 tem sua atividade otimizada. Este trabalho poderá ser utilizado como base para direcionar o planejamento de novos candidatos a agentes larvicidas.
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Geylan, Gökçe. „Training Machine Learning-based QSAR models with Conformal Prediction on Experimental Data from DNA-Encoded Chemical Libraries“. Thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447354.
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DNA-encoded chemical libraries (DEL) allows an exhaustive chemical space sampling with a large-scale data consisting of compounds produced through combinatorial synthesis. This novel technology was utilized in the early drug discovery stages for robust hit identification and lead optimization. In this project, the aim was to build a Machine Learning- based QSAR model with conformal prediction for hit identification on two different target proteins, the DEL was assayed on. An initial investigation was conducted on a pilot project with 1000 compounds and the analyses and the conclusions drawn from this part were later applied to a larger dataset with 1.2 million compounds. With this classification model, the prediction of the compound activity in the DEL as well as in an external dataset was aimed to be analyzed with identification of the top hits to evaluate model’s performance and applicability. Support Vector Machine (SVM) and Random Forest (RF) models were built on both the pilot and the main datasets with different descriptor sets of Signature Fingerprints, RDKIT and CDK. In addition, an Autoencoder was used to supply data-driven descriptors on the pilot data as well. The Libsvm and the Liblinear implementations were explored and compared based on the models’ performances. The comparisons were made by considering the key concepts of conformal prediction such as the trade-off between validity and efficiency, observed fuzziness and the calibration against a range of significance levels. The top hits were determined by two sorting methods, credibility and p-value differences between the binary classes. The assignment of correct single-labels to the true actives over a wide range of significance levels regardless of the similarity of the test compounds to the training set was confirmed for the models. Furthermore, an accumulation of these true actives in the models’ top hit selections was observed according to the latter sorting method and additional investigations on the similarity and the building block enrichments in the top 50 and 100 compounds were conducted. The Tanimoto similarity demonstrated the model’s predictive power in selecting structurally dissimilar compounds while the building block enrichment analysis showed the selectivity of the binding pocket where the target protein B was determined to be more selective. All of these comparison methods enabled an extensive study on the model evaluation and performance. In conclusion, the Liblinear model with the Signature Fingerprints was concluded to give the best model performance for both the pilot and the main datasets with the considerations of the model performances and the computational power requirements. However, an external set prediction was not successful due to the low structural diversity in the DEL which the model was trained on.
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Bloyet, Nicolas. „Caractérisation et plongement de sous-graphes colorés : application à la construction de modèles structures à activité (QSAR)“. Thesis, Lorient, 2019. http://www.theses.fr/2019LORIS546.
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Dans le domaine de la chimie, il est intéressant de pouvoir estimer des propriétés physico- chimiques de molécules, notamment pour des applications industrielles. Celles-ci sont difficiles à estimer par simulations physique, présentant une complexité temporelle prohibitive. L'émergence des données (publiques ou privées) ouvre toutefois de nouvelles perspectives pour le traitement de ces problèmes par des méthodes statistiques et d'apprentissage automatique. La principale difficulté réside dans la caractérisation des molécules : celles-ci s'apparentent davantage à un réseau d'atomes (autrement dit un graphe coloré) qu'à un vecteur. Or, les méthodes de modélisation statistiques traitent usuellement avec des observations encodées comme telles, d'où la nécessité de méthodes spécifiques, nommées relations structures-activité, traitant des observations encodées sous forme de graphes. Le but de cette thèse est de tirer parti des corpus publics pour apprendre les meilleures représentations possibles de ces structures, et de transférer cette connaissance globale vers des jeux de données plus restreints. Nous nous inspirons pour ce faire de méthodes utilisées en traitement automatique des langages naturels. Pour les mettre en œuvre, des travaux d'ordre plus théorique ont été nécessaires, notamment sur le problème d'isomorphisme de graphes. Les résultats obtenus sur des tâches de classification/régression sont au moins compétitifs avec l'état de l'art, voire meilleurs, en particulier sur des jeux de données restreints, attestant des possibilités d'apprentissage par transfert sur ce domaineIn the field of chemistry, it is interesting to be able to estimate the physicochemical properties of molecules, especially for industrial applications. These are difficult to estimate by physical simulations, as their implementation often present prohibitive time complexity. However, the emergence of data (public or private) opens new perspectives for the treatment of these problems by statistical methods and machine learning. The main difficulty lies in the characterization of molecules: these are more like a network of atoms (in other words a colored graph) than a vector. Unfortunately, statistical modeling methods usually deal with observations encoded as such, hence the need for specific methods able to deal with graphs- encoded observations, called structure-activity relationships. The aim of this thesis is to take advantage of public corpora to learn the best possible representations of these structures, and to transfer this global knowledge to smaller datasets. We adapted methods used in automatic processing of natural languages to achieve this goal. To implement them, more theoretical work was needed, especially on the graph isomorphism problem. The results obtained on classification / regression tasks are at least competitive with the state of the art, and even sometimes better, in particular on restricted data sets, attesting some opportunities for transfer learning in this field
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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 orbital (ELUMO) via QSAR modelling and analysis; 2) to validate the models by using internal and external cross-validation techniques; 3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: 1) Linear or Multi-linear Regression (MLR); 2) Partial Least Squares (PLS); and 3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: 1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; 2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; 3) ELUMO are shown to correlate highly with the NCl for several classes of DBPs; and 4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.
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Pedreros, Riquelme América Beatriz. „Diseño racional de antibacterianos de núcleo 8-aril- mercapto-pirimidoisoquinolinquinonas basado en las herramientas de química medicinal de gráfica de craig y modelo 3D-QSAR/COMFA“. Tesis, Universidad de Chile, 2017. http://repositorio.uchile.cl/handle/2250/144766.
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Memoria para optar al título de Químico FarmacéuticoA lo largo del tiempo, los antibacterianos han sido ampliamente utilizados en el tratamiento y control de enfermedades infecciosas. Actualmente la lucha contra varias enfermedades de origen bacteriano se ha vuelto un problema de salud a nivel mundial, debido a la resistencia bacteriana. Sumado a lo anterior, la tendiente disminución en investigación y desarrollo de nuevos antibacterianos nos insta a destinar los esfuerzos en investigar nuevos compuestos activos, idealmente, frente a bacterias multirresistentes, con una estructura química y mecanismo de acción novedoso. En este sentido la cadena transportadora de electrones, y en particular la ubiquinona aparece como un blanco interesante para el desarrollo de nuevos antibacterianos. Por este motivo nuestro grupo de investigación ha llevado a cabo el desarrollo de derivados isoquinolinquinona activos en bacterias Gram positivo. Sin embargo, los factores que modulan la actividad de estos compuestos no han sido estudiados. De esta forma, el objetivo de este estudio es desarrollar un modelo 3D- QSAR/CoMFA y compararlo con la gráfica de Craig para establecer si estos modelos son útiles para el desarrollo de nuevos compuestos antibacterianos. La metodología CoMFA, la cual mide parámetros estéricos y electrostáticos de cada molécula, indica que el primer parámetro es el principal contribuyente de la actividad de esta familia de compuestos. En contraste, la gráfica de Craig, la cual relaciona la constante de hidrofobicidad y el carácter electrónico de los sustituyentes, entrega como resultado que la actividad de esta serie de compuestos depende principalmente del carácter hidrofóbico del sustituyente. Para validar estos resultados se realizó la síntesis de 8 derivados 8-aril-mercapto-pirimidoisoquinolinquinona, obteniéndose en dos pasos, con rendimientos para la última etapa entre 43% y 89%. Los compuestos obtenidos fueron evaluados para determinar su concentración inhibitoria mínima, en 6 bacterias prototipo. Los resultados muestran que 5 compuestos presentaron actividad en las bacterias Gram positivo ensayadas. Ninguno de los compuestos presento actividad en bacterias Gram negativa. En conclusión, se establece que el empleo de la gráfica de Craig y modelo CoMFA resultaron ser herramientas útiles para el desarrollo de nuevos compuestos antibacterianos de estructura 8-(4’-aril-mercapto)-pirimidoisoquinolinquinonas
Antibacterials have been used in treatment and control of infectious diseases in modern times. Today, the struggle against several diseases with bacterial origins has become a global health problem, due to bacterial resistance. In addition, diminishing research and development trends in antibacterial drugs urges us to focus resources on new active compounds. These should target multi-resistant bacteria with novel chemical structures and action mechanisms. In this sense, the electron transport chain and in particular the ubiquinone seems like an interesting target for new antibacterial developments. For this reason, our research group has conducted the development of active isoquinolinquinone derivatives on gram positive bacteria. However, the research of the factors that modulate the activity of these compounds has not been previously performed. The objective of this research is to develop a CoMFA model and compare it with Craig´s graph to establish the usefulness of these models for the development of new antibacterial compounds. The CoMFA methodology measures steric and electrostatic parameters of each molecule and indicates that the first parameters are the main contributors to the activity of this compound family. In contrast, Craig plot relates the hydrophobic and electronic constants of the substituents. From this plot, as a result of the activity of this compound series, it is derived that the hydrophobic properties of the substituents are the main contributor. To validate these results, eight derivatives of 8-aril-mercapto-pirimidoisoquinolinquinone were synthesized, in two steps, with efficiency between 42.7% and 88.5%. These compounds were evaluated to determine the minimum inhibitory concentration, using six prototype bacteria. The results show that five compounds achieved activity on the tested Gram-positive bacteria. None of these compounds revealed activity on Gram-negative bacteria. In conclusion, it has been stablished that both tools, Craig plot and CoMFA, are useful methods in the development of new antibacterial compounds of 8-(4’-aryl-mercapto)-pyrimidoisoquinolinquinone structure
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Olguín, Carlos José Maria. „Modelagem do coeficiente de sorção do solo de poluentes orgânicos persistentes no meio ambiente“. Universidade Estadual do Oeste do Paraná, 2017. http://tede.unioeste.br/handle/tede/3006.
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The soil sorption coefficient normalized for organic carbon content (Koc) is a physicochemical parameter used in environmental risk assessments to determine the final destination of chemicals released in the environment. So, in oreder to predict this parameter, several models were proposed based on the relationship between LogKoc and LogP. The difficulty and cost to obtain experimental values of LogP have drawn to the algorithms development to calculate those values. Thus, in the first paper of this thesis, several free algorithms were considered to calculate LogP, and it was concluded that the best QSPR models to predict soil sorption coefficient of organic nonionic compounds were obtained using ALOGPs, KOWWIN and XLOGP3 algorithms. This study demonstrated the importance and usefulness of the statistical equivalence test used, since it allowed us to state that the models obtained from the considered algorithms are statistically equivalent. In this study, the both importance and usefulness of the statistical equivalence test were proved. These data allowed us to state that the models that have been obtained from the algorithms are statistically equivalent. Thus, in the impossibility of obtaining LogP values based on one of the algorithms, values obtained by another one of them can be used. It was also observed that the models presented in this study presented statistical quality and predictive capacity compatible with more complex models recently published in the area. In addition, it is a well accepted practice in the area the requirement to validate the prediction of a QSPR model from a data set that was not used in the model generation. In this context, some studies have explored the impact that several sizes of training sets would have on the predictive capacity of the generated QSPR models, consequently not reaching conclusive results. Thus, the second paper has been shown that, from not so large training sets, statistically equivalent QSPR models can be developed and that these models have similar predictive capacity to those ones created from a larger training set. Therefore, models were generated considering LogP values of the total training set, calculated with the ALOGPs algorithm and also with subsets of itself (i.e., halves, quarters and eighths). This study, just like the previous one, has confirmed the importance of using the statistical equivalence test since it was ascertained that, following the adopted procedures, the models obtained with subsets of the training set are statistically equivalent
O coeficiente de sorção do solo normalizado para o conteúdo de carbono orgânico (Koc) é um parâmetro físico-químico utilizado em avaliações de risco ambiental e na determinação do destino final das substâncias químicas lançadas na natureza. Vários modelos para prever este parâmetro foram propostos com base na relação entre LogKoc e LogP. A dificuldade e o custo para a obtenção de valores experimentais de LogP levaram ao desenvolvimento de algoritmos para calculá-los. Assim, no primeiro artigo desta tese foram considerados diversos algoritmos gratuitos para cálculo de LogP, e concluiu-se que os melhores modelos QSPR para predizer o coeficiente de sorção do solo de compostos orgânicos não iónicos foram obtidos usando os algoritmos ALOGPs, KOWWIN e XLOGP3. Neste estudo, foram demonstradas a importância e a utilidade do teste de equivalência estatística utilizado, dados que nos permitiram afirmar que os modelos obtidos dos algoritmos considerados são estatisticamente equivalentes. Assim, na impossibilidade de obterem-se valores de LogP a partir de um dos algoritmos, valores obtidos por outro podem ser usados. Verificou-se ainda que os modelos apresentados neste estudo possuem qualidade estatística e capacidade de predição compatíveis à de modelos mais complexos, publicados recentemente na área. Adicionalmente, a necessidade de se realizar a validação da predição de um modelo QSPR a partir de um conjunto de dados que não foi utilizado na geração do modelo é uma prática bem aceita na área. Nesse contexto, alguns trabalhos exploraram o impacto que diversos tamanhos de conjuntos de treinamento teriam na capacidade de predição dos modelos QSPR gerados, não chegando a resultados conclusivos. Assim, no segundo artigo desta tese, foi mostrado que, a partir de conjuntos de treinamento não tão grandes, modelos QSPR estatisticamente equivalentes podem ser desenvolvidos e que tais modelos têm capacidade de predição similar daqueles criados a partir de um conjunto de treinamento maior. Para isto, modelos foram gerados considerando valores de LogP do conjunto de treinamento total, calculados com o algoritmo ALOGPs e também com subconjuntos do mesmo (i.e., metades, quartos e oitavos). Este estudo, assim como o anterior, confirmou a importância do uso do teste de equivalência estatística utilizado nesta tese já que foi verificado que, seguindo os procedimentos adotados, os modelos obtidos com subconjuntos do conjunto de treinamento são estatisticamente equivalentes.
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AbdulHameed, Mohamed Diwan Mohideen. „COMPUTATIONAL DESIGN OF 3-PHOSPHOINOSITIDE DEPENDENT KINASE-1 INHIBITORS AS POTENTIAL ANTI-CANCER AGENTS“. UKnowledge, 2009. http://uknowledge.uky.edu/gradschool_diss/757.
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Computational drug design methods have great potential in drug discovery particularly in lead identification and lead optimization. 3-Phosphoinositide dependent kinase-1 (PDK1) is a protein kinase and a well validated anti-cancer target. Inhibitors of PDK1 have the potential to be developed as anti-cancer drugs. In this work, we have applied various novel computational drug design strategies to design and identify new PDK1 inhibitors with potential anti-cancer activity. We have pursued novel structure-based drug design strategies and identified a new binding mode for celecoxib and its derivatives binding with PDK1. This new binding mode provides a valuable basis for rational design of potent PDK1 inhibitors. In order to understand the structure-activity relationship of indolinone-based PDK1 inhibitors, we have carried out a combined molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling study. The predictive ability of the developed 3D-QSAR models were validated using an external test set of compounds. An efficient strategy of the hierarchical virtual screening with increasing complexity was pursued to identify new hits against PDK1. Our approach uses a combination of ligand-based and structure-based virtual screening including shape-based filtering, rigid docking, and flexible docking. In addition, a more sophisticated molecular dynamics/molecular mechanics- Poisson-Boltzmann surface area (MD/MM-PBSA) analysis was used as the final filter in the virtual screening. Our screening strategy has led to the identification of a new PDK1 inhibitor. The anticancer activities of this compound have been confirmed by the anticancer activity assays of national cancer institute-developmental therapeutics program (NCI-DTP) using 60 cancer cell lines. The PDK1-inhibitor binding mode determined in this study may be valuable in future de novo drug design. The virtual screening approach tested and used in this study could also be applied to lead identification in other drug discovery efforts.
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Dureckova, Hana. „Robust Machine Learning QSPR Models for Recognizing High Performing MOFs for Pre-Combustion Carbon Capture and Using Molecular Simulation to Study Adsorption of Water and Gases in Novel MOFs“. Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37288.
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Metal organic frameworks (MOFs) are a class of nanoporous materials composed through self-assembly of inorganic and organic structural building units (SBUs). MOFs show great promise for many applications due to their record-breaking internal surface areas and tunable pore chemistry. This thesis work focuses on gas separation applications of MOFs in the context of carbon capture and storage (CCS) technologies. CCS technologies are expected to play a key role in the mitigation of anthropogenic CO2 emissions in the near future. In the first part of the thesis, robust machine learning quantitative structure-property relationship (QSPR) models are developed to predict CO2 working capacity and CO2/H2 selectivity for pre-combustion carbon capture using the most topologically diverse database of hypothetical MOF structures constructed to date (358,400 MOFs, 1166 network topologies). The support vector regression (SVR) models are developed on a training set of 35,840 MOFs (10% of the database) and validated on the remaining 322,560 MOFs. The most accurate models for CO2 working capacities (R2 = 0.944) and CO2/H2 selectivities (R2 = 0.876) are built from a combination of six geometric descriptors and three novel y-range normalized atomic-property-weighted radial distribution function (AP-RDF) descriptors. 309 common MOFs are identified between the grand canonical Monte Carlo (GCMC) calculated and SVR-predicted top-1000 high-performing MOFs ranked according to a normalized adsorbent performance score. This work shows that SVR models can indeed account for the topological diversity exhibited by MOFs.
In the second project of this thesis, computational simulations are performed on a MOF, CALF-20, to examine its chemical and physical properties which are linked to its exceptional water-resisting ability. We predict the atomic positions in the crystal structure of the bulk phase of CALF-20, for which only a powder X-ray diffraction pattern is available, from a single crystal X-ray diffraction pattern of a metastable phase of CALF-20. Using the predicted CALF-20 structure, we simulate adsorption isotherms of CO2 and N2 under dry and humid conditions which are in excellent agreement with experiment. Snapshots of the CALF-20 undergoing water sorption simulations reveal that water molecules in a given pore adsorb and desorb together due to hydrogen bonding. Binding sites and binding energies of CO2 and water in CALF-20 show that the preferential CO2 uptake at low relative humidities is driven by the stronger binding energy of CO2 in the MOF, and the sharp increase in water uptake at higher relative humidities is driven by the strong intermolecular interactions between water.
In the third project of this thesis, we use computational simulations to investigate the effects of residual solvent on Ni-BPM’s CH4 and N2 adsorption properties. Single crystal X-ray diffraction data shows that there are two sets of positions (Set 1 and 2) that can be occupied by the 10 residual DMSO molecules in the Ni-BPM framework. GCMC simulations of CH4 and N2 uptake in Ni-BPM reveal that CH4 uptake is in closest agreement with experiment when the 10 DMSO’s are placed among the two sets of positions in equal ratio (Mixed Set). Severe under-prediction and over-prediction of CH4 uptake are observed when the DMSO’s are placed in Set1 and Set 2 positions, respectively. Through binding site analysis, the CH4 binding sites within the Ni-BPM framework are found to overlap with the Set 1 DMSO positions but not with the Set 2 DMSO positions which explains the deviations in CH4 uptake observed for these cases. Binding energy calculations reveal that CH4 molecules are most stabilized when the DMSO’s are in the Mixed Set of positions.
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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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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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Michielan, Lisa. „Advance Methodologies in Linear and Nonlinear Quantitative Structure-Activity Relationships (QSARs): from Drug Design to In Silico Toxicology Applications“. Doctoral thesis, Università degli studi di Padova, 2010. http://hdl.handle.net/11577/3422242.
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Novel computational strategies are continuously being demanded by the pharmaceutical industry to assist, improve and speed up the drug discovery process. In this scenario chemoinformatics provide reliable mathematical tools to derive quantitative structure-activity relationships (QSARs), able to describe the correlation between molecular descriptors and various experimental profiles of the compounds. In the last years, nonlinear machine learning approaches have demonstrated a noteworthy predictive capability in several QSAR applications, confirming their superiority over the traditional linear methodologies. Particularly the feasibility of the classification approach has been highlighted in solving complex tasks. Moreover, the introduction of the autocorrelation concept in chemistry allows the structural comparison of the molecules
by using a vectorial fixed-length representation to serve as effective molecular descriptor. In the present thesis we have deeply investigated the wide applicability and the potentialities of nonlinear QSAR strategies, especially in combination with autocorrelation molecular electrostatic potential descriptors projected on the molecular surface. Our intent is arranged in six different case studies that focus on crucial problems in pharmacodynamics, pharmacokinetics and toxicity fields.
The first case study considers the estimation of a physicochemical property, the aqueous solvation free energy, that strictly relates to the pharmacokinetic profile and toxicity of chemicals. Our discussion on pharmacodynamics deals with the prediction of potency and selectivity of human adenosine receptor antagonists (hAR). The adenosine receptor family belongs to GPCR (G protein-coupled receptors) family A, including four different subtypes, referred to as A1, A2A, A2B and A3, which are widely distributed in the tissues. They differentiate for both pharmacological profile and effector coupling. Intensive explorative synthesis and pharmacological evaluation are aimed at discovering potent and selective ligands for each adenosine receptor subtype. In the present thesis, we have considered several pyrazolo-triazolo-pyrimidine and xanthine derivatives, studied as promising adenosine receptor antagonists. Then, a second case study focuses on the comparison and the parallel applicability of linear and nonlinear models to predict the binding affinity of human adenosine receptor A2A antagonists and find a consensus in the prediction results. The following studies evaluate the prediction of both selectivity and binding affinity to A2AR and A3R subtypes by combining classification and regression strategies, to finally investigate the full adenosine receptor potency spectrum and human adenosine receptor subtypes selectivity profile by applying a multilabel classification approach. In the field of pharmacokinetics, and more specifically in metabolism prediction, the use of multi- and single-label classification strategies is involved to analyze the isoform specificity of cytochrome P450 substrates. The results lead to the identification of the appropriate methodology to interpret the real metabolism information, characterized by xenobiotics potentially transformed by multiple cytochrome P450 isoforms. As final case study, we present a computational toxicology investigation. The recent regulatory initiatives due to REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) require the ecotoxicological and risk assessment of chemicals for safety. Most of the current evaluation protocols are based on costly animal experiments. So, chemoinformatic tools are heartily recommended to facilitate the toxicity characterization
of chemical substances. We describe a novel integrated strategy to predict the acute aquatic toxicity through the combination of both toxicokinetic and toxicodynamic behaviors of chemicals, by using a machine learning classification method. The goal is to assign chemicals to different levels of acute aquatic toxicity, providing an appropriate answer to the new regulatory requirements. As preliminary validation of our approach, two toxicokinetic and toxicodynamic models have been applied in series to inspect both aquatic toxicity hazard and mode of action of a set of chemical substances with unknown or uncertain toxicodynamic information, assessing the potential ecological risk and the toxic mechanism.Nuove strategie computazionali vengono continuamente richieste dall'industria farmaceutica per assistere, migliorare e velocizzare il processo di scoperta dei farmaci. In questo scenario la chemoinformatica fornisce affidabili strumenti matematici per ottenere relazioni quantitative struttura-attività (QSAR), in grado di descrivere la correlazione tra descrittori molecolari e vari profili sperimentali dei composti. Negli ultimi anni approcci non lineari di machine learning hanno dimostrato una notevole capacità predittiva in diverse applicazioni QSAR, confermando la loro superiorità sulle tradizionali metodologie lineari. E' stata evidenziata particolarmente la praticabilità dell'approccio di classificazione nel risolvere compiti complessi. Inoltre, l'introduzione del concetto di autocorrelazione in chimica permette il confronto strutturale delle molecole attraverso l'uso di una rappresentazione vettoriale di lunghezza fissa che serve da efficace descrittore molecolare. Nella presente tesi abbiamo studiato approfonditamente l'ampia applicabilità e le potenzialità delle strategie QSAR non lineari, soprattutto in combinazione con i descrittori autocorrelati potenziale elettrostatico molecolare proiettato sulla superficie molecolare. Il nostro intento si articola in sei differenti casi studio, che si concentrano su problemi cruciali nei campi della farmacodinamica, farmacocinetica e tossicologia. Il primo caso studio considera la valutazione di una proprietà fisico-chimica, l'energia libera di solvatazione acquosa, che è strettamente connessa con il profilo farmacocinetico e la tossicità dei composti chimici. La nostra discussione in farmacodinamica riguarda la predizione di potenza e selettività di antagonisti del recettore adenosinico umano (hAR). La famiglia del recettore adenosinico appartiene alla famiglia A di GPCR (recettori accoppiati a proteine G), che include quattro diversi sottotipi, cui ci si riferisce come A1, A2A, A2B e A3, ampiamente distribuiti nei tessuti. Si differenziano sia per profilo farmacologico che per effettore cui sono accoppiati. Le intense sintesi esplorativa e valutazione farmacologica hanno lo scopo di scoprire ligandi potenti e selettivi per ogni sottotipo del recettore adenosinico. Nella presente tesi abbiamo considerato diversi derivati pirazolo-triazolo-pirimidinici e xantinici, studiati come promettenti antagonisti del recettore adenosinico. Quindi, un secondo caso studio si focalizza sul confronto e l'applicabilità in parallelo di modelli lineari e non lineari per predire l'affinità di legame di antagonisti del recettore adenosinico A2A umano e trovare un consenso nei risultati di predizione. Gli studi successivi valutano la predizione sia della selettività che dell'affinità di legame ai sottotipi A2AR e A3R combinando strategie di classificazione e regressione, per studiare infine il completo spettro di potenza del recettore adenosinico e il profilo di selettività per i sottotipi hAR mediante l'applicazione di un approccio di classificazione multilabel. Nel campo della farmacocinetica, e più specificamente nella predizione del metabolismo, è coinvolto l'uso di strategie di classificazione multi- e single-label per analizzare la specificità di isoforma di substrati del citocromo P450. I risultati conducono all'identificazione della metodologia appropriata per interpretare la reale informazione sul metabolismo, caratterizzata da xenobiotici potenzialmente trasformati da multiple isoforme del citocromo P450. Come caso studio finale, presentiamo un'indagine in tossicologia computazionale. Le recenti iniziative regolatorie dovute al REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) richiedono l'accertamento ecotossicologico e del rischio dei composti chimici per la sicurezza. La maggiorparte dei correnti protocolli di valutazione è basata su costosi esperimenti animali. Così, gli strumenti chemoinformatici sono caldamente raccomandati per facilitare la caratterizzazione della tossicità di sostanze chimiche. Noi descriviamo una nuova strategia integrata per predire la tossicità acquatica acuta attraverso la combinazione di entrambi i comportamenti tossicocinetico e tossicodinamico dei composti chimici, utilizzando un metodo di classificazione machine learning. L'obbiettivo è assegnare i composti chimici a diversi livelli di tossicità acquatica acuta, fornendo un'appropriata risposta alle nuove esigenze regolatorie. Come validazione preliminare del nostro approccio, due modelli tossicocinetico e tossicodinamico sono stati applicati in serie per esaminare sia il rischio di tossicità acquatica che il modo d'azione di un set di sostanze chimiche con informazione tossicodinamica sconosciuta o incerta, valutandone il potenziale rischio ecologico ed il meccanismo tossico.
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Schaal, Wesley. „Computational Studies of HIV-1 Protease Inhibitors“. Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2002. http://publications.uu.se/theses/91-554-5213-2/.
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Durán, Alcaide Ángel. „Development of high-performance algorithms for a new generation of versatile molecular descriptors. The Pentacle software“. Doctoral thesis, Universitat Pompeu Fabra, 2010. http://hdl.handle.net/10803/7201.
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The work of this thesis was focused on the development of high-performance algorithms for a new generation of molecular descriptors, with many advantages with respect to its predecessors, suitable for diverse applications in the field of drug design, as well as its implementation in commercial grade scientific software (Pentacle). As a first step, we developed a new algorithm (AMANDA) for discretizing molecular interaction fields which allows extracting from them the most interesting regions in an efficient way. This algorithm was incorporated into a new generation of alignmentindependent molecular descriptors, named GRIND-2. The computing speed and efficiency of the new algorithm allow the application of these descriptors in virtual screening. In addition, we developed a new alignment-independent encoding algorithm (CLACC) producing quantitative structure-activity relationship models which have better predictive ability and are easier to interpret than those obtained with other methods.El trabajo que se presenta en esta tesis se ha centrado en el desarrollo de algoritmos de altas prestaciones para la obtención de una nueva generación de descriptores moleculares, con numerosas ventajas con respecto a sus predecesores, adecuados para diversas aplicaciones en el área del diseño de fármacos, y en su implementación en un programa científico de calidad comercial (Pentacle). Inicialmente se desarrolló un nuevo algoritmo de discretización de campos de interacción molecular (AMANDA) que permite extraer eficientemente las regiones de máximo interés. Este algoritmo fue incorporado en una nueva generación de descriptores moleculares independientes del alineamiento, denominados GRIND-2. La rapidez y eficiencia del nuevo algoritmo permitieron aplicar estos descriptores en cribados virtuales. Por último, se puso a punto un nuevo algoritmo de codificación independiente de alineamiento (CLACC) que permite obtener modelos cuantitativos de relación estructura-actividad con mejor capacidad predictiva y mucho más fáciles de interpretar que los obtenidos con otros métodos.
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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 recommended QSARs; and, b) Support Vector Regressions (SVRs), acting as Quantitative Structure-Fate Relationships (QSFRs), linking mass ratios to molecular weight and constituent counts (atoms, bonds, functional groups and rings) for training chemicals. Best predictions were obtained for test and validation chemicals optimally found to be within the domain of applicability of the QSFRs, evidenced by low MAE and high q2 values (in air, MAE≤0.54 and q2≥0.92; in water, MAE≤0.27 and q2≥0.92).Las propiedades fisicoquímicas de un gran espectro de contaminantes químicos son desconocidas. Esta tesis analiza la posibilidad de evaluar la distribución ambiental de compuestos utilizando algoritmos de aprendizaje supervisados alimentados con descriptores moleculares, en vez de modelos ambientales multimedia alimentados con propiedades estimadas por QSARs. Se han comparado fracciones másicas adimensionales, en unidades logarítmicas, de 468 compuestos entre: a) SimpleBox 3, un modelo de nivel III, propagando valores aleatorios de propiedades dentro de distribuciones estadísticas de QSARs recomendados; y, b) regresiones de vectores soporte (SVRs) actuando como relaciones cuantitativas de estructura y destino (QSFRs), relacionando fracciones másicas con pesos moleculares y cuentas de constituyentes (átomos, enlaces, grupos funcionales y anillos) para compuestos de entrenamiento. Las mejores predicciones resultaron para compuestos de test y validación correctamente localizados dentro del dominio de aplicabilidad de los QSFRs, evidenciado por valores bajos de MAE y valores altos de q2 (en aire, MAE≤0.54 y q2≥0.92; en agua, MAE≤0.27 y q2≥0.92).
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Chen, Kuan-Ju, und 陳冠如. „Applying 3D-QSAR technique to construct the pharmacophore model of farnesyltransferase inhibitors“. Thesis, 2008. http://ndltd.ncl.edu.tw/handle/52165476443295609708.
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碩士國立清華大學
分子醫學研究所
96
Abstract A set of 68 imidazole and cyanophenyl containing farnesyltransferase (FTase) inhibitors were subjected to three-dimensional quantitative structure-activity relationship (3D-QSAR) studies using the comparative molecular field analysis (CoMFA) , comparative molecular similarity indices analysis (CoMSIA), and a pharmacophore building method, the Catalyst program. The structures of these inhibitors were generated theoretically, and the conformations used in the 3D-QSAR studies were defined by docking them into the known structure of FTase binding pocket through GOLD3.1. The models constructed by CoMFA and CoMSIA were found to be conformed to each other and were both fitted in with the property potential surface of FTase active site. These pharmacophore features were also compared with those obtained by the Catalyst program and superimposed on the receptor site of FTase. All of the pharmacophore features are in well agreement with structural characteristics and in accord with each other. In addition, we provided a consensus between CoMSIA and Catalyst help improved the statistical results. The final 3D-QSAR models and the information of the inhibitor–receptor interaction would be guide the design of new drug leads against FTase activity.
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Chi-Hung, Tsai. „Applying Support Vector Machines to Protein Disulfide Connectivity Prediction and QSAR Model Construction“. 2006. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-1710200607485500.
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Tsai, Chi-Hung, und 蔡其杭. „Applying Support Vector Machines to Protein Disulfide Connectivity Prediction and QSAR Model Construction“. Thesis, 2006. http://ndltd.ncl.edu.tw/handle/97920504275014104395.
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博士國立臺灣大學
資訊工程學研究所
95
Support Vector Machine (SVM) is widely adopted in the field of machine learning and pattern recognition, and recently the application of SVM techniques to bioinformatics is also very promising. In this dissertation, we applied SVM to two important issues in bioinformatics: protein disulfide connectivity prediction and quantitative-structure activity relationship (QSAR) model construction. For disulfide connectivity prediction, we implemented an algorithm which infers pair-wise bonding probability by SVM, and introduced a descriptor which derived from the sequential distance between oxidized cysteines (DOC). From the analysis of prediction, it revealed that the prediction accuracy is improved with the addition of this descriptor DOC. Furthermore, we developed a two-level prediction model to integrate protein local and global information. The experimental results showed that the prediction accuracy is greatly enhanced. These results are compared with those of previous studies, and a prediction web-service is also provided on the internet. For QSAR model construction, we developed an approach to build QSAR models by selecting the hypothetical descriptor pharmacophore (HDP) with generic evolutionary method (GEM) and correlating the descriptors to activities with SVM. Experimental results of 5 public datasets indicated that our approach is comparable to those of previous studies. Additionally, we incorporated k-means and hierarchical clustering methods to cluster compounds into subsets and construct specific QSAR model for each cluster. The experimental results show that compounds with particular structural features are successfully clustered into the same subset, and the prediction accuracy was enhanced using specific models build by these clusters.
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Chang, Li-Jen, und 張立人. „Integrating GEMDOCK with GEMPLS and GEMkNN for QSAR model of huAChE and AGHO“. Thesis, 2005. http://ndltd.ncl.edu.tw/handle/26000984055629466054.
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碩士國立交通大學
生物資訊研究所
93
Molecular docking and quantitative structure activity relationships (QSAR) are the core technologies in computer-aided drug design. These technologies would help to save much time and cost to find out potential leads for the target protein in drug discovery. In this study, we introduced molecular docking tool, GEMDOCK to generate the atom-based protein-ligand interaction profile. We utilized the interaction profile to be descriptor and integrate with GEMPLS and GEMkNN for QSAR model of human acetylcholinesterase (huAChE) and Arthrobacter globiformis histamine oxidase (AGHO). Our method has adopted the atom-based interaction profile of protein-ligand complex to represent the molecular descriptor. The atom-based interaction profile would be used in GEMPLS and GEMkNN to construct the preliminary QSAR models. By collecting the selected feature of preliminary models, we generated the consensus feature set. Finally, the consensus feature set and ligand specific skeleton set were used to generate the final QSAR model and improve the prediction accuracy of model. We have verified our method for QSAR model of human acetylcholinesterase (huAChE). The model shows the leave-one-out cross validation of q2 is 0.818 and the correlation of r2 is 0.781 between the predicted and experimental values. After verifying the utility of our method on huAChE, we applied it to develop a novel QSAR model for Arthrobacter globiformis histamine oxidase (AGHO). This model is the first QSAR model for AGHO, and it shows a correlation of r2 is 0.983 between the predicted values and experimental values. This model has also been employed to a series of substrates and derivatives to probe the relationship between affinities of AGHO and hydrophobicities of ligands (including the length of substitution group and ring size). From QSAR models of AGHO, we discovered a novel substrate, which was called benzylamine and was evaluated by experiments. Experiments show that our QSAR model was capable of predicting with reasonable accuracy even that the activity of novel compounds not included in the original dataset. The successful development of highly predictive QSAR models implies that our method is a robust and useful tool for QSAR models.
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Palczewska, Anna Maria, Daniel Neagu und Mick J. Ridley. „Using Pareto points for model identification in predictive toxicology“. Thesis, 2013. http://hdl.handle.net/10454/9709.
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noPredictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology.
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Suško, Jurij V. [Verfasser]. „Applicability domain of QSAR models / Iurii Sushko“. 2011. http://d-nb.info/1011026651/34.
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Poiares, João Pedro da Silva Gonçalves. „Development of a QSAR models for the prediction of plasma protein binding“. Master's thesis, 2014. http://hdl.handle.net/10437/5858.
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Orientação: Paulo PaixãoOne of the most important factors, affecting the pharmacokinetic profile of a drug is binding to plasma protein. As such, this study aimed at the development of a quantitative structure–activity relationship model, to predict the fraction unbound in plasma (fub) for four species, using artificial neural network ensemble (ANNE). To this end a database of 363 drugs was used, and molecular descriptors were determined. The dataset was divided in two groups, a train and an external validation, to avoid overfitting. The ANNE optimization reduced the descriptors required to determine the fub to 37, and 150 ANN were randomly selected, trained and the optimal configuration was collected. The different ANNE were built by averaging the output values of the selected ANN and the best ANNE was selected. The model created was able to predict, with a small amount of error, the fub values (root mean square error of 0.16798 and 0.193705 for train and test dataset respectively), however, it tends to underestimate this value (mean error of -0.00291 and -0.015780 for train and test dataset respectively). The ANNE interpretation showed that the main characteristics of that affect fub were the molecule charge, size, structure and lipophilic and hydrophilic affinity.
Um dos factores mais influentes na farmacocinética deum fármaco é a ligação às proteínas plasmáticas. Sendo assim, com este estudo pretendeu -se desenvolver um modelo QSAR, para prever facção do fármaco livre no plasma (fub)para quatro espécies, usando um “ensamble ”de redes neuronais (ANNE). Para tal, utilizou –se uma base – de -dados de 363 fármacos, e determinou-se os seus descritores moleculares. Esta base de dados foi dividida em dois grupos, um para treino e outro para validação externa, para evitar “overfitting”. O ANNE foi optimizado, reduzindo o número de descritores para 37, e 150 redes foram aleatoriamente selecionadas, treinadas e a sua configuração optimizada registada. Os diversos ANNE foram obtido através da média aritmética dos valores das redes seleccionadas, e o melhor ANNE foi escolhido. Este modelo foi capaz de prever com um erro reduzido, o valor da fub (erro quadrático médio de 0.16798 e0.193705 para o grupo de treino e teste respectivamente), no entanto tendencialmente subestima o seu valor (erro médio de -0.00291 e -0.015780 para o grupo de treino e teste respectivamente) . A interpretação do modelo permitiu observar que o tamanho da molécula, a sua estrutura, carga, lipofilia e hidrofilia são as características que mais afectam o valor da fub.
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„Modelo QSAR de Compuestos Análogos a la Isoniazida y Estudio de la Enzima KatG“. Tesis, Universidad de las Américas Puebla, 2005. http://catarina.udlap.mx/u_dl_a/tales/documentos/lqu/castellanos_u_a/.
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Pires, Cristiana Lages. „Permeability through Caco-2 cell monolayers as a model for BBB: implementation and preliminary evaluation using model compounds“. Master's thesis, 2018. http://hdl.handle.net/10316/86489.
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Dissertação de Mestrado em Química Medicinal apresentada à Faculdade de Ciências e TecnologiaIn early stages of drug discovery, the capacity of a compound to cross biological membranes is a determining factor in the selection of drug candidates. This is very important in the case of orally administered drugs once they must be absorbed at the gastrointestinal tract in order to reach the target of interest. Compounds targeted to the central nervous system have also to overcome the blood-brain barrier (BBB), which imposes even more constraints on the drug properties.In general, passive permeation is the major route for the intake of drugs across cell membranes, as they are usually not recognized by specific transporters. Additionally, drugs may be substrates of active efflux pumps such as P-glycoprotein, which further limits their effective permeation towards the target tissue/receptor.The use of in vitro models to mimic biological barriers is of extreme importance to evaluate drug‟s bioavailability. The human colon carcinoma cell line Caco-2 is a well characterized in vitro model of the intestinal epithelium for prediction of intestinal drug permeability and oral drug absorption in humans. This model has been thoroughly used by the scientific community and has delivered a large amount of data. Unfortunately, the discrepancy between the permeability coefficients reported by different research groups is very large, often differing by several orders of magnitude. This precludes the use of this information to obtain Quantitative Structure Property Relationships (QSPR) that may be reliably applied to predict drug‟s bioavailability in cells and tissues. Those discrepancies may come from several sources, namely the cell passage number or the experimental conditions used in the permeability assay (particularly the composition of the transport media).In this work we implement in vitro permeability assays using the Caco-2 cell model, for several model drugs. The effect of the presence of serum albumin in the donor/receiver compartment was quantitatively evaluated and rationalized in terms of the free fraction of drug. The apparent permeability coefficient was interpreted taking into account the drug‟s interaction with components of the transport media as well as with the cell membranes, using the kinetic model recently developed in our group.The ultimate goal of this study is the establishment of QSPR, with a view to understand and predict the rate of drug permeation through cell monolayers such as the BBB. With this objective, permeability assays using endothelial cells obtained from Hematopoietic Stem Cells, have been undertaken. Comparison between the results obtained with the two distinct cell models allows a better characterization of the properties of the different biological barriers, and an optimization of the drug‟s properties depending on their target tissue.
Nas fases iniciais do desenvolvimento de fármacos, a capacidade que o composto tem de atravessar as membranas biológicas é um factor determinante na selecção de candidatos a fármaco. Este processo é particularmente relevante no caso de fármacos administrados por via oral, uma vez que estes têm de ser absorvidos ao nível do tracto gastrointestinal para atingirem o alvo de interesse. Compostos direccionados ao sistema nervoso central tem adicionalmente de atravessar a barreira hematoencefálica (BHE), que por sua vez impõe mais restrições às propriedades do fármaco.De uma forma geral, os fármacos permeiam as membranas celulares por processos passivos, uma vez que não são reconhecidos por transportadores específicos. Adicionalmente, os fármacos podem ser substratos de transportadores de efluxo, tais como a glicoproteína-P, o que limita a sua permeação passiva em direcção ao tecido/receptor alvo.O uso de modelos in vitro que mimetizam as membranas biológicas é de extrema importância para avaliar a biodisponibilidade de um fármaco. A linha celular de adenocarcinoma do cólon humano (Caco-2) é um modelo in vitro bem caracterizado do epitélio intestinal, usado para prever a permeabilidade intestinal de fármacos e a sua absorção oral em humanos. Este modelo tem sido extensivamente usado pela comunidade científica dando origem a uma grande quantidade de dados. Infelizmente, a discrepância entre os coeficientes de permeabilidade reportados por diferentes grupos de investigação é enorme, muitas vezes diferindo em várias ordens de grandeza. Isto impede a sua utilização para a obtenção de relações quantitativas estrutura-propriedade (QSRPs) que possam ser aplicadas para prever a biodisponibilidade de fármacos nas células e tecidos. Estas discrepâncias podem surgir de várias fontes, nomeadamente do número de passagem das células ou das condições experimentas usadas no ensaio de permeabilidade (particularmente a composição do meio de transporte).Neste trabalho, implementaram-se ensaios de permeabilidade in vitro usando o modelo Caco-2 para vários compostos. O efeito da presença de albumina nos compartimentos dador/aceitante foi quantitativamente avaliada e racionalizada em termos da fracção de composto livre. Os coeficientes de permeabilidade aparente foram interpretados tendo em conta a interacção do composto com os componentes do meio de transporte bem como com as membranas das células, usando um modelo cinético desenvolvido no grupo.O objectivo último deste trabalho é o estabelecimento de QSRPs com vista a entender e prever a velocidade de permeação através de monocamadas de células, tais como a BHE. Com este objectivo, foram realizados ensaios de permeabilidade em células endoteliais obtidas a partir de células estaminais hematopoéticas. A comparação entre os resultados obtidos nos dois modelos celulares permitirá uma melhor caracterização das propriedades das diferentes membranas biológicas, e uma optimização das propriedades do fármaco tendo em conta o seu tecido alvo.
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Wagner, Steffen [Verfasser]. „Sesquiterpenlactone : neuronale Netze als QSAR-Modell sowie pharmakokinetische Untersuchungen am Beispiel von Arnica montana / vorgelegt von Steffen Wagner“. 2006. http://d-nb.info/980669685/34.
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„Statistical contribution to the virtual multicriteria optimisation of combinatorial molecules libraries and to the validation and application of QSAR models“. Université catholique de Louvain, 2008. http://edoc.bib.ucl.ac.be:81/ETD-db/collection/available/BelnUcetd-01032008-172816/.
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Palczewska, Anna Maria, X. Fu, Paul R. Trundle, Longzhi Yang, Daniel Neagu, Mick J. Ridley und Kim Travis. „Towards model governance in predictive toxicology“. 2013. http://hdl.handle.net/10454/9708.
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noEfficient management of toxicity information as an enterprise asset is increasingly important for the chemical, pharmaceutical, cosmetics and food industries. Many organisations focus on better information organisation and reuse, in an attempt to reduce the costs of testing and manufacturing in the product development phase. Toxicity information is extracted not only from toxicity data but also from predictive models. Accurate and appropriately shared models can bring a number of benefits if we are able to make effective use of existing expertise. Although usage of existing models may provide high-impact insights into the relationships between chemical attributes and specific toxicological effects, they can also be a source of risk for incorrect decisions. Thus, there is a need to provide a framework for efficient model management. To address this gap, this paper introduces a concept of model governance, that is based upon data governance principles. We extend the data governance processes by adding procedures that allow the evaluation of model use and governance for enterprise purposes. The core aspect of model governance is model representation. We propose six rules that form the basis of a model representation schema, called Minimum Information About a QSAR Model Representation (MIAQMR). As a proof-of-concept of our model governance framework we develop a web application called Model and Data Farm (MADFARM), in which models are described by the MIAQMR-ML markup language. (C) 2013 Elsevier Ltd. All rights reserved.
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Santos, Rodrigo Daniel Garrilha. „Construction of machine learning models to predict pharmacology properties of molecules“. Master's thesis, 2019. http://hdl.handle.net/10451/41459.
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Tese de mestrado, Bioinformática e Biologia Computacional, Universidade de Lisboa, Faculdade de Ciências, 2019O processo de desenvolvimento de drogas é altamente condicionado pela qualidade dos modelos com os quais se realiza a seleção dos primeiros compostos. Este trabalho procurou avaliar vários metodologias e descobrir qual a melhor abordagem para a construção de modelos de QSAR (relação quantitativa estrutura-propriedade/atividade) usando um conjunto grande de problemas. Usando um banco de dados de modelação de problemas desenvolvidos no projeto de pesquisa MIMED, 500 conjunto de dados foram extraídos de forma a serem usados para a construção de modelos QSAR. Quarenta metodologias diferentes, resultantes na combinação de quatro algoritmos de machine learning, dois fingerprints e cinco valores de bits, foram usados para fazer os modelos. Com o uso destas metodologias forma criados 18000 modelos, dos quais após análise surgiu a abordagem que melhor generaliza os modelos. Esta é a combinação dos seguintes parâmetros: random forest without maximum depth com Extended-Connectivity Fingerprints de raio 2 usando 2048 bits. Esta abordagem após validação construiu modelos com valores RMSE (Root Mean Square Error) de 0.17 e valores PVE (Proportion of Variance Explained) de 0.63. Por fim, procurou-se otimizar o processo de construção de modelos QSAR com a utilização da técnica de feature selection. Daqui resultou uma redução no conjunto de variáveis utilizadas pelo algoritmo resultando na construção de modelos mais robustos, mantendo o mesmo desempenho, RMSE de 0.17 e PVE de 0.59. Por fim a metodologia escolhida foi comparada com uma abordagem construída usando KNIME de forma a ter a perceção do fitness dos modelos construídos.
The drug development process is highly conditioned by the quality of the mathematical models with which the first compounds are selected. In this work, we tried to evaluate various methods and find out which are the best parameters for building Quantitative Structure-Activity Relationship (QSAR) models using a large set of problems. Using a database of modelling problems developed within the research project MIMED, 500 datasets were extracted to be used for building QSAR models. Forty different methodologies, resulting from the combination of four machine learning algorithms, two fingerprints and five bit values, were used to make the models. Using these methodologies, 18000 models were created, from which after analysis came the approach that best generalizes the models. This is the combination of the following parameters: random forest without maximum depth with Extended-Connectivity Fingerprints of radius 2 using 2048 bits. This approach after validation builds models with Root Mean Square Error (RMSE) values of 0.17 and Proportion of Variance Explained (PVE) values of 0.63. After the choice of the methodology, we tried to optimize the process of building QSAR models using the feature selection technique. This resulted in a reduction in the set of variables used by the algorithm resulting in the construction of more robust models, maintaining the same performance, RMSE of 0.17 and PVE of 0.59. Finally, the chosen methodology was compared with an approach built using KNIME to have the perception of the fitness of the built models.
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Zhang, K., M. H. Abraham und Xiangli Liu. „An Equation for the Prediction of Human Skin Permeability of Neutral Molecules, Ions and Ionic Species“. 2017. http://hdl.handle.net/10454/11486.
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yesExperimental values of permeability coefficients, as log Kp, of chemical compounds across human skin were collected by carefully screening the literature, and adjusted to 37 °C for the effect of temperature. The values of log Kp for partially ionized acids and bases were separated into those for their neutral and ionic species, forming a total data set of 247 compounds and species (including 35 ionic species). The obtained log Kp values have been regressed against Abraham solute descriptors to yield a correlation equation with R2 = 0.866 and SD = 0.432 log units. The equation can provide valid predictions for log Kp of neutral molecules, ions and ionic species, with predictive R2 = 0.858 and predictive SD = 0.445 log units calculated by the leave-one-out statistics. The predicted log Kp values for Na+ and Et4N+ are in good agreement with the observed values. We calculated the values of log Kp of ketoprofen as a function of the pH of the donor solution, and found that log Kp markedly varies only when ketoprofen is largely ionized. This explains why models that neglect ionization of permeants still yield reasonable statistical results. The effect of skin thickness on log Kp was investigated by inclusion of two indicator variables, one for intermediate thickness skin and one for full thickness skin, into the above equation. The newly obtained equations were found to be statistically very close to the above equation. Therefore, the thickness of human skin used makes little difference to the experimental values of log Kp.