Relevant bibliographies by topics / QSPR

Academic literature on the topic 'QSPR'

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Published: 4 June 2021
Last updated: 22 February 2023

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Journal articles on the topic "QSPR"

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Toropov, Andrey A., and Alla P. Toropova. "QSPR/QSAR: State-of-Art, Weirdness, the Future." Molecules 25, no. 6 (March 12, 2020): 1292. http://dx.doi.org/10.3390/molecules25061292.

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Ability of quantitative structure–property/activity relationships (QSPRs/QSARs) to serve for epistemological processes in natural sciences is discussed. Some weirdness of QSPR/QSAR state-of-art is listed. There are some contradictions in the research results in this area. Sometimes, these should be classified as paradoxes or weirdness. These points are often ignored. Here, these are listed and briefly commented. In addition, hypotheses on the future evolution of the QSPR/QSAR theory and practice are suggested. In particular, the possibility of extending of the QSPR/QSAR problematic by searching for the “statistical similarity” of different endpoints is suggested and illustrated by an example for relatively “distanced each from other” endpoints, namely (i) mutagenicity, (ii) anticancer activity, and (iii) blood–brain barrier.
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Li, Yan Kun, and Xiao Ying Ma. "QSAR/QSPR Model Research of Complicated Samples." Advanced Materials Research 740 (August 2013): 306–9. http://dx.doi.org/10.4028/www.scientific.net/amr.740.306.

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QSAR/QSPR study is a hot issue in present chemical informatics research, and is the very active research domain. In present, a large number of QSAR/QSPR (quantitative structure-activity/property relationships) models have been widely studied and applied in a lot of different areas. This paper overviews the developments, research methods and applications of QSAR/QSPR model.
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Costa, Paulo C. S., Joel S. Evangelista, Igor Leal, and Paulo C. M. L. Miranda. "Chemical Graph Theory for Property Modeling in QSAR and QSPR—Charming QSAR & QSPR." Mathematics 9, no. 1 (December 29, 2020): 60. http://dx.doi.org/10.3390/math9010060.

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Quantitative structure-activity relationship (QSAR) and Quantitative structure-property relationship (QSPR) are mathematical models for the prediction of the chemical, physical or biological properties of chemical compounds. Usually, they are based on structural (grounded on fragment contribution) or calculated (centered on QSAR three-dimensional (QSAR-3D) or chemical descriptors) parameters. Hereby, we describe a Graph Theory approach for generating and mining molecular fragments to be used in QSAR or QSPR modeling based exclusively on fragment contributions. Merging of Molecular Graph Theory, Simplified Molecular Input Line Entry Specification (SMILES) notation, and the connection table data allows a precise way to differentiate and count the molecular fragments. Machine learning strategies generated models with outstanding root mean square error (RMSE) and R2 values. We also present the software Charming QSAR & QSPR, written in Python, for the property prediction of chemical compounds while using this approach.
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Zhang, Xiujun, H. G. Govardhana Reddy, Arcot Usha, M. C. Shanmukha, Mohammad Reza Farahani, and Mehdi Alaeiyan. "A study on anti-malaria drugs using degree-based topological indices through QSPR analysis." Mathematical Biosciences and Engineering 20, no. 2 (2022): 3594–609. http://dx.doi.org/10.3934/mbe.2023167.

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<abstract> <p>The use of topological descriptors is the key method, regardless of great advances taking place in the field of drug design. Descriptors portray the chemical characteristic of a molecule in numerical form, that is used for QSAR/QSPR models. The numerical values related with chemical constitutions that correlates the chemical structure with the physical properties referto topological indices. The study of chemical structure with chemical reactivity or biological activity is termed as quantitative structure activity relationship, in which topological index play a significant role. Chemical graph theory is one such significant branches of science which play a key role in QSAR/QSPR/QSTR studies. This work is focused on computing various degree-based topological indices and regression model of nine anti-malaria drugs. Regression models are fitted for computed indices values with 6 physicochemical properties of the anti-malaria drugs are studied. Based on the results obtained, an analysis is carried out for various statistical parameters for which conclusions are drawn.</p> </abstract>
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Rasulev, Bakhtiyor, and Gerardo Casanola-Martin. "QSAR/QSPR in Polymers." International Journal of Quantitative Structure-Property Relationships 5, no. 1 (January 2020): 80–88. http://dx.doi.org/10.4018/ijqspr.2020010105.

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Predictive modeling of the properties of polymers and polymeric materials is getting more attention, while it is still very complicated due to complexity of these materials. In this review, we discuss main applications of quantitative structure-property/activity relationships (QSPR/QSAR) methods for polymers published recently. The most relevant publications are discussed covering this field highlighting the main advantages and drawbacks of the obtained predictive models. Examples dealing with refractive index, glass transition temperatures, intrinsic viscosity, thermal decomposition and flammability properties are shown, together with a fouling-release activity study. Finally, some considerations are discussed in order to give some clues that could lead to the improvement in the efficient computational design and/or optimization of new polymers with enhanced properties/activities.
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Hosamani, Sunilkumar M., Bhagyashri B. Kulkarni, Ratnamma G. Boli, and Vijay M. Gadag. "QSPR Analysis of Certain Graph Theocratical Matrices and Their Corresponding Energy." Applied Mathematics and Nonlinear Sciences 2, no. 1 (April 25, 2017): 131–50. http://dx.doi.org/10.21042/amns.2017.1.00011.

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AbstractIn QSAR/QSPR study, topological indices are utilized to guess the bioactivity of chemical compounds. In this paper, we study the QSPR analysis of certain graph theocratical matrices and their corresponding energy. Our study reveals some important results which helps to characterize the useful topological indices based on their predicting power.
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Shirakol, Shailaja, Manjula Kalyanshetti, and Sunilkumar M. Hosamani. "QSPR Analysis of certain Distance Based Topological Indices." Applied Mathematics and Nonlinear Sciences 4, no. 2 (September 27, 2019): 371–86. http://dx.doi.org/10.2478/amns.2019.2.00032.

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AbstractIn QSAR/QSPR study, topological indices are utilized to guess the bioactivity of chemical compounds. In this paper, we study the QSPR analysis of selected distance and degree-distance based topological indices. Our study reveals some important results which help us to characterize the useful topological indices based on their predicting power.
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Karelson, Mati, Uko Maran, Yilin Wang, and Alan R. Katritzky. "QSPR and QSAR Models Derived Using Large Molecular Descriptor Spaces. A Review of CODESSA Applications." Collection of Czechoslovak Chemical Communications 64, no. 10 (1999): 1551–71. http://dx.doi.org/10.1135/cccc19991551.

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An overview on the development of QSPR/QSAR equations using various descriptor-mining techniques and multilinear regression analysis in the framework of the CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) program is given. The description of the methodologies applied in CODESSA is followed by the presentation of the QSAR and QSPR models derived for eighteen molecular activities and properties. The properties cover single molecular species, interactions between different molecular species, properties of surfactants, complex properties and properties of polymers. A review with 54 references.
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Jorgensen, William L. "QSAR/QSPR and Proprietary Data." Journal of Chemical Information and Modeling 46, no. 3 (May 2006): 937. http://dx.doi.org/10.1021/ci0680079.

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Li, Yi-Xia, Abdul Rauf, Muhammad Naeem, Muhammad Ahsan Binyamin, and Adnan Aslam. "Valency-Based Topological Properties of Linear Hexagonal Chain and Hammer-Like Benzenoid." Complexity 2021 (April 22, 2021): 1–16. http://dx.doi.org/10.1155/2021/9939469.

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Topological indices are quantitative measurements that describe a molecule’s topology and are quantified from the molecule’s graphical representation. The significance of topological indices is linked to their use in QSPR/QSAR modelling as descriptors. Mathematical associations between a particular molecular or biological activity and one or several biochemical and/or molecular structural features are QSPRs (quantitative structure-property relationships) and QSARs (quantitative structure-activity relationships). In this paper, we give explicit expressions of two recently defined novel ev-degree- and ve-degree-based topological indices of two classes of benzenoid, namely, linear hexagonal chain and hammer-like benzenoid.
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Dissertations / Theses on the topic "QSPR"

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Espinosa, Porragas Gabriela. "Modelos QSPR/QSAR/QSTR basados en sistemas neuronales cognitivos." Doctoral thesis, Universitat Rovira i Virgili, 2002. http://hdl.handle.net/10803/8505.

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Un área sumamente interesante dentro del modelado molecular es el diseño de nuevos compuestos. Con sus propiedades definidas antes de ser sintetizados. Los métodos QSPR/QSAR han demostrado que las relaciones entre la estructura molecular y las propiedades físico químicas o actividades biológicas de los compuestos se pueden cuantificar matemáticamente a partir de parámetros estructurales simples.
Las redes neuronales (ANN) constituyen una alternativa para el desarrollo de algoritmos predictivos aplicados en diversos campos como: análisis masivo de bases de datos, para subsanar los obstáculos derivados de la selección o la multicolinealidad de variables, así como la sensibilidad de los modelos a la presencia de ruido en los datos de entrada al sistema neuronal. En la mayoría de los casos, las redes neuronales han dado mejores resultados que los métodos de regresión multilineal (MLR), el análisis de componentes principales (PCA), o los métodos de mínimos cuadrados parciales (PLS) debido a la no linealidad inherente en los modelos de redes.

En los últimos años el interés por los modelos QSPR/QSAR basados en redes neuronales se ha incrementado. La principal ventaja de los modelos de redes recae en el hecho que un modelo QSAR/QSPR puede desarrollarse sin especificar a priori la forma analítica del modelo. Las redes neuronales son especialmente útiles para establecer las complejas relaciones existentes entre la salida del modelo (propiedades físico químicas o biológicas) y la entrada del modelo (descriptores moleculares). Además, permiten clasificar los compuestos de acuerdo a sus descriptores moleculares y usar esta información para seleccionar el conjunto de índices capaz de caracterizar mejor al conjunto de moléculas. Los modelos QSPR basados en redes usan principalmente algoritmos del tipo backpropagation. Backpropagation es un sistema basado en un aprendizaje por minimización del error. Sin embargo, ya que los compuestos químicos pueden clasificarse en grupos de acuerdo a su similitud molecular, es factible usar un clasificador cognitivo como fuzzy ARTMAP para crear una representación simultánea de la estructura y de la propiedad objetivo. Este tipo de sistema cognitivo usa un aprendizaje competitivo, en el cual hay una activa búsqueda de la categoría o la hipótesis cuyos prototipos provean una mejor representación de los datos de entrada (estructura química).

En el presente trabajo se propone y se estudia una metodología que integra dos sistemas cognitivos SOM y fuzzy ARTMAP para obtener modelos QSAR/QSPR. Los modelos estiman diferentes propiedades como las temperaturas de transición de fase (temperatura de ebullición, temperatura de fusión) y propiedades críticas (temperatura y presión), así como la actividad biológica de compuestos orgánicos diversos (indicadores de toxicidad). Dentro de este contexto, se comparan la selección de variables realizados por métodos tradicionales (PCA, o métodos combinatorios) con la realizada usando mapas auto-organizados (SOM).

El conjunto de descriptores moleculares más factible se obtiene escogiendo un representante de cada categoría de índices, en particular aquel índice con la correlación más alta con respecto a la propiedad objetivo. El proceso continúa añadiendo índices en orden decreciente de correlación. Este proceso concluye cuando una medida de disimilitud entre mapas para los diferentes conjuntos de descriptores alcanza un valor mínimo, lo cual indica que el añadir descriptores adicionales no provee información complementaria a la clasificación de los compuestos estudiados. El conjunto de descriptores seleccionados se usa como vector de entrada a la red fuzzy ARTMAP modificada para poder predecir.

Los modelos propuestos QSPR/QSAR para predecir propiedades tanto físico químicas como actividades biológicas predice mejor que los modelos obtenidos con métodos como backpropagation o métodos de contribución de grupos en los casos en los que se apliquen dichos métodos.
One of the most attractive applications of computer-aided techniques in molecular modeling stands on the possibility of assessing certain molecular properties before the molecule is synthesized. The field of Quantitative Structure Activity/Property Relationships (QSAR/QSPR) has demonstrated that the biological activity and the physical properties of a set of compounds can be mathematically related to some "simple" molecular structure parameters.

Artificial neural network (ANN) approaches provide an alternative to established predictive algorithms for analyzing massive chemical databases, potentially overcoming obstacles arising from variable selection, multicollinearity, specification of important parameters, and sensitivy to erroneous values. In most instances, ANN's have proven to be better than MLR, PCA or PLS because of their ability to handle non-linear associations.

In the last years there has been a growing interest in the application of neural networks to the development of QSAR/QSPR. The mayor advantage of ANN lies in the fact QSAR/QSPR can be developed without having to a priori specify an analytical form for the correlation model. The NN approach is especially suited for mapping complex non-linear relationships that exists between model output (physicochemical or biological properties) and input model (molecular descriptors). The NN approach could also be used to classify chemicals according to their chemical descriptors and used this information to select the most suitable indices capable of characterize the set of molecules. Existing neural networks based QSAR/QSPR for estimating properties of chemicals have relied primarily on backpropagation architecture. Backpropagation are an error based learning system in which adaptive weights are dynamically revised so as to minimize estimation errors of target values. However, since chemical compounds can be classified into various structural categories, it is also feasible to use cognitive classifiers such as fuzzy ARTMAP cognitive system, for unsupervised learning of categories, which represent structure and properties simultaneously. This class of neural networks uses a match-based learning, in that it actively searches for recognition categories or hypotheses whose prototype provides an acceptable match to input data.

The current study have been proposed a new QSAR/QSPR fuzzy ARTMAP neural network based models for predicting diverse physical properties such as phase transition temperatures (boiling and melting points) and critical properties (temperature and pressure) and the biological activities (toxicity indicators) of diverse set of compounds. In addition, traditional pre-screening methods to determine de minimum set of inputs parameters have been compared with novel methodology based in self organized maps algorithms.

The most suitable set of molecular descriptor was obtained by choosing a representative from each cluster, in particular the index that presented the highest correlation with the target variable, and additional indices afterwards in order of decreasing correlation. The selection process ended when a dissimilarity measure between the maps for the different sets of descriptors reached a minimum valued, indicating that the inclusion of more descriptors did not add supplementary information. The optimal subset of descriptors was finally used as input to a fuzzy ARTMAP architecture modified to effect predictive capabilities.

The proposed QSPR/QSAR model predicted physicochemical or biological activities significantly better than backpropagation neural networks or traditional approaches such as group contribution methods when they applied.
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Al-Fahemi, Jabir Hamad. "Momentum-space descriptors for QSPR and QSAR studies." Thesis, University of Liverpool, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.439465.

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Arruda, Anna Celia. "Ampliação e aplicação do método semi-empírico topológico (IET) em modelos QSRR/QSPR/QSAR para compostos alifáticos halogenados e cicloalcanos." Florianópolis, SC, 2008. http://repositorio.ufsc.br/xmlui/handle/123456789/91111.

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Tese (doutorado) - Universidade Federal de Santa Catarina, Centro de Ciências Físicas e Matemáticas. Programa de Pós-Graduação em Química.
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Este estudo foi desenvolvido para avaliar a capacidade de prognóstico do índice semi-empírico topológico (IET) em estimar a retenção cromatográfica (IR) de compostos alifáticos halogenados e cicloalcanos. Também foram desenvolvidos modelos de QSPR/QSAR para prever importantes propriedades físico-químicas, termodinâmicas e atividades biológicas. O modelo de QSRR do IRExpde 141 haloalcanos e o IET mostrou boa qualidade estatística (r2=0,9995; SD=8; r2cv=0,999). A partir do modelo de QSPR obtido entre o ponto de ebulição, Bp(ºC), com o IET (N=86; r2=0,9971; SD=4,2; r2cv=0,997), foram calculados os valores para um grupo externo de 24 compostos (r2=0,9931; SD=7,6). Uma boa correlação entre o ponto de fusão, Mp (°C), e o IET foi obtida (N=43; r2=0,9865; SD=6,1; r2cv=0,985). As correlações obtidas entre os valores calculados e experimentais de log P foram de r2=0,9871 e r2=0,9750, respectivamente para os Métodos Semi-Empírico Topológico e Contribuição dos Fragmentos. Esses resultados mostram a capacidade de prognóstico do IET para propriedades físico-químicas e termodinâmicas. A habilidade de prognóstico do IR pelo IET também foi verificada usando fases estacionárias com diferentes polaridades. Resultados satisfatórios foram encontrados aplicando o IET para estimar o IR de 48 cicloalcanos (r2=0,9905; SD=7; r2cv=0,997) e Bp(°C) (N=33; r2cv=0,988). Esse método permitiu retirar informações sobre as características estruturais, eletrônicas e geométricas das moléculas que estão influenciando no processo de retenção cromatográfico e a distinção entre isômeros cis/trans dos compostos estudados.
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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 Master’s 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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Tämm, Kaido. "QSPR modeling of some properties of organic compounds /." Online version, 2006. http://dspace.utlib.ee/dspace/bitstream/10062/475/5/tammkaido.pdf.

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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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Aguado, Ullate Sonia. "Modeling of homogeneous catalysis: from dft to qspr approaches." Doctoral thesis, Universitat Rovira i Virgili, 2012. http://hdl.handle.net/10803/79119.

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La catálisis es un campo de la ciencia que explora soluciones a los problemas ambientales como la contaminación, la eliminación de los residuos generados en el proceso de síntesis de materiales o la regeneración de los recursos naturales. En la presente Tesis, hemos reportado un estudio de cálculos DFT para la σ activación del enlace NH de amoníaco considerando las especies μ3-alquilidinos de titanio utilizando el complejo modelo [{Ti(η5-C5H5)(μ-O)}3(μ3-CH)]. Posteriormente, con el fin de analizar la hidroformilación asimétrica de estireno catalizada por complejos Rh-Binaphos, se han combinando estudios basados en la aproximación de la determinación del estado de transición y un análisis cualitativo a través de un descriptor molecular recién definido (volumen de distancia ponderada, VW). Usando nuestro conocimiento mecanicista anterior, hemos presentado un estudio QSPR para predecir la actividad y la enantioselectividad de la hidroformilación de estireno catalizada por complejos Rh-difosfinas. También, hemos desarrollado una nueva metodología 3D-QSSR para predecir la enantioselectividad basada en la cuantificación de la representación de diagramas por cuadrantes y aplicándola en el ciclopropanación asimétrica de alquenos catalizadas por complejos de cobre.
Catalysis is a field of science that explores solutions to environmental problems such as pollution, elimination of waste generated in the process of materials synthesis or regeneration of natural resources. In the present Thesis, we have reported a DFT study on the N-H σ-bond activation of ammonia by the µ3-alkylidyne titanium species using the [{Ti(η5-C5H5)(µ-O)}3(µ3-CH)] model complex. Afterwards, we have combined the TS-based approach and qualitative analysis through a newly defined molecular descriptor (distance-weighted volume, VW), in order to analyze the asymmetric hydroformylation of styrene catalyzed by Rh-binaphos complexes. Using our previous mechanistic knowledge, we have presented a QSPR study to predict the activity and the enantioselectivity in the hydroformylation of styrene catalyzed by Rh-diphosphane complexes. We have also developed a new methodology to predict enantioselectivity based on the quantitative quadrant-diagram representation of the catalysts and 3D-QSSR modeling; and we have applied it in the asymmetric cyclopropanation of alkenes catalyzed by copper complexes.
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Fara, Dan Cornel. "QSPR modeling of complexation and distribution of organic compounds /." Online version, 2004. http://dspace.utlib.ee/dspace/bitstream/10062/475/5/tammkaido.pdf.

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Vijay, Vikrant. "Assessment of Cutaneous Permeability of Biocides in Mixtures using QSPR Approach." NCSU, 2009. http://www.lib.ncsu.edu/theses/available/etd-06292009-233331/.

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The purpose of this research work was to assess the dermal permeation of biocides in metalworking fluids (MWFs) to develop predictive QSAR models and to develop an appropriate training set of chemicals to enhance the predictive ability of QSAR models for dermal permeation. Estimation of the amount of chemicals absorbed through skin plays a vital role in dermal risk assessment. Approximately 1.2 million US workers are occupationally exposed to MWFs annually. Different components of MWFs especially biocides, contribute to adverse health effects including irritant and allergic contact dermatitis as well as carcinogenesis. These adverse effects may be positively correlated to their dermal absorption and may cause systemic toxicity if absorbed in significant amount in workers involved in metalworking operations. A lack of scientific data exists regarding the dermal permeation of MWF components, particularly biocides. Therefore, the first two studies were conducted to (1) determine the dermal permeation of biocides and other chemicals (used as training set to develop Linear Solvation Energy Relationship (LSER) models) in commercial and generic MWFs; and (2) develop a LSER model for predicting dermal permeation of other biocides, not used in these studies. Dermal permeation was evaluated in dermatomed porcine skin by utilizing a flow through diffusion cell system. Chemical analysis was performed by employing gas chromatography with a solid phase micro-extraction technique and ultra performance liquid chromatography with a solid phase extraction technique. LSER models, which are a subset of quantitative structure activity relationship models, were constructed by multiple linear regression analysis with permeability coefficient as the response variable and solvatochromic descriptors as the predictor variables. The LSER model is useful to quantitatively measure the difference in interaction between the two phases (skin and vehicle) as well as a predictive tool. Since the training set used to develop a LSER model was not optimally diverse in terms of structure and chemical space, the third study focused on developing a training set of chemicals representing a wider chemical space (in terms of descriptor values) using a best possible chemical selection method. The results from the first two studies demonstrated that (1) the dermal permeation of biocides as well as other chemicals was highest in aqueous solution followed by synthetic, semi-synthetic and soluble oil type of MWFs; (2) addition of water to MWFs for dilution increased dermal permeation; (3) the LSER model adequately predicted the dermal permeability of biocides in MWFs and also shed light on the chemical interactions resulting in reduced permeability. An optimal and less subjective method (uniform coverage design) to select chemicals representing a wider chemical space was identified in the third study. The LSER model based on the new selected training set of chemicals performed statistically better over the LSER model based on the training set used in the previous study. In summary, the aforementioned results demonstrated that there is a difference in the absorption profile of chemicals among the type of MWFs and dilution of MWFs with water increases the dermal permeation of chemicals; the LSER model can be useful to explain the change in vehicle solvatochromic properties upon addition of water as well as can be an effective prediction model for dermal permeation of chemicals in mixtures; finally, a structurally diverse training set of chemicals representing a wider chemical space is required to improve the predictive capability of a model. All of these results will augment the dermal risk assessment of the chemicals in mixtures and contribute to the improvement of computational predictive models.
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Oprisiu, Ioana. "Modélisation QSPR de mélanges binaires non-additifs : application au comportement azéotropique." Phd thesis, Université de Strasbourg, 2012. http://tel.archives-ouvertes.fr/tel-00862598.

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Généralement les modèles QSPR ne sont utilisés que pour prédire des propriétés des corps purs. Dans cette thèse nous avons développé une approche QSPR permettant de prédire des propriétés non additives de mélanges binaires, plus précisément leur caractère azéotropique/zéotropique. Pour parvenir à ce résultat, plusieurs types de modèles quantitatifs et qualitatifs ont été développés. L'approche est originale pour deux raisons. Premièrement, peu de travaux de recherche ont été publiés sur des mélanges dont les propriétés sont non-additives. Deuxièmement, plusieurs nouveaux aspects méthodologiques ont été introduits dans ce travail. Tout d'abord des descripteurs "spéciaux", capables de décrire des mélanges ont été proposés. De plus, un protocole robuste d'obtention et de validation des modèles a été utilisé, et un domaine d'applicabilité des modèles fiable a été proposé. La méthodologie développée pendant cette thèse démontre la fiabilité d'un nouveau concept - les modèles QSPR pour les mélanges. Elle est comparable à d'autres méthodes classiques, quoique n'utilisant qu'un faible nombre de données en comparaison.
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Books on the topic "QSPR"

1

Roy, Kunal, Supratik Kar, and Rudra Narayan Das. A Primer on QSAR/QSPR Modeling. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17281-1.

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Dehmer, Matthias, Kurt Varmuza, and Danail Bonchev, eds. Statistical Modelling of Molecular Descriptors in QSAR/QSPR. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2012. http://dx.doi.org/10.1002/9783527645121.

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1956-, Devillers J., ed. Comparative QSAR. Washington, DC: Taylor & Francis, 1998.

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Corwin, Hansch, Leo Albert, and Hoekman D. H, eds. Exploring QSAR. Washington, DC: American Chemical Society, 1995.

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Leza, German París. Els camins d'Al-qsar. Lleida [Spain]: Universitat de Lleida, 2002.

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Roy, Kunal, ed. Advances in QSAR Modeling. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56850-8.

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Kubinyi, Hugo, Gerd Folkers, and Yvonne C. Martin, eds. 3D QSAR in Drug Design. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/0-306-46857-3.

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Puzyn, Tomasz, Jerzy Leszczynski, and Mark T. Cronin, eds. Recent Advances in QSAR Studies. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-1-4020-9783-6.

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Kubinyi, Hugo, Gerd Folkers, and Yvonne C. Martin, eds. 3D QSAR in Drug Design. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/0-306-46858-1.

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Kaiser, Klaus L. E., ed. QSAR in Environmental Toxicology - II. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3937-0.

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Book chapters on the topic "QSPR"

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Sippl, Wolfgang, and Dina Robaa. "QSAR/QSPR." In Applied Chemoinformatics, 9–52. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2018. http://dx.doi.org/10.1002/9783527806539.ch2.

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Roy, Kunal, Supratik Kar, and Rudra Narayan Das. "QSAR/QSPR Methods." In SpringerBriefs in Molecular Science, 61–103. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17281-1_3.

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Golbraikh, Alexander, and Alexander Tropsha. "QSAR/QSPR Revisited." In Chemoinformatics, 465–95. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2018. http://dx.doi.org/10.1002/9783527816880.ch12.

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Roy, Kunal, Supratik Kar, and Rudra Narayan Das. "QSAR/QSPR Modeling: Introduction." In SpringerBriefs in Molecular Science, 1–36. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17281-1_1.

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Roy, Kunal, Supratik Kar, and Rudra Narayan Das. "Statistical Methods in QSAR/QSPR." In SpringerBriefs in Molecular Science, 37–59. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17281-1_2.

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Roy, Kunal, Supratik Kar, and Rudra Narayan Das. "Newer Directions in QSAR/QSPR." In SpringerBriefs in Molecular Science, 105–21. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17281-1_4.

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Talevi, Alan. "In Silico ADME: QSPR/QSAR." In The ADME Encyclopedia, 525–31. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-84860-6_149.

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Talevi, Alan. "In Silico ADME: QSPR/QSAR." In The ADME Encyclopedia, 1–7. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-51519-5_149-1.

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Yee, Liew Chin, and Yap Chun Wei. "Current Modeling Methods Used in QSAR/QSPR." In Statistical Modelling of Molecular Descriptors in QSAR/QSPR, 1–31. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2012. http://dx.doi.org/10.1002/9783527645121.ch1.

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Gasteiger, Johann. "Modeling and Prediction of Properties (QSPR/QSAR)." In Chemoinformatics, 345–47. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2018. http://dx.doi.org/10.1002/9783527816880.ch9.

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Conference papers on the topic "QSPR"

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Duprat, A., J. L. Ploix, F. Dioury, and G. Dreyfus. "Toward big data in QSAR/QSPR." In 2014 IEEE 24th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2014. http://dx.doi.org/10.1109/mlsp.2014.6958884.

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Skvortsova, M. I., I. I. Baskin, V. A. Palyulin, O. L. Slovokhotova, and N. S. Zefirov. "Structural design inverse problems for topological indices in QSAR/QSPR studies." In The first European conference on computational chemistry (E.C.C.C.1). AIP, 1995. http://dx.doi.org/10.1063/1.47751.

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Laghridat, Charifa, Ilham Mounir, and Mohamed Essalih. "Understanding changes in the structure of complex networks using QSAR/QSPR." In 2022 11th International Symposium on Signal, Image, Video and Communications (ISIVC). IEEE, 2022. http://dx.doi.org/10.1109/isivc54825.2022.9800741.

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Zeryouh, Meryam, Mohamed El Marraki, and Mohamed Essalih. "Some tools of QSAR/QSPR and drug development: Wiener and Terminal Wiener indices." In 2015 International Conference on Cloud Technologies and Applications (CloudTech). IEEE, 2015. http://dx.doi.org/10.1109/cloudtech.2015.7336963.

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Chiewvanichakorn, Rachaya, Chenxi Wang, Zhe Zhang, Aleksandar Shurbevski, Hiroshi Nagamochi, and Tatsuya Akutsu. "A Method for the Inverse QSAR/QSPR Based on Artificial Neural Networks and Mixed Integer Linear Programming." In ICBBB '20: 2020 10th International Conference on Bioscience, Biochemistry and Bioinformatics. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3386052.3386054.

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Mirajkar, K. G., A. V. Deshpande, and H. H. Budihal. "QSPR analysis of KCD coindices for some Chemical compounds." In INTERNATIONAL CONFERENCE ON ADVANCES IN MATERIALS, COMPUTING AND COMMUNICATION TECHNOLOGIES: (ICAMCCT 2021). AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0070749.

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Mirajkar, K. G., A. Morajkar, and H. H. Budihal. "QSPR analysis of some chemical structures using KCD indices." In INTERNATIONAL CONFERENCE ON ADVANCES IN MATERIALS, COMPUTING AND COMMUNICATION TECHNOLOGIES: (ICAMCCT 2021). AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0070746.

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

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Jack, Kevin, Heping Liu, Idriss Blakey, David Hill, Wang Yueh, Heidi Cao, Michael Leeson, Greg Denbeaux, Justin Waterman, and Andrew Whittaker. "The rational design of polymeric EUV resist materials by QSPR modelling." In Advanced Lithography, edited by Qinghuang Lin. SPIE, 2007. http://dx.doi.org/10.1117/12.716213.

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Vladimirova, Nadezhda, Julia Ashina, and Dmitry Kirsanov. "QSPR Modelling of Potentiometric HCO3−/Cl− Selectivity for Polymeric Membrane Sensors." In CSAC2021. Basel Switzerland: MDPI, 2021. http://dx.doi.org/10.3390/csac2021-10621.

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Reports on the topic "QSPR"

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Mills, Jeffrey D. IL QC QSPR - Preliminary Results. Fort Belvoir, VA: Defense Technical Information Center, February 2004. http://dx.doi.org/10.21236/ada422511.

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Katritzky, Alan R. Detoxification of Military Wastes by Nearcritical and Supercritical Water and QSPR Investigations. Fort Belvoir, VA: Defense Technical Information Center, September 1998. http://dx.doi.org/10.21236/ada357837.

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Leszczynski, Jerzy. Development of efficient solar cells using combination of QSPR and DFT approaches. Office of Scientific and Technical Information (OSTI), May 2021. http://dx.doi.org/10.2172/1785077.

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Ward, Keith B. Antiviral Drugs: Molecular Modeling and QSAR. Fort Belvoir, VA: Defense Technical Information Center, December 1990. http://dx.doi.org/10.21236/ada256419.

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Lu, P.-Y., and K. Yuracko. LiverTox: Advanced QSAR and Toxicogeomic Software for Hepatotoxicity Prediction. Office of Scientific and Technical Information (OSTI), February 2011. http://dx.doi.org/10.2172/1006280.

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Brashear, W. T., and P. P. Lu. Evaluation of QSAR for Use in Predictive Toxicology Modeling. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada274144.

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Hausel, J. M. Questionnaire for sensitive positions (QSP) version 4.0 -- Users guide document. Office of Scientific and Technical Information (OSTI), May 1996. http://dx.doi.org/10.2172/251338.

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Sherlock, R. L., and D. W. Lindsay. Volcanic stratigraphy of the QSP area, Hope Bay volcanic belt, Nunavut. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/213182.

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Hartman, Andrea, William Long, and Lisa Veitch. Quiet Supersonic Platform (QSP) Materials and Structures Focus Group Meeting, 26 June 2001. Fort Belvoir, VA: Defense Technical Information Center, July 2001. http://dx.doi.org/10.21236/ada408538.

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