Dissertations / Theses: 'QSPkR'

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Gottipati, Gopichand. "PREDICTION OF HUMAN SYSTEMIC, BIOLOGICALLY RELEVANT PHARMACOKINETIC (PK) PROPERTIES USING QUANTITATIVE STRUCTURE PHARMACOKINETIC RELATIONSHIPS (QSPKR) AND INTERSPECIES PHARMACOKINETIC ALLOMETRIC SCALING (PK-AS) APPROACHES FOR FOUR DIFFERENT PHARMACOLOGICAL CLASSES OF COMPOUNDS." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3525.

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This research developed and validated QSPKR models for predicting in-vivo human, systemic biologically relevant PK properties (i.e., reflecting the disposition of the unbound drug) of four, preselected, pharmacological classes of drugs, namely, benzodiazepines (BZD), neuromuscular blocking agents (NMB), triptans (TRP) and class III antiarrhythmic agents (AAR), as well as PK allometric scaling (PK-AS) models for BZD and NMB, using pertinent human and animal systemic PK information (fu, CLtot, Vdss and fe) from published literature. Overall, lipophilicity (logD7.4) and molecular weight (MW) were found to be the most important and statistically significant molecular properties, affecting biologically relevant systemic PK properties, and the observed relationships were mechanistically plausible: For relatively small MW and lipophilic molecules, (e.g., BZD), an increase in logD7.4 was associated with a decrease in fu, an increase in Vdssu and CLnonrenu, suggesting the prevalence of nonspecific hydrophobic interactions with biological membranes/plasma proteins as well as hepatic partitioning/DME binding. Similar trends were observed in fu and Vdssu for intermediate to large MW, hydrophilic molecules (e.g., NMB). However, although similar trends were observed in fu and Vdssu for relatively hydrophilic, intermediate MW molecules (e.g., TRP), and a heterogeneous class (e.g., Class III AAR), logD7.4 and MW were found to be highly correlated, i.e., the indepdendent effects of logD7,4 and MW cannot be assessed NMB, TRP and Class III AAR show mechanistically diverse clearance pathways, e.g., hepatobiliary, extrahepatic, enzymatic/chemical degradation and renal excretion; therefore, effects of the logD7.4 and/or MW are note generalizable for any of the clearances across classes. PK-AS analyses showed that Vdssu and Vdss scaled well with body weight across animal species (including humans) for BZD. Overall, within the limitations of the methods (and the sample size), ‘acceptable’ predictions (i.e., within 0.5- to 2.0-fold error range) were obtained for Vdssu and Vdss for BZD (and fu correction resulted in improvement of the prediction); however, none of the CLtot predictions were acceptable, suggesting major, qualitative interspecies differences in drug metabolism, even after correcting for body weight (BW). NMB undergo little extravascular distribution owing to their relatively large MW and charged nature, and, as a result, a high percentage of acceptable predictions was obtained for Vdss (based on BW). Similarly, the prediction of CLren (based on BW and glomerular filtration rate, GFR) was acceptable, suggesting that NMB are cleared by GFR across species, and there are no interspecies differences in their tubular handling. On the other hand, CLtot (and/or CLnonren) could not be acceptably predicted by PK-AS, suggesting major differences in their clearance mechanisms across animal species.

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Turner, Joseph Vernon. "Application of Artificial Neural Networks in Pharmacokinetics." Thesis, The University of Sydney, 2003. http://hdl.handle.net/2123/488.

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Drug development is a long and expensive process. It is often not until potential drug candidates are administered to humans that accurate quantification of their pharmacokinetic characteristics is achieved. The goal of developing quantitative structure-pharmacokinetic relationships (QSPkRs) is to relate the molecular structure of a chemical entity with its pharmacokinetic characteristics. In this thesis artificial neural networks (ANNs) were used to construct in silico predictive QSPkRs for various pharmacokinetic parameters using different drug data sets. Drug pharmacokinetic data for all studies were taken from the literature. Information for model construction was extracted from drug molecular structure. Numerous theoretical descriptors were generated from drug structure ranging from simple constitutional and functional group counts to complex 3D quantum chemical numbers. Subsets of descriptors were selected which best modeled the target pharmacokinetic parameter(s). Using manual selective pruning, QSPkRs for physiological clearances, volumes of distribution, and fraction bound to plasma proteins were developed for a series of beta-adrenoceptor antagonists. All optimum ANN models had training and cross-validation correlations close to unity, while testing was performed with an independent set of compounds. In most cases the ANN models developed performed better than other published ANN models for the same drug data set. The ability of ANNs to develop QSPkRs with multiple target outputs was investigated for a series of cephalosporins. Multilayer perceptron ANN models were constructed for prediction of half life, volume of distribution, clearances (whole body and renal), fraction excreted in the urine, and fraction bound to plasma proteins. The optimum model was well able to differentiate compounds in a qualitative manner while quantitative predictions were mostly in agreement with observed literature values. The ability to make simultaneous predictions of important pharmacokinetic properties of a compound made this a valuable model. A radial-basis function ANN was employed to construct a quantitative structure-bioavailability relationship for a large, structurally diverse series of compounds. The optimum model contained descriptors encoding constitutional through to conformation dependent solubility characteristics. Prediction of bioavailability for the independent testing set were generally close to observed values. Furthermore, the optimum model provided a good qualitative tool for differentiating between drugs with either low or high experimental bioavailability. QSPkR models constructed with ANNs were compared with multilinear regression models. ANN models were shown to be more effective at selecting a suitable subset of descriptors to model a given pharmacokinetic parameter. They also gave more accurate predictions than multilinear regression equations. This thesis presents work which supports the use of ANNs in pharmacokinetic modeling. Successful QSPkRs were constructed using different combinations of theoretically-derived descriptors and model optimisation techniques. The results demonstrate that ANNs provide a valuable modeling tool that may be useful in drug discovery and development.

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Turner, Joseph Vernon. "Application of Artificial Neural Networks in Pharmacokinetics." University of Sydney, 2003. http://hdl.handle.net/2123/488.

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Drug development is a long and expensive process. It is often not until potential drug candidates are administered to humans that accurate quantification of their pharmacokinetic characteristics is achieved. The goal of developing quantitative structure-pharmacokinetic relationships (QSPkRs) is to relate the molecular structure of a chemical entity with its pharmacokinetic characteristics. In this thesis artificial neural networks (ANNs) were used to construct in silico predictive QSPkRs for various pharmacokinetic parameters using different drug data sets. Drug pharmacokinetic data for all studies were taken from the literature. Information for model construction was extracted from drug molecular structure. Numerous theoretical descriptors were generated from drug structure ranging from simple constitutional and functional group counts to complex 3D quantum chemical numbers. Subsets of descriptors were selected which best modeled the target pharmacokinetic parameter(s). Using manual selective pruning, QSPkRs for physiological clearances, volumes of distribution, and fraction bound to plasma proteins were developed for a series of beta-adrenoceptor antagonists. All optimum ANN models had training and cross-validation correlations close to unity, while testing was performed with an independent set of compounds. In most cases the ANN models developed performed better than other published ANN models for the same drug data set. The ability of ANNs to develop QSPkRs with multiple target outputs was investigated for a series of cephalosporins. Multilayer perceptron ANN models were constructed for prediction of half life, volume of distribution, clearances (whole body and renal), fraction excreted in the urine, and fraction bound to plasma proteins. The optimum model was well able to differentiate compounds in a qualitative manner while quantitative predictions were mostly in agreement with observed literature values. The ability to make simultaneous predictions of important pharmacokinetic properties of a compound made this a valuable model. A radial-basis function ANN was employed to construct a quantitative structure-bioavailability relationship for a large, structurally diverse series of compounds. The optimum model contained descriptors encoding constitutional through to conformation dependent solubility characteristics. Prediction of bioavailability for the independent testing set were generally close to observed values. Furthermore, the optimum model provided a good qualitative tool for differentiating between drugs with either low or high experimental bioavailability. QSPkR models constructed with ANNs were compared with multilinear regression models. ANN models were shown to be more effective at selecting a suitable subset of descriptors to model a given pharmacokinetic parameter. They also gave more accurate predictions than multilinear regression equations. This thesis presents work which supports the use of ANNs in pharmacokinetic modeling. Successful QSPkRs were constructed using different combinations of theoretically-derived descriptors and model optimisation techniques. The results demonstrate that ANNs provide a valuable modeling tool that may be useful in drug discovery and development.

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Davor, Lončar. "Definisanje lipofilnosti, farmakokinetičkih parametara i antikancerogenog potencijala novosintetisane serije stiril laktona." Phd thesis, Univerzitet u Novom Sadu, Tehnološki fakultet Novi Sad, 2018. https://www.cris.uns.ac.rs/record.jsf?recordId=107622&source=NDLTD&language=en.

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Reverzno-faznom tečnom hromatografijom pod visokim pritiskom primenom dva sistemarastvarača ispitano je ponašanje i hromatografska lipofilnost prirodnih stiril laktona 7-(+)-goniofufurona, 7-epi-(+)-goniofufurona, krasalaktona B i C i dvadeset njihovihnovosintetizovanih derivata i analoga. U ranijim ispitivanjima pokazalo se da ova jedinjenjaimaju veliki biološki potencijal jer pokazuju zapaženu citotoksičnost prema više humanihtumorskih ćelijskih linija. Hromatografsko ponašanje jedinjenja uglavnom je u skladu sanjihovom strukturom. Ustanovljene su linearne veze između hromatografskih retencionihkonstanti i većine in silico parametara lipofilnosti. Primenom hemometrijske QSRR analizeutvrđeni su veoma dobri multi linearni regresioni prediktivni modeli kvantitativne zavisnostiizmeđu eksperimentalno dobijene hromatografske retencione konstante, koja definišeretenciju jedinjenja u čistoj vodi i in silico molekulskih deskriptora odnosno strukturejedinjenja. Lipofilnost jedinjenja ima najveći uticaj na njihove farmakokinetičke, tj. ADME(apsorpcija, distribucija, metabolizam, eliminacija) osobine. Definisani su i statističkipotvrđeni najbolji multi linearni regresioni modeli zavisnosti farmakokinetičkih parametarastiril laktona i od drugih molekulskih deskriptora. In vitro citotoksična aktivnost jedinjenjaevaluirana je prema četiri nove humane maligne ćelijske linije: kancer prostate (PC3), kancer debelog creva (HT-29), melanom (Hs294T), adenokancer pluća (A549). Najaktivnijenovosintetizovano jedinjenje je triciklični 4-fluorocinamatni analog, koji ispoljavananomolarnu aktivnost (IC₅₀ 2,1 nM) prema ćelijama melanoma i aktivniji je preko 2250 puta od komercijalnog antitumorskog agensa doksorubicina (DOX). SAR analizom utvrđena je zavisnost između strukture i biološke aktivnosti jedinjenja. Molekulskim dokingom ispitana je veza stiril laktona i ciljanog proteina značajnog za kancer prostate. Jedinjenja sa visokom inhibitornom aktivnošću prema ćelijama kancera prostate imaju visok doking skor i mogu graditi koordinativno-kovalentnu vezu sa Fe²+jonom prisutnim u aktivnom centru enzima. 3D-QSAR analizom, koja je izvedena metodama komparativnih polja CoMFA i CoMSIA, formiran je značajan prediktivni model između hemijske strukture i biološke aktivnosti stiril laktona.
The behavior and the chromatographic lipophilicity natural styryl lactone 7-(+)-goniofufurone, 7-epi-(+)-goniofufurone, crassalactones B and C and twenty of their newlysynthesized derivatives and analogs were examined using reverse-phase high performance liquid chromatography in the two solvent systems. In previous studies it has been shown that these compounds have great biological potential toward several human tumor cell lines. Chromatographic behavior of the compounds is generally in accordance with their structure. The relationships between the chromatographic retention constants and the majority of their in silico lipophilicity parameters are linear. The application of chemometric QSRR analysis determined very good multiple linear regression predictive models of quantitative correlation between experimentally obtained chromatographic retention constant, which determines the retention of the compound in pure water and in silico molecular descriptors, i.e. the structure of the compound. The lipophilicity of the compounds has a major influence on their pharmacokinetics, i.e. ADME (absorption, distribution, metabolism, elimination) properties. The best multi-linear regression models depending on the pharmacokinetic parameters of styryl lactone and other molecular descriptors have been defined and statistically validated. In vitro cytotoxic activity of the compounds was evaluated according to four novel human malignant cell lines: prostate cancer (PC3), colon cancer (HT-29), melanoma (Hs294T), lung adenocarcinom (A549). The most active compound was tricyclic 4-fluorocinnamic analog, which exhibits a nanomolar activity (IC50 2,1 nM) toward melanoma cells. This compound is over 2250 times more active than commercial antitumor agent doxorubicin (DOX). SAR analysis has revealed a correlation between the structure and the biological activity of the compounds. Using the molecular docking the relationship of the styryl lactone and the target protein important for prostate cancer was examined. The compounds with high inhibitory activity against prostate cancer cells have a high docking score and are capable to form a coordinative-covalent bond with a Fe2+ ion present in the active centre of the enzyme. 3DQSAR analysis, which was performed by methods of comparative CoMFA and CoMSIA fields, has formed a good predictive model between chemical structure and biological activity of the styryl lactone.

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Al, Tafif Abdullah. "PREDICTION OF HUMAN SYSTEMIC, BIOLOGICALLY RELEVANT PHARMACOKINETIC PROPERTIES BASED ON PHYSICOCHEMICAL PROPERTIES OF CALCIUM CHANNEL BLOCKERS." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2868.

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This research explored quantitative relationships (QSPKR) between different molecular descriptors and pertinent, systemic PK properties for 14 calcium channel blockers (CCB). Physicochemical properties (PC) such as molecular weight (MW), molar volume (MV), calculated logP (clogP), pKa, calculated logD7.4 (clogD), % ionized at pH 6.3 and pH 7.4, hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), and number of rotatable bonds (nRot) were chosen as possible predictor variables for systemic PK properties for CCB, obtained from pertinent literature, assessing the PK of CCB after intravenous administration to healthy humans. All PC properties and molecular descriptors were computed using ACD-solubility/DB 12.01. Total body clearance (CLtot), steady-state volume of distribution (Vdss), total area under the plasma concentration-time profile (AUCoo), terminal half-life (t1/2), and fraction of drug excreted unchanged in urine (fe), if available, were obtained or derived from original references, exclusively from IV studies that administered CCB to healthy human volunteers. Several articles focused on drug interactions with grapefruit juice or the impact of renal/hepatic dysfunction, and in such cases, data from the healthy control group were used. Each study was evaluated for study design, PK sampling schedule, bioanalytical and PK analysis methods before inclusion into the final database. The assumption of linear systemic PK was verified by assessing AUCoo versus (IV) dose. Plasma protein binding information was collected from in-vitro experiments to obtain the fraction unbound in plasma (fu). Unbound volume of distribution at a steady state (Vdssu), unbound total (CLtotu), renal (CLrenu), and non-renal clearance (CLnonrenu) were estimated and compared with the relevant physiological references for Vdssu (plasma volume, blood volume, extracellular and intracellular spaces, total body water and body weight) and for the unbound clearances (liver blood flow, renal plasma flow, and glomerular filtration rate, GFR). Final PK property values were obtained by averaging across available studies. The distribution of both PC and PK properties were evaluated, and correlation matrices amongst PC properties were constructed to assess for collinearity. If two PC descriptors were found to be collinear, i.e. r, ≥ 0.8, only one of them was used in the final univariate analysis. Finally, univariate linear regression of all PK variables versus each molecular descriptor was performed; any relationship with p<0.05 and r2≥0.30 was considered to be statistically significant. The PC properties of the final 14 CCB were reasonably normally distributed with few exceptions. Overall, CCBs are small (MW range of 316-496 Da), basic and lipophilic (logD7.4 range of 1.5-5.1) molecules. On the other hand, for the PK properties, the distributions were found to be skewed with high standard deviations. Thus, all PK variables (except fu) were log-transformed. Although CCB are mostly highly plasma protein bound (fu range of 0.2-20%), they are characterized by extensive extravascular tissue distribution (Vdss range of 0.6-20.4 l/kg) and high, mainly metabolic, clearance (CLtot range of 3.7-131.7 ml/min/kg). Clevidipine is the only CCB undergoing extensive, extra-hepatic ester hydrolysis, responsible for the highest CLtot value. Urinary excretion for CCB is negligible. Amlodipine is a PK outlier due to its high Vdss (20.4 l/kg) and low CLtot (6.9 ml/min/kg, due to low hepatic extraction) with fu of 2%. Therefore, the final QSPKR analysis was performed including, as well as excluding amlodipine. Excluding amlodipine, the relationship between fu and logD7.4 was negative and significant (r2 of 0.4, n=12). The relationships between CLtotu, CLnonrenu and CLrenu and logD7.4 were found to be positive and significant (r2 between 0.6-0.7, n=3-12); none of the other PC variables affected any of the clearance terms. Although the relationship between Vdssu and logD7.4 was not significant (r2 of 0.25, n=12), it showed the expected positive slope. In fact, after removing bepridil (the remaining outlier in Vdssu), the relationship with logD7.4 became statistically significant (r2=0.46, n=11). The QSPKR obtained in this study for CCB, with logD7.4 being the main PC determinant for systemic PK properties, were similar to those previously reported for opioids, β-adrenergic receptor ligands and benzodiazepines. However, slope estimates for the relationships of CLnonrenu and CLtotu as a function of logD7.4 for CCB were higher compared to these previously studied compounds, which showed higher sensitivity, most likely as a result of their higher lipophilicity. Overall, lipophilicity measured as logD7.4 was found to be a statistically significant and plausible PC determinant for the biologically relevant systemic PK properties for CCB and other classes of drugs.

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Badri, Prajakta. "PREDICTION OF HUMAN SYSTEMIC, BIOLOGICALLY RELEVANT PHARMACOKINETIC (PK) PROPERTIES BASED ON QUANTITATIVE STRUCTURE PHARMACOKINETIC RELATIONSHIPS (QSPKR) AND INTERSPECIES PHARMACOKINETIC ALLOMETRIC SCALING (PK-AS)." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/124.

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This research developed validated QSPKR and PK-AS models for predicting human systemic PK properties of three, preselected, pharmacological classes of drugs, namely opioids, β-adrenergic receptor ligands (β-ARL) and β-lactam antibiotics (β-LAs) using pertinent human and animal systemic PK properties (fu,, CLtot, Vdss, fe) and their biologically relevant unbound counterparts from the published literature, followed by an assessment of the effect of different molecular descriptors on these PK properties and on the PK-AS slopes for CLtot and Vdss from two species (rat and dog). Lipophilicity (log (D)7.4) and molecular weight (MW) were found to be the most statistically significant and biologically plausible, molecular properties affecting the biologically relevant, systemic PK properties: For compounds with log (D)7.4 > -2.0 and MW < 350 D (e.g., most opioids and β-ARL), increased log (D)7.4 resulted in decreased fu and increased Vdssu, CLtotu and CLnonrenu, indicating the prevalence of hydrophobic interactions with biological membrane/proteins. As result, the final QSPKR models using log (D)7.4 provided acceptable predictions for fu, Vdssu, CLtotu and CLnonrenu. CLnonrenu and CLtotu. For both the datasets, inclusion of drugs undergoing extrahepatic clearance worsened the QSPKR predictions. For compounds with log (D)7.4 < -2.0 and MW > 350 D (e.g., β-LA), increased MW (leading to more hydrogen bond donors/acceptors) resulted in a decrease in fu, likely indicating hydrogen bonding interactions with plasma proteins. In general, it was more difficult to predict PK parameters for β-LAs, as their Vdssu approached plasma volume and CLrenu and CLnonrenu were low - as a result of their high hydrophilicity and large MW, requiring specific drug transporters for distribution and excretion. The PK-AS analysis showed that animal body size accounted for most of the observed variability (r2> 0.80) in systemic PK variables, with single species methods, particularly those using dog, gave the best predictions. The fu correction of PK variables improved goodness of fit and predictability of human PK. There were no apparent effects of molecular properties on the predictions. CLren, CLrenu, CLnonren, and CLnonrenu were the most difficult variables to predict, possibly due to the associated interspecies differences in the metabolism, renal and hepatobiliary drug transporters.

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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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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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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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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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Kucia, Urszula. "Analizy QSPR wielkich bibliotek związków chemicznych na przykładzie bazy danych Abamachem." Doctoral thesis, Katowice : Uniwersytet Śląski, 2019. http://hdl.handle.net/20.500.12128/12222.

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Structure-activity relationship is an essential concept in chemistry guiding, for example, drug design. However, we need economics to fully understand the fate of drugs on the market. Quantitative structure-economy relationships (QSER) for a large dataset of a commercial building block library of over 2.2 million chemicals have been modeled and analyzed for the first time. Our study shows how data binning could be used as an informative method when analyzing big data in chemistry. The modeled molecular statistics shows that on average what we are paying for is the quantity of matter. The influence of synthetic availability scores is also revealed. Finally, we are buying substances by looking at the molecular graphs or molecular formulas. Thus, those molecules that have a higher number of atoms look more attractive and are, on average, also more expensive.

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Fayet, Guillaume. "Développement de modèles QSPR pour la prédiction des propriétés d'explosibilité des composés nitroaromatiques." Phd thesis, Paris 6, 2010. http://pastel.archives-ouvertes.fr/pastel-00006157.

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L'objectif de ces travaux était de développer et d'évaluer des modèles quantitatifs structure-propriété (QSPR) pour la prédiction des propriétés explosives des composés nitroaromatiques, en vue d'une utilisation dans un cadre règlementaire, en particulier celui du nouveau règlement européen REACH. Différentes approches méthodologiques (régressions multi-linéaires, PCA, PLS, arbres de décision) ont été utilisées pour mettre en place des modèles pour la prédiction de la chaleur de décomposition. Les descripteurs des modèles ont été sélectionnés dans un jeu étendu de plus de 300 descripteurs (constitutionnels, topologiques, géométriques et quantiques). Deux premiers modèles avec des domaines d'applicabilité définis et des pouvoirs prédictifs importants ont été obtenus. Des modèles pour trois autres propriétés explosives (la température de décomposition, les sensibilités à la décharge électrique et à l'impact) ont ensuite été développés, avec des performances similaires voire supérieures aux modèles existants. Enfin, l'analyse des mécanismes réactionnels sous-jacents, menée à l'aide de la DFT, a permis de mettre en évidence la présence de chemins de décomposition spécifiques au sein des composés nitroaromatiques et a ainsi complété l'approche QSPR en termes d'interprétation phénoménologique. Cette étude a donc pris en compte l'intégralité des principes mis en place par l'OCDE pour la validation des modèles QSAR/QSPR dans un usage règlementaire (cible expérimentale, structure du modèle, validation, domaine d'applicabilité et interprétation des mécanismes sous-jacents). Deux modèles prédictifs ont même été développés pour la chaleur de décomposition des composés nitroaromatiques.

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Peng, Xiaoling. "Methods of variable selection and their applications in quantitative structure-property relationship (QSPR)." HKBU Institutional Repository, 2005. http://repository.hkbu.edu.hk/etd_ra/594.

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Jamshidian, Majid. "Inclusion et libération de molécules antioxydantes dans un emballage à base d’Acide Poly Lactique en contact alimentaire." Thesis, Vandoeuvre-les-Nancy, INPL, 2011. http://www.theses.fr/2011INPL109N/document.

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Les emballages actifs permettent d’étendre la durée de conservation des aliments, réduisent l'utilisation d'additifs et de conservateurs dans les préparations alimentaires, préservent mieux les saveurs et la qualité des aliments. La libération contrôlée des antioxydants à partir d'emballages alimentaires étend la stabilité des produits (oxydation des lipides réduite) par enrichissement continu en antioxydants alimentaires en surface de l’aliment. L'objectif général du présent travail était d'étudier l'applicabilité de l'acide poly lactique (PLA, polymère biodégradable fabriqué industriellement) comme l'emballage actif. Pour cela, nous avons choisi plusieurs antioxydants synthétiques ou naturels, comme l'alpha-tocophérol, le palmitate d'ascorbyle, le BHA, le BHT, le gallate de propyle et le TBHQ pour produire les emballages antioxydants. En premier lieu, le mode de d’inclusion de ces antioxydants dans la matrice de PLA et leurs effets sur diverses propriétés structurale, thermique, mécanique et barrière du PLA ont été étudiés. L'étude la libération des antioxydants a été réalisée à partir de films de PLA-antioxydants en contact avec trois simulateurs d'aliments (95%, 50%, et 10% d'éthanol) à deux températures (20°C et 40°C). Les résultats ont montré que le PLA a la capacité de contenir et de libérer des antioxydants dans certains produits alimentaires. Enfin, un modèle mathématique basé sur des relations quantitatives structure/propriété (QSPR) a été développé pour prédire la diffusion antioxydants dans les systèmes aliments simulés /emballage actif
Active packaging generates longer shelf-life foods, lower usage of additives and preservatives in food formulations, higher protection of flavors and higher food qualities. Antioxidant controlled release from packaging provides longer food stability (reduced lipid oxidation) by continuously liberating antioxidants at food surface. The overall objective of the present work was to study the suitability of Poly Lactic Acid (PLA, biodegradable polymer industrially produced) as active packaging. We chose various synthetic or natural antioxidants including alpha-tocopherol, Ascorbyl palmitate, BHA, BHT, Propyl gallate and TBHQ to produce the antioxidant packaging. Firstly, the mode of incorporation of these antioxidants in PLA matrix and also their potential effects on diverse structural, thermal, mechanical and barrier properties of PLA were investigated. The release study of antioxidants was accomplished from PLA-antioxidants films into three food simulants, i.e. 95%, 50%, and 10% ethanol at two temperatures (20°C and 40°C). The results showed that PLA has the capability for being as a suitable carrier for antioxidant-active packaging for some food products. Finally, a mathematical model based on quantitative structure property relationships (QSPR) was developed to predict antioxidant diffusion in food simulant/ active packaging system

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Plass, Monika. "The influence of conformational and associative effects on the QSPR descriptors of oligopeptide derivatives." [S.l. : s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=96170795X.

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Khabzina, Yoldes. "Influence des cations d'échange dans les zéolithes type faujasites sur la sélectivité d'adsorption des isomères du xylène." Thesis, Lyon 1, 2015. http://www.theses.fr/2015LYO10008.

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Depuis plusieurs années, IFPEN développe des adsorbants à base de zéolithe faujasite pour le procédé de séparation des xylènes. Dans ce cadre, cette thèse a permis de rationaliser les origines de la sélectivité des isomères du xylène dans les zéolithes faujasites. Pour ce faire, une nouvelle approche est proposée. L'objectif est d'établir un modèle à la fois explicatif et prédictif qui permet de relier la sélectivité à un certain nombre de paramètres caractéristiques du système, appelés descripteurs. Après la proposition d'un plan d'expériences contenant une soixantaine d'adsorbants, leur préparation et leur test étaient effectués en utilisant des outils adéquats automatisés et parallélisés. L'analyse statistique descriptive faite sur l'ensemble des 43 propriétés d'adsorption évaluées a révélé l'existence de 4 différentes classes d'adsorbants. L'étape de construction du modèle était précédée par l'identification et le calcul des descripteurs. Ceux qui sont retenus caractérisent, essentiellement, l'état de confinement responsable de la sélectivité au sein de la zéolithe. On cite la taille des cations des sites II, l'occupation des sites III ou encore la saturation des sites II. Deux méthodes statistiques étaient utilisées pour construire les relations structures-propriétés. Tout d'abord, la régression linéaire multiple avec comme variables explicatives les 3 descripteurs cités. Le modèle explicatif retenu prédit avec un coefficient de corrélation R² de 0,78. Aussi, l'analyse discriminante était utilisée. Ces mêmes 3 descripteurs ont servi à prédire l'affectation des adsorbants dans les 4 classes identifiées avec un pourcentage de prédiction total de 76%
For several years, IFPEN develops based faujasite adsorbents for the xylene separation process. In this context, this thesis allowed to streamline the selectivity origins of xylene isomers in faujasite zeolites. To do it, a new approach is proposed. The objective is to establish, at the same time, an explanatory and predictive model which allows to relate the selectivity to a number of characteristic parameters of the system, called descriptors. After the proposal of an experimental design containing about sixty adsorbents, their preparation and their test were made by using automated and paralleled adequate tools. A descriptive statistical analysis made on 43 evaluated adsorption properties revealed the existence of 4 various classes of adsorbents. The stage of the model construction was preceded by the identification and the calculation of descriptors. Those who are retained characterize, essentially, the confinement state responsible for the selectivity within the zeolite. We quote the sites II cations size, the sites III occupation or still the sites II saturation. Two statistical methods were used to build the structures-properties relationship. First, a multiple linear regression with, as predictive variables, the 3 quoted descriptors. The retained explanatory model predicts with a correlation coefficient R² = 0,78. So, the discriminant analysis was used. The same 3 descriptors served to predict the affectation of adsorbents in the 4 identified classes with a total prediction percentage of 76 %

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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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Kireeva, Natalia. "Ensemble QSPR modeling of stabilities of metal : ligands complexes and melting points of ionic liquids." Strasbourg, 2009. http://www.theses.fr/2009STRA6008.

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Paula, Fávero Reisdorfer. "QSPR/SAR em derivados 5-nitro-heterocíclicos com atividades antichagásica. Estudo das relações entre o potencial de redução do grupo nitro e propriedades físico-químicas." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/9/9135/tde-29052007-152154/.

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Alguns nitrocompostos apresentam atividade antichagásica resultante do processo de redução do grupo nitro com conseqüente formação de radical nitro ânion e de intermediários tóxicos ao parasita. A variação estrutural dos derivados 5-nitroheterocíclicos pode interferir no processo de redução destes compostos e, também, na atividade biológica. Neste aspecto, o estudo da redução dos nitrocompostos por meio de técnicas voltamétricas, como a voltametria cíclica e a voltametria de onda quadrada é uma forma de avaliar ou até simular o mecanismo de ação destes derivados. Assim, a avaliação de processos eletródicos e a determinação dos potenciais de redução dos nitrocompostos fornecem subsídios para a compreensão do mecanismo da atividade antiparasitária. Neste trabalho, desenvolveram-se procedimentos eletroquímicos utilizados para a determinação do potencial de redução e do potencial de meia onda de vinte e três nitrocompostos (5-nitro-2-tiofilideno 4-R-benzidrazidas e 5-nitro-2-furfurilideno 4-R-benzidrazidas), visando o emprego destes em estudos de QSPR e SAR. As determinações foram realizadas empregando-se Voltametria Cíclica e Voltametria de Onda Quadrada em célula eletroquímica de 10 mL nos meios prótico (solução tampão PIPES com eletrólito de suporte NaNO3 0,1 mol L-1), misto de DMSO/solução tampão PIPES (50% v/v) e em meio aprótico de DMSO (com eletrólito de suporte Bu4NH4BF4). O eletrodos de referência e auxiliar utilizados foram Ag/AgCl saturado e platina. Os eletrodos de trabalho utilizados foram o carbono vítreo (meio aprótico e misto), pasta de carbono (meio misto e prótico) e pasta de carbono modificada com nitrocompostos (meio prótico). Foram realizados estudos teóricos, de modelagem molecular, e experimental, espectrofotometria UV/visível, com o objetivo de determinar a conformação de menor energia mínima global e confirmar a ocorrência de disposição estrutural co-planar. No estudo teórico, utilizou-se as metodologias de otimização de geometria, e análise conformacional dos nitrocompostos calculadas por meio de AM1 e HF3-21G* e HF6- 31G* para obtenção dos confôrmeros de menor energia mínima. Estes confôrmeros foram submetidos a cálculo de carga de ponto único (AM1), onde determinou-se os valores de energia de diversas propriedades de caráter eletrônico (energias de formação em vácuo e meio solvatado, de HOMO, de LUMO, momento de dipolo, dureza, potencial químico, afinidade eletrônica, potencial de ionização, cargas de potencial eletrostático), geométrica (volume molecular) e mapas 3D de densidade de potencial eletrostático, de HOMO e de LUMO. Todos os cálculos foram realizados empregando-se os pacotes computacionais Spartan O2 for Linux, Spartan O4 for Windows e ClogP 4.0 (para determinar os valores de hidrofobicidade). Os valores do potencial de pico catódico (Epc 1) e de meia onda bem como as propriedades físico-químicas, obtidos a partir de cálculos de química quântica, de σp e σR, ClogP e π) foram utilizados em análise de QSPR aplicando-se a Análise de Hansch e regressão multivariada, PLS. Avaliou-se ainda, a atividade antichagásica de dez nitrocompostos (oito derivados tiofilidênicos e dois derivados fufurilidênicos) visando investigar a possibilidade de influência do processo de redução dos nitrocompostos sobre a atividade biológica. Realizaram-se ensaios que permitiram avaliar o efeito antiproliferativo dos nitrocompostos sobre o parasita, em 24 horas, por meio de contagem do número de unidades viáveis em câmara de Neubauer. Os métodos voltamétricos utilizados mostraram-se adequados para a avaliação da redução eletroquímica dos nitrocompostos, e foram apropriados para a obtenção dos potenciais de redução de pico catódico e dos potenciais de meia onda de todos os derivados em meio aprótico e misto. Em meio aquoso, obtiveram-se os valores dos potenciais de redução de pico catódico apenas para os derivados tiofilidênicos. A partir dos resultados obtidos na análise conformacional, observou-se a quebra de efeito co-planar estrutural na região do anel benzênico substituído e porção hidrazídica molecular. Adicionalmente, nos mapas de densidade de potencial eletrostático, registrou-se a ocorrência de efeito mesomérico conjugado reduzido entre as porções moleculares citadas acima. De posse destes dados, em conjunto com as visualizações de sinais característicos para grupos cromóforos distintos em espectros UV/visível, sugere-se a ausência de efeito conjugado molecular e, como conseqüência, de influência dos grupos substituintes em posição para do anel benzênico sobre o grupo nitro ligado ao anel heterocíclico. Nos resultados obtidos a partir das análises de QSPR, observou-se que não ocorre correlação entre as propriedades físico-químicas, determinadas para os nitrocompostos, e os descritores eletroquímicos, obtidos em diferentes meios de reação. Diante deste fato, sugere-se que a quebra do efeito co-planar interfere diretamente na intensidade da influência das propriedades físico-químicas de grupos substituintes sobre o potencial de redução dos nitrocompostos. Observou-se, também, que a maioria dos compostos apresentou atividade antichagásica superior ao fármaco de referência, o benznidazol. Verificou-se que os compostos não substituído e cloro derivado da série dos 5-nitro-2-furfurilidênicos são mais ativos que os correspondentes análogos das 5-nitro-2-tiofilideno benzidrazidas. Em análise preliminar, observou-se que o potencial de redução e o potencial de meia onda não exercem influência sobre a atividade antichagásica dos nitrocompostos.
Some nitrocompounds have show activity against to Trypanosoma cruzi. Those biological activity are resulting from nitro group reduction with subsequent formation of radical nitro anion and other reaction\'s intermediates, which are toxic to the parasite. The structural difference among the 5-nitro-heterocyclic derivatives might exert influence on these compounds reduction and also on the antichagasic activity. In this way, the study of reduction process by voltammetric techniques, such as cyclic voltammetry and square wave voltammetry, is a mode of evaluate the biological action mechanism. Therefore, the evaluation of the electrodic process and the determination of nitrocompounds reduction potential provide information for elucidating the antitrypanosomal activity mechanism. In the present work, the procedures employed to determinate the cathodic reduction and reduction potentials were carried out on twenty three nitrocompounds (5-nitro-2-tiofilidenic benzhidrazides and 5-nitro-2-furfurilidenic benzhidrazides) with the aim using those data in QSPR and SAR analysis. Cyclic voltammetry and square wave voltammetry techniques were used to determined the reduction potential in electrochemical cell of 10 mL in the protic media (PIPES buffer and electrolyte of support NaNO3 0.1 mol L-1), mixed media of DMSO and PIPES buffer 50:50 v/v (electrolyte of support NaNO3 0.1 mol L-1), and aprotic media of DMSO (electrolyte of support Bu4NH4BF4 0.1 mol L-1). The reference and counter electrodes used were Ag/AgCl (with KCl saturated) and platinum, respectively. In these studies, the work electrodes were glassy carbon (aprotic and mixed media), carbon paste (mixed and protic media) and carbon paste modified with nitrocompounds (protic media). Molecular modeling studies and UV/visible spectrophotometry were investigated with the aim for determining the lowest conformational energy and the occurrence of co-planarity molecular structure. In this work were performed the geometry optimization (using the quantum chemistry AM1, ab initio HF3-21G* and HF6-31G* level of theory), conformational analysis(AM1 and ab initio HF6-31G*) and single point calculations (AM1). All the lowest energy conformers from conformational analysis were submitted to single point calculations. These models were used to determine the physicochemical properties (energy of formation in vacuum and solvated media, energy of HOMO, energy of LUMO, dipole moment, hardness, chemistry potential, electronic affinity, ionization potential and charges of electrostatic potential) and the molecular electrostatic potential, HOMO and LUMO maps, respectively. All calculations were performed using the computational software Spartan O2 for Linux, Spartan O4 for Windows, and the ClogP 4.0 (only for hydrophobicity). The cathodic reduction and half-wave potentials values and the set of physicochemical properties, which are obtained from quantum chemistry calculations, as well as the electronic effect (σp and σR derived of σ Hammett) and the hydrophobicity property (ClogP, and π of Hansch) were used in QSPR analysis applying Hansch analysis and PLS methodologies. Afterwards, the antichagasic activity of ten compounds (eight thiofilidenic and two furfurylidenic derivatives) was evaluated considering the influence of nitrocompounds reduction on biological activity. This assay allows to analyse the antiproliferative effect of nitrocompounds on the parasites growing, in twenty-four (24) hours, reading the number of trypanosomes in Haemocytometer. The voltammetric methodologies allowed to determinate the cathodic reduction and half-wave potential values in aprotic and mixed media to all nitrocompounds. In protic media, however, the cathodic reduction potential values were obtained only for the 5-nitro-2-tiofilidenic benzhidrazides. Those procedures seen to be appropriated for perform the electrochemistry reduction evaluation. The geometry optimization and conformational analysis allows determining a lack of structural planarity in all derivatives located at the bond between the carbonilic carbon and benzhoyl group. Additionally, the electrostatic potential maps presented a decreased on the electronic conjugate effect of the structures of nitrocompounds investigated. The conformational analysis allows us to determine the nitroheterocyclic biosostere conformations dependence on the reaction phase studied. The UV/visible spectra presented two waves, which are indicating responses related to different chromophores. These results suggest the absence of conjugate effect in nitrocompound structure, indicating that the presence of groups attached to benzene ring do not exert influence on the nitro group attached to heterocyclic ring. In the QSPR analysis was not detected any correlations between thel physicochemical properties and the cathodic reduction and reduction potentials, in all reaction media investigated. Those results might be occurred due to the lack of molecular co-planarity, which is reducing the influence of any property on the nitrocompounds reduction. Almost all nitrocompounds investigated show higher antichagasic activity than the reference drug, benznidazol. In a preliminary analysis it was verified that the cathodic reduction and half-wave potentials do not exert influence on the nitrocompounds antichagasic activity.

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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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24

Jover, Modrego Jesús. "Aplicació de la metodologia QSPR al càlcul de propietats de compostos inorgànics i de sistemes multicomponents." Doctoral thesis, Universitat de Barcelona, 2008. http://hdl.handle.net/10803/665934.

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En esta tesis se ha utilizado la metodología QSPR para calcular las propiedades de diferentes compuestos y sistemas complejos que no habían estudiados anteriormente. En concreto, se han establecido modelos que permiten el cálculo de la viscosidad y la tensión superficial, en estado líquido, y la entalpía de formación en fase gas para conjuntos de compuestos organometálicos de fórmula general MRnXm, en la que M puede ser un metal, semimetal o no metal de los grupos 12 al 16 de la tabla periódica; los grupos R corresponden a sustituyentes orgánicos alquílicos, arílicos, etc.; y los ligandos terminales X pueden ser cloro, bromo, yodo e hidrógeno. Se ha estudiado también la basicidad catiónica de un conjunto de compuestos orgánicos, de naturaleza química muy diversa, frente al catión Li+. En general, esta propiedad puede asociarse a la energía del proceso de formación de los complejos [Li-Ligando]+. Los sistemas complejos estudiados, que reciben el nombre de multicomponentes, son aquellos en los que la propiedad estudiada depende, a la vez, de dos o más elementos constituyentes del sistema. Las propiedades estudiadas en esta tesis son: las afinidades y basicidades catiónicas de los aminoácidos habituales frente a los cationes monovalentes de litio, sodio, potasio, cobre y plata; y las constantes de acidez (pKa) de familias de ácidos orgánicos en diferentes solventes, en este caso las familias de ácidos orgánicos estudiadas son fenoles, ácidos benzoicos, ácidos carboxílicos alifáticos y anilinas. En el tratamiento de estos sistemas multicomponentes se han utilizado descriptores externos para caracterizar a los cationes metálicos y los solventes. En el primer caso se han utilizado propiedades físicas, como potenciales de ionización, afinidades electrónicas, escalas de electronegatividad, etc.; para los diferentes solventes se han usado también propiedades físicas, como el momento dipolar, la constante dieléctrica, la polarizabilidad, etc.; y parámetros derivados de las diferentes escalas de polaridad más habituales, como los parámetros de Kamlet y Taft, los de Koppel y Palm, los de Drago, Gutmann, etc. Los modelos, lineales y no lineales, desarrollados para todas las propiedades proporcionan resultados excelentes para todas ellas, con valores de R2 mayores que 0.95, errores muy bajos y capacidad predictiva elevada, comprobada mediante la utilización de conjuntos de valores externos. Además de proporcionar una manera de calcular las propiedades, los modelos establecidos contienen descriptores que permiten realizar, en todos los casos, una interpretación razonable de las propiedades en términos fisicoquímicos.

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Delouis, Grace. "Modélisation QSPR de solvants d’intérêt technologique : les liquides ioniques et les électrolytes pour batteries Li-ion." Thesis, Strasbourg, 2017. http://www.theses.fr/2017STRAF035/document.

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Cette thèse a pour but de modéliser les liquides ioniques et les électrolytes pour batteries Li-ion. Nous avons développé des modèles SVR afin de prédire 9 propriétés d’intérêt pour ces solvants. Les modèles construits pour les liquides ioniques ont permis la détection de divers problèmes, et sont accessibles sur le site web du laboratoire : infochim.u-strasbg.fr/webserv/VSEngine.html. Les modèles construits pour les électrolytes ont permis la modélisation de candidats testés expérimentalement par nos collaborateurs. Le nombre de données étant limité pour ces solvants, nous avons également testé l’approche transductive par le biais de la TRR (Transductive Ridge Regression). Nous avons mis en place un protocole d’optimisation des paramètres de la méthode et appliqué la TRR aux solvants étudiés. Les résultats obtenus par la TRR sont légèrement meilleurs que ceux de la Régression Ridge, mais restent modestes si on veut éviter une détérioration accidentelle du modèle
This thesis is dedicated to the modelling of ionic liquids and electrolytes of Li-ion batteries. We developed several SVR models in order to predict 9 interesting properties of these solvents. The models built for the ionic liquids allowed us to detect several problems, and are freely available on the laboratory’s website: infochim.u-strasbg.fr/webserv/VSEngine.html. The models built for the electrolytes were used to model some candidates tested experimentally by our colleagues. As the amount of data is quite small for these solvents, we also tested the transductive approach with the help of the TRR (Transductive Ridge Regression). We have developed an optimization procedure for the method’s parameters, and applied the TRR to the studied solvents. The results obtained with the TRR are slightly better than of the Ridge Regression but stay modest if we want to avoid any accidental damage of the model

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26

Amboni, Renata Dias de Mello Castanho. "Estudo da correlação quantitativa entre estrutura e propriedade (QSPR) usando descritores topológicos para compostos carbonílicos alifáticos." Florianópolis, SC, 2001. http://repositorio.ufsc.br/xmlui/handle/123456789/81581.

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Tese (doutorado) - Universidade Federal de Santa Catarina, Centro de Ciências Físicas e Matemáticas. Curso de Pós-Graduação em Química
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Neste trabalho foi aplicada a relação quantitativa entre estrutura e atividade, empregando-se diferentes descritores moleculares para estimar o odor frutal de ésteres alifáticos. Os parâmetros estatísticos, obtidos nas equações para os ésteres, empregando-se o método de regressão linear múltipla, foram de boa qualidade. O modelo obtido teve uma alta capacidade de predição, como estabelecido pelo coeficiente de validação cruzada. O método semi-empírico topológico (IET) foi ampliado para estimar a retenção cromatográfica, em fases estacionárias de baixa polaridade, de ésteres, aldeídos e cetonas lineares e ramificados. Os parâmetros estatísticos das regressões lineares simples entre os índices de retenção de Kováts e o IET foram excelentes para todos os compostos. Os modelos de correlação quantitativa entre estrutura e retenção cromatográfica obtidos com um único descritor tiveram alta capacidade de predição, além de apresentarem uma melhora na ordem de precisão e exatidão que os métodos de regressão linear múltipla. Este IET foi aplicado para estimar o ponto de ebulição de aldeídos e cetonas e os valores de "threshold" de odor de cetonas com odor canforáceo e frutal. Os pontos de ebulição de 35 aldeídos e cetonas foram precisamente estimados através de uma regressão linear simples e os valores dos "thresholds" de odor de 27 cetonas foram estimados através de uma função polinomial quadrática. Assim, o método semi-empírico topológico, baseado no comportamento geral da retenção cromatográfica de ésteres, aldeídos e cetonas utilizando um único descritor, representa um grande avanço nos estudos de correlação quantitativa entre estrutura e propriedade (QSPR).

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27

Valenzuela, Venegas Guillermo Andrés. "Diseño y análisis de significancia estadística de descriptores en la elección de moléculas a reformar utilizando CAMPD." Tesis, Universidad de Chile, 2014. http://www.repositorio.uchile.cl/handle/2250/116855.

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Ingeniero Civil Químico
Hoy en día, para cubrir las necesidades energéticas a nivel industrial, de transporte y social, se utilizan combustibles fósiles, los que dañan el medio ambiente. Una alternativa para reemplazar esta fuente es el hidrógeno, el que puede ser producido a través del reformado de compuestos orgánicos como el metanol, metano, etanol, glicerol, pentano, hexadecano, entre otros. Pero, ya que se tiene un conjunto de compuestos reformables, cabe pensar lo siguiente ¿Son éstas las mejores moléculas para reformar? ¿Cuál es la mejor? ¿Cómo se podría determinar? Estas preguntas resultan difíciles de responder, ya que corresponden a un problema inverso y de optimización, cuya resolución es lenta y costosa. La solución que se propone, es utilizar CAMPD (Computer Aided Molecular and Process Design), que considera la definición de descriptores y QSPRs, para estimar el comportamiento de las propiedades macroscópicas (variables de salida que se desean optimizar) a partir de propiedades microscópicas (variables de entrada), para así facilitar el ciclo de optimización. CAMPD por su parte, está vinculado con el concepto de piezas, que se utilizan para generar los compuestos candidatos que cumplen las propiedades macroscópicas objetivo. Es por eso, que el presente trabajo, se enmarca en la primera etapa de CAMPD, lo que corresponde a definir descriptores estadísticamente significativos para el desempeño de moléculas en el proceso de reformado y proponer un tamaño de piezas que pueda representar al conjunto de moléculas a analizar y que genere la menor cantidad estructuras infactibles fisicoquímicamente. En cuanto al tamaño de piezas, se selecciona al grupos de átomos, ya que logran representar la totalidad del conjunto de compuestos y generar alrededor de 140 estructuras infactibles. Por otro lado, se determina que los descriptores que se obtienen para las propiedades macroscópicas: rendimiento de hidrógeno, emisiones y costo de producción; son el número de carbonos (Z1), hidrógenos (Z2) y grupos OH (Z3). Por su parte, a través del PCA, se obtienen dos variables, v1 = Z1 y v2 = 0.42Z2 + 0.903Z3 que resumen y explican el comportamiento de las tres propiedades macroscópicas. Cabe señalar, que podrían existir más descriptores (como el ángulo de enlace o la accesibilidad al carbono), pero no se opta por ellos, ya que su cálculo es muy complejo. Por último, se debe señalar que muchos de los datos que se utilizan en el análisis cuantitativo y cualitativo, son estimados, por lo que las relaciones obtenidas, podrían no ser tan acertadas. Además, el análisis cuantitativo, solo determina si existe alguna relación lineal entre las variables, por lo que puede generar sesgo en los resultados. Sin embargo, se debe destacar el desarrollo de una estrategia para abordar este problema (o alguno similar), una vez que se posean todos los datos necesarios y, así, obtener las relaciones correctas. No disponible a texto completo No autorizada por el autor a ser publicada a texto completo en Cybertesis.

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Liu, Tao. "Chemoinformetics for green chemistry." Doctoral thesis, Linnéuniversitetet, Institutionen för naturvetenskap, NV, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-8634.

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This thesis focuses on the development of quantitative structure-activity relationship (QSPR) models for physicochemical properties, e.g., vapor pressure and partitioning coefficients. Such models can be used to estimate environmental distribution and transformation of the pollutants or to characterize solvents properties. Here, chemoinformatics was used as an efficient tool for modeling to produce safe chemicals based on green chemistry principles. Experimental determinations are only available for a limited number of the chemicals; however, theoretical molecular descriptors can be used for modeling of all organic compounds. In this thesis, we developed and validated a global and local QSPR model for vapor pressure of liquid and subcooled liquid organic compounds, in which perfluorinated compounds (PFCs) as outliers appeared in the model due to their molecular properties. Subsequently, after the update of the previous model, the vapor pressure of perfluorinated compounds (PFCs) for which no reliable experimental data are available was successfully predicted. At the same time, we used partitioning between n-octanol/water (Kow) and water solubility (Sw) to investigate the similarities and differences between linear solvation energy relationship (LSER) and partial least square projection to latent structures (PLS) models. Further, we developed QSPR model for prediction of melting points and boiling points of PFCs using multiple linear regression (MLR), PLS and associative neural networks (ASNN) approaches, meanwhile, the applicability domain of PFCs was also investigated. Experimental, semi-empirical and theoretical quantitative structure-retention relationship (QSRR) models were used to accurately predict retention factors (logk) in reversed-phase liquid chromatography (RPLC). These models are useful to characterize solvents for determination of the behavior and interactions of molecular structure and develop chromatographic methods. In both of QSPR and QSRR models using the PLS method, the first and second components captured main information which is related to van der Waals forces and polar interactions, and their results coincide with those from LSER. The results showed that the models of physicochemical properties and retention factors (logk) in chromatographic system can be successfully developed by the PLS method. PLS models were able to predict physicochemical properties of organic compounds directly from theoretical descriptors without prior synthesis, measurement or sampling. Further, the PLS method could overcome colinearity in data sets, and it is therefore a rapid, cheap and highly efficient approach

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Glavatskikh, Marta. "Modeling and visualization of complex chemical data using local descriptors." Thesis, Strasbourg, 2018. http://www.theses.fr/2018STRAF008/document.

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Cette étude considère des systèmes où non seulement la structure moléculaire, mais les conditions expérimentales sont impliquées. Les structures chimiques ont été codées par des descripteurs locaux ISIDA MA ou ISIDA CGR, ciblant spécifiquement les centres actifs et leur environnement le plus proche. Les descripteurs locaux ont été combinés avec les paramètres spécifiques des conditions expérimentales, codant ainsi un objet chimique particulier. La méthodologie a été appliquée avec succès pour la modélisation QSPR des paramètres thermodynamiques et cinétiques des interactions intermoléculaires (liaisons halogène et hydrogène), des équilibres tautomères et des réactions chimiques (cycloaddition et SN1). La méthode GTM a été appliquée pour la première fois pour la modélisation et la visualisation de données chimiques mixtes. La méthode sépare avec succès les groupes de données à la fois en raison des structures et des conditions
This work describes original approaches for predictive chemoinformatics modeling of molecular interactions and reactions as a function of the structures of interacting partners and of the chemical environment (experimental conditions). Chemical structures have been encoded by local ISIDA MA-based or CGR-based descriptors, specifically targeting the active centers and their closest environment. The local descriptors have been combined with the specific parameters of experimental conditions, thereby encoding a particular chemical object. The methodology has been successfully applied for QSPR modeling of thermodynamic and kinetic parameters of intermolecular interactions (halogen and hydrogen bonds), tautomeric equilibria and chemical reactions (cycloaddition and SN1). GTM method has been applied for the first time for QSPR modeling and visualization of mixed chemical data. This method successfully separates data clusters on account of both chemical structures and experimental conditions

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Al-Antary, Doaa Tawfiq, and Doaa Tawfiq Al-Antary. "The Estimation of Selected Physicochemical Properties of Organic Compounds." Diss., The University of Arizona, 2018. http://hdl.handle.net/10150/626758.

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Thermodynamic relationships are used to predict several physicochemical properties of organic compounds. As described in chapter one, the UPPER model (Unified Physicochemical Property Estimation Relationships) has been used to predict nine essential physicochemical properties of pure compounds. It was developed almost 25 years ago and has been validated by the Yalkowsky group for almost 2000 aliphatic, aromatic, and polyhalogenated hydrocarbons. UPPER is based on a group of additive and nonadditive descriptors along with a series of well-accepted thermodynamic relationships. In this model, the two-dimensional chemical structure is the only input needed. Chapter (1) extends the applicability of UPPER to hydrogen bonding and non-hydrogen bonding aromatic compounds with several functional groups such as alcohol, aldehyde, ketone, carboxylic acid, carbonate, carbamate, amine, amide, nitrile as well as aceto, and nitro compounds. The total data set includes almost 3000 compounds. Aside from the enthalpies and entropies of melting and boiling, no training set is used for the calculation of the properties. The results show that UPPER enables a reasonable estimation of all the considered properties. Chapter (2) uses modification of the van't Hoff equation to predict the solubility of organic compounds in dry octanol as explained in chapter two. The equation represents a linear relationship between the logarithm of the solubility of a solute in octanol to its melting temperature. More than 620 experimentally measured octanol solubilities, collected from the literature, are used to validate the equation without using any regression or fitting. The average absolute error of the prediction is 0.66 log units. Chapter (3) compares the use of a statistic based model for the prediction of aqueous solubility to the existing general solubility equation (GSE).

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Santos, Victor Hugo Jacks Mendes dos. "Uma perspectiva da modelagem QSPR para triagem/desenho de catalisadores para a s?ntese de carbonatos oleoqu?micos." Pontif?cia Universidade Cat?lica do Rio Grande do Sul, 2018. http://tede2.pucrs.br/tede2/handle/tede/8260.

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To date, only a small number of organocatalysts have been applied to produce oleochemical carbonates, while the description of new catalysts system still limited. This work presents a preliminary perspective of Quantitative Structure-Property Relationship (QSPR) modeling to assist in the targeted choice/design of active organocatalysts to produce cyclic carbonates. The QSPR was developed by applying the molecular descriptors (2D) to model the structure-property relationship between the organocatalysts features and its activity to produce oleochemical carbonates. From the virtual screening, a total of 122 catalysts have their activity predicted and the best molecular targets are proposed. The principal molecular features (organic structure, molecular arrangement, carbon chain size and substituent type) were identified through data mining, while the principal component analysis (PCA) proved to be suitable to perform the exploratory analysis of the molecules set. In addition, is presented the first report of the application of cetyltrimethylammonium bromide (CTAB) as a new catalyst to produce oleochemical carbonate derived from soy, canola and rice oils. The reactions were performed in a 50 cm3 stainless steel autoclave at 120?C, for 48 hours, without stirring, 5 MPa (p, CO2), 2 g of epoxidized oil, 4 mL of butanol and 5 mol% of CTAB. From the proposed method, all reactions showed more than 98% of epoxide conversion to cyclic carbonate for all the vegetable oil. In this way, the QSPR modelling can be applied to reduce the costs and time in the organocatalysts screening/design for the cyclic carbonates synthesis from CO2 and epoxides.
At? o momento, apenas um pequeno n?mero de organocatalisadores foram aplicados para produ??o de carbonatos oleoqu?micos, enquanto a descri??o de novos sistemas de catalisadores ainda ? limitada. O presente trabalho apresenta uma perspectiva preliminar da modelagem por Rela??o Quantitativa Estrutura-Propriedade (QSPR) para auxiliar na escolha/desenho de novos organocatalisadores para produ??o de carbonatos c?clicos. O QSPR foi desenvolvido aplicando os descritores moleculares (2D) para modelar a rela??o estrutura-propriedade entre as caracter?sticas dos organocatalisadores e sua atividade para produ??o de carbonatos oleoqu?micos. A partir da triagem virtual, um total de 122 catalisadores tiveram sua atividade prevista e os melhores alvos moleculares s?o propostos. As principais caracter?sticas moleculares (estrutura org?nica, arranjo molecular, tamanho da cadeia de carbono e tipo de substituinte) foram identificadas atrav?s da minera??o de dados, enquanto a an?lise de componentes principais (PCA) mostrou-se adequada para realizar a an?lise explorat?ria do conjunto de mol?culas. Al?m disso, ? apresentado o primeiro relato da aplica??o do brometo de cetiltrimetilam?nio (CTAB) como um catalisador para a produ??o de carbonato oleoqu?mico derivados dos ?leos de soja, canola e arroz. As rea??es foram realizadas em uma autoclave de a?o inoxid?vel de 50 cm3 a 120 ? C, durante 48 horas, sem agita??o, 5 MPa (p, CO2), 2 g de ?leo epoxidado, 4 mL de butanol e 5% molar de CTAB. A partir do m?todo proposto, todas as rea??es apresentaram mais de 98% de convers?o de ep?xido em carbonato c?clico para todos os ?leos vegetais. Desta forma, a modelagem QSPR pode ser aplicada para reduzir os custos e tempo na sele??o/desenho de organocatalisadores para a s?ntese de carbonatos c?clicos a partir de CO2 e ep?xidos.

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Gaudin, Théophile. "Développement de modèles QSPR pour la prédiction et la compréhension des propriétés amphiphiles des tensioactifs dérivés de sucre." Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2318/document.

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Les tensioactifs dérivés de sucres représentent la principale famille de tensioactifs bio-sourcés et constituent de bons candidats pour substituer les tensioactifs dérivés du pétrole puisqu'ils sont issus de ressources renouvelables et peuvent être autant, voire plus performants dans diverses applications, comme la formulation (détergents, cosmétiques,…), la récupération assistée du pétrole ou des minéraux, etc. Différentes propriétés amphiphiles permettent de caractériser la performance des tensioactifs dans de telles applications, comme la concentration micellaire critique, la tension de surface à la concentration micellaire critique, l'efficience et le point de Krafft. Prédire ces propriétés serait bénéfique pour identifier plus rapidement les tensioactifs possédant les propriétés désirées. Les modèles QSPR sont des outils permettant de prédire de telles propriétés, mais aucun modèle QSPR fiable dédié à ces propriétés n'a été identifié pour les tensioactifs bio-sourcés, et en particulier les tensioactifs dérivés de sucres. Au cours de cette thèse, de tels modèles QSPR ont été développés. Une base de données fiables est nécessaire pour développer tout modèle QSPR. Concernant les tensioactifs dérivés de sucres, aucune base de données existante n'a été identifiée pour les propriétés ciblées. Cela a donné suite à la construction de la première base de données de propriétés amphiphiles de tensioactifs dérivés de sucres, qui est en cours de valorisation. L'analyse de cette base de données a mis en évidence différentes relations empiriques entre la structure de ces molécules et leurs propriétés amphiphiles, et permis d'isoler des jeux de données les plus fiables et au protocole le plus homogène possibles en vue du développement de modèles QSPR. Après établissement d'une stratégie robuste pour calculer les descripteurs moléculaires constituant les modèles QSPR, qui s'appuie notamment sur des analyses conformationnelles des tensioactifs dérivés de sucres et des descripteurs des têtes polaires et chaînes alkyles, différents modèles QSPR ont été développés, validés, et leur domaine d'applicabilité spécifié, pour la concentration micellaire critique, la tension de surface à la concentration micellaire critique, l'efficience et le point de Krafft. Pour les trois premières propriétés, des modèles quantitatifs performants ont pu être obtenus. Si les descripteurs quantiques ont apporté un gain prédictif important pour la tension de surface à la concentration micellaire critique, et un léger gain pour la concentration micellaire critique, aucun gain n'a été observé pour l'efficience. Pour ces trois propriétés, des modèles simples basés sur des descripteurs constitutionnels des parties hydrophile et hydrophobe de la molécule (comme des décomptes d'atomes) ont aussi été obtenus. Pour le point de Krafft, deux arbres de décision qualitatifs, classant la molécule comme soluble ou insoluble dans l'eau à température ambiante, ont été proposés. Les descripteurs quantiques ont ici aussi apporté un gain en prédictivité, même si un modèle relativement fiable basé sur des descripteurs constitutionnels des parties hydrophile et hydrophobe de la molécule a aussi été obtenu. Enfin, nous avons montré comment ces modèles QSPR peuvent être utilisés, pour prédire les propriétés de nouvelles molécules avant toute synthèse dans un contexte de screening, ou les propriétés manquantes de molécules existantes, et pour le design in silico de nouvelles molécules par combinaison de fragments
Sugar-based surfactants are the main family of bio-based surfactants and are good candidates as substitutes for petroleum-based surfactants, since they originate from renewable resources and can show as good as, or even better, performances in various applications, such as detergent and cosmetic formulation, enhanced oil or mineral recovery, etc. Different amphiphilic properties can characterize surfactant performance in such applications, like critical micelle concentration, surface tension at critical micelle concentration, efficiency and Kraft point. Predicting such properties would be beneficial to quickly identify surfactants that exhibit desired properties. QSPR models are tools to predict such properties, but no reliable QSPR model was identified for bio-based surfactants, and in particular sugar-based surfactants. During this thesis, such QSPR models were developed. A reliable database is required to develop any QSPR model. Regarding sugar-based surfactants, no database was identified for the targeted properties. This motivated the elaboration of the first database of amphiphilic properties of sugar-based surfactants. The analysis of this database highlighted various empirical relationships between the chemical structure of these molecules and their amphiphilic properties, and enabled to isolate the most reliable datasets with the most homogeneous possible protocol, to be used for the development of the QSPR models. After the development of a robust strategy to calculate molecular descriptors that constitute QSPR models, notably relying upon conformational analysis of sugar-based surfactants and descriptors calculated only for the polar heads and for the alkyl chains, different QSPR models were developed, validated, and their applicability domain defined, for the critical micelle concentration, the surface tension at critical micelle concentration, the efficiency and the Kraft point. For the three first properties, good quantitative models were obtained. If the quantum chemical descriptors brought a significant additional predictive power for the surface tension at critical micelle concentration, and a slight improvement for the critical micelle concentration, no gain was observed for efficiency. For these three properties, simple models based on constitutional descriptors of polar heads and alkyl chains of the molecule (like atomic counts) were also obtained. For the Krafft point, two qualitative decision trees, classifying the molecule as water soluble or insoluble at room temperature, were proposed. The use of quantum chemical descriptors brought an increase in predictive power for these decision trees, even if a quite reliable model only based on constitutional descriptors of polar heads and alkyl chains was also obtained. At last, we showed how these QSPR models can be used, to predict properties of new surfactants before synthesis in a context of computational screening, or missing properties of existing surfactants, and for the in silico design of new surfactants by combining different polar heads with different alkyl chain

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Couchaux, Gabriel. "Relation structure-propriété pour la cinétique de la réaction amine-CO2 en solution [i.e.solutions] aqueuses." Thesis, Université de Lorraine, 2013. http://www.theses.fr/2013LORR0206/document.

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Le procédé de captage du CO2 en post-combustion par lavage aux amines est actuellement le plus mature pour réduire les émissions de dioxyde de carbone industrielles. Cependant, s'il existe de nombreux démonstrateurs, son coût en termes d'investissement et de fonctionnement est encore trop important pour être mis en oeuvre à une large échelle. La cinétique de réaction amine-dioxyde de carbone est un des principaux facteurs influençant ces coûts. Les objectifs de ces travaux portent sur l'étude et la compréhension de la cinétique de réaction amine-CO2 et de la mise en place d'un modèle structure-propriété prédictif. Cette démarche est adaptée au grand nombre d'amines envisageables pour le procédé. Dans un premier temps nous avons étudié cinq types d'amines (primaires, secondaires acycliques, secondaires cycliques, tertiaires et multi-amines) représentatifs des molécules candidates. Parmi ces molécules deux comportements peuvent être distingués : d'une part les amines qui forment des carbamates et d'autre part celles qui n'en forment pas. Des mesures réalisées sur des solutions diluées d'amine, à différentes concentrations et à 25°C, obtenues par la technique d'écoulement bloqué ont permis de caractériser la cinétique intrinsèque de chacune des 87 amines par deux constantes cinétiques. Pour chaque type d'amine les principaux facteurs structuraux, électroniques et géométriques influant sur la cinétique de réaction ont été identifiés. Un modèle statistique utilisant des descripteurs moléculaires pour décrire les différents paramètres de chaque amine a permis d'établir une relation structure - propriété pour différentes constantes cinétiques. Un nouveau descripteur de l'encombrement stérique de l'azote a également été calculé pour décrire les résultats et prédire la réactivité des amines
The post-combustion process by amine scrubbing is currently the most mature to reduce carbon dioxide emissions from industry. However, if there are numerous demonstrators, the investment and operating cost of this process are still too important to develop it in a large scale. The kinetics of reaction between the amine and the carbon dioxide is one of the major factor which influence the costs. The objectives of this work are to study and understand the kinetics of the amine-CO2 reaction and to set up of a predictive structure-property model. This approach is adapted to the large number of possible amines which can be candidates for the process. In a first time we study five kinds of amines (primary, acyclic secondary, cyclic secondary, tertiary and multi-amines) representatives of candidate molecules. Among those molecules, two behaviours can be distinguished: one the one hand amines which form carbamates and on the other hand those which do not form carbamates. Measurements have been realised at 25 °C in diluted solutions by stopped-flow technique to characterize the intrinsic kinetics of each of the 87 studied amines using two kinetic constants. For each kind of amine, the main structural factors, electronic and geometric, which impact the kinetics of reaction have been identified. Then, from a statistical model using molecular descriptors to describe the different parameters of each amine, a structure-property relationship has been set up with the different kinetic constants. A descriptor of the steric hindrance has been developed

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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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Langeron, Julie. "Etablissement d’une relation de type structure-propriétés (QSPR) entre les propriétés des pesticides et deux sols de Champagne crayeuse." Thesis, Reims, 2012. http://www.theses.fr/2012REIMS023/document.

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Ces travaux s'inscrivent dans le cadre du contrat d'objectifs AQUAL dont le but est la lutte contre les pollutions diffuses en milieu rural. Ils font également l'objet d'un partenariat avec le programme Phyt'Eau Ref initié par la Chambre Régionale d'Agriculture de Champagne-Ardenne. Ils portent sur la compréhension de la rétention et du transfert des pesticides du sol à la nappe dans les sols champardennais. Deux sols différents de par leur contenu en matière organique et en calcaire ont été choisis pour réaliser l'étude afin d'étudier le comportement des pesticides dans des sols caractéristiques de la région (pH et taux de calcaire élevé). L'étude a été réalisée sur quarante pesticides appartenant à diverses familles chimiques et de propriétés physico-chimiques différentes. L'objectif est d'identifier les propriétés des pesticides gouvernant leur rétention dans les sols puis d'établir une relation de type structure-propriétés (QSPR) permettant la prédiction du coefficient d'adsorption Kd. L'adsorption et le transfert des pesticides ont été étudiés au laboratoire au moyen d'expériences en réacteurs fermés (étude à l'équilibre) et en colonne de sol reconstituées au laboratoire. Le tracé d'isothermes d'adsorption suivi d'une étude statistique a permis d'identifier l'hydrophobicité, la polarisabilité et la solubilité comme les paramètres physico-chimiques majeurs gouvernant la rétention des quarante pesticides étudiés. Des relations à deux paramètres ont été proposées et testées afin de pouvoir prédire le coefficient d'adsorption des pesticides. Enfin, les travaux réalisés en mode dynamique (colonne) ont permis de mettre en évidence que le phénomène de transfert peut être relié à l'adsorption des pesticides et qu'il est possible de passer de l'un à l'autre des paramètres caractérisant ces deux phénomènes par de simples relations permettant ainsi de s'affranchir de lourdes expériences
This work is a part of the multidisciplinary AQUAL research program, which aims at to strive against diffuse pollutions in rural environment. It also took part in the Phyt'Eau Ref program initiated by the “Chambre Régionale d'Agriculture de Champagne-Ardenne”. It deals with the comprehension of retention and transfer of pesticides from soil to groundwaters in Champagne-Ardenne. Two different soils by their organic matter and calcite contents, were chosen to carry out the study in order to evaluate the behavior of pesticides in characteristic soils of the region (pH and calcite content). The study was carried out on forty pesticides from various chemical families and having different physico-chemical properties. The aim was to identify the physico-chemical properties of pesticides governing their retention in soils and then to establish a quantitative structure properties relationship (QSPR) predicting the adsorption coefficient Kd. Adsorption and transfer of pesticides were studied in laboratory through batch experiments (equilibrium study) and soil column reconstituted in laboratory. Adsorption isotherm plot followed by a statistical analysis allowed identifying hydrophobicity, polarisability and solubility as the main physico-chemical parameters correlated to the pesticide retention. Relations combining two of these parameters were proposed and tested in order to predict the pesticide adsorption coefficient. Finally, studies in dynamic mode (column) allowed to evidence that the transfer phenomenon can be correlated to the pesticide adsorption nd that it was possible to go from one to the other parameters describing these two phenomena by simple linear relations, allowing to get rid of onerous experiences

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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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Saraf, Sanjeev R. "Molecular characterization of energetic materials." Texas A&M University, 2003. http://hdl.handle.net/1969.1/331.

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Assessing hazards due to energetic or reactive chemicals is a challenging and complicated task and has received considerable attention from industry and regulatory bodies. Thermal analysis techniques, such as Differential Scanning Calorimeter (DSC), are commonly employed to evaluate reactivity hazards. A simple classification based on energy of reaction (-H), a thermodynamic parameter, and onset temperature (To), a kinetic parameter, is proposed with the aim of recognizing more hazardous compositions. The utility of other DSC parameters in predicting explosive properties is discussed. Calorimetric measurements to determine reactivity can be resource consuming, so computational methods to predict reactivity hazards present an attractive option. Molecular modeling techniques were employed to gain information at the molecular scale to predict calorimetric data. Molecular descriptors, calculated at density functional level of theory, were correlated with DSC data for mono nitro compounds applying Quantitative Structure Property Relationships (QSPR) and yielded reasonable predictions. Such correlations can be incorporated into a software program for apriori prediction of potential reactivity hazards. Estimations of potential hazards can greatly help to focus attention on more hazardous substances, such as hydroxylamine (HA), which was involved in two major industrial incidents in the past four years. A detailed discussion of HA investigation is presented.

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Moreira, Bastos Patricia. "Comparison of experimentally and theoretically determined oxidation and photochemical transformation rates of some organohalogens to promote prediction of persistence." Doctoral thesis, Stockholms universitet, Institutionen för miljökemi, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-26040.

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The diversity of choices we have to make everyday influence our environment and ourselves in more ways than most of us realise. Anthropogenic substances, such as flame retardants, date back as early as 450 BC when the Egyptians used alum to reduce flammability. The increasing demand for new articles has led to an increased production of chemical substances, for which many are commercially produced without complete knowledge on properties such as persistence, bioaccumulation and toxicology (PBT). Commercial compounds may be properly tested and denominated as “safe” regarding PBT properties, but their degradation products and/or metabolites may cause environmental impact. The availability of uniform and accurate data for prediction of persistence is of key importance for the understanding of chemical fate. A method to determine the susceptibility of chemicals to undergo oxidation in water has been developed and applied on several organohalogens, including PBDEs and OH-PBDEs. The method was used to determine reaction rates and the group of OH-PBDEs were subsequently subjected to photolysis by use of UV-light. Hence, susceptibility to undergo both oxidation and photolysis for the OH-PBDEs were investigated and compared to previously reported degradation rates on PBDEs. As a final step in promoting the prediction of persistence, Quantitative structure-property relationship (QSPR) models were performed on a set of compounds which had undergone photolytic degradation under similar conditions. The QSPRs were used as a preliminary step in predicting photolysis half-lives for chemical substances and to determine which physicochemical descriptors are of greatest importance thereof. This thesis presents the possibility of performing and assessing oxidation transformations on compounds of low and high water solubility, photolysis transformations in various media and using obtained data to predict behaviour via QSPR models, to promote predictions of persistence.

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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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Reis, Ralpho Rinaldo dos. "Modelos de predição do coeficiente de sorção no solo de pesticidas não iônicos: diferentes algoritmos de logP e uma abordagem alternativa de logS." Universidade Estadual do Oeste do Parana, 2013. http://tede.unioeste.br:8080/tede/handle/tede/166.

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Collecting data on pesticide effects on the environment and several ecosystems is a slow and costly process. Therefore, significant research efforts have been focused on developing mathematical models to predict physical, chemical or biological properties of environmental interest. The soil sorption coefficient normalized to organic carbon content (Koc) is a physicochemical key parameter used in environmental risk assessments of substances released into the environment. Thus, several logKoc prediction models that use hydrophobic parameter (logP) or the logarithm of water solubility (logS) as descriptor have been reported in the last decades. Mostly, due to the lack of reliable experimental values of logP or logS, algorithms are used to calculate such properties. Despite the availability and easiness to access several algorithms for this purpose, scientific studies do not describe the procedure adopted to choose the algorithm used in quantitative structure-property relationship (QSPR) studies. Furthermore, the strong correlation between logP and logS prevents their application in the same mathematical equation obtained by multiple linear regression method. Since the sorption process of a chemical compound in soil is related both to its water solubility and its water/organic matter partition, it is expected models that are able to combine these two properties will can record more realistic results. This doctoral dissertation consists of two scientific papers. In the first one, a study was carried out to check the influence of choosing logP algorithm on logKoc modeling. Models were constructed to relate logKoc with logP according to different freeware algorithms. All models were assessed based on their statistic qualities and predictive power. The obtained results clearly showed that an arbitrary choice of the algorithm may not result in the best prediction model. On the other hand, a good choice can lead to obtaining simple models with statistic qualities and predictive power comparable to more complex models. The second paper aims at proposing an alternative approach for logKoc modeling, using simple descriptor of solubility, here referred as logarithm of corrected solubility by octanol/water partition (logSP). Thus, models were built with this descriptor and also with logP and logS conventional descriptors, which are isolated or associated with other explicative variables of easy physicochemical interpretation. The obtained models were validated and compared to other models previously published. The results showed that the use of logSP descriptor to replace the conventional ones led to obtaining simple models with statistic qualities and predictive power that are higher than other more complex models already found in literature.
A coleta de dados relativos aos danos causados pelos pesticidas sobre o meio ambiente e seus ecossistemas é lenta e onerosa. Desta maneira, grandes incentivos têm sido destinados às pesquisas que visam à construção de modelos matemáticos para predição de propriedades físicas, químicas ou biológicas de interesse ambiental. O coeficiente de sorção no solo normalizado para o conteúdo de carbono orgânico (Koc) é um importante parâmetro físico-químico utilizado nas avaliações de riscos ambientais das substâncias lançadas no meio ambiente. Assim, vários modelos para predição de logKoc, utilizando o parâmetro hidrofóbico (logP) ou o logaritmo da solubilidade em água (logS) como descritores, têm sido publicados nas últimas décadas. Muitas vezes, em virtude da ausência de valores experimentais confiáveis de logP ou logS, são usados algoritmos para o cálculo dessas propriedades. Apesar da disponibilidade e facilidade de acesso a diversos algoritmos para tal finalidade, os artigos científicos não descrevem o procedimento adotado para escolha do algoritmo usado nos estudos QSPR. Além disto, a forte correlação entre logP e logS impede que sejam usados em uma mesma equação obtida por regressão linear múltipla. Como o processo de sorção de um composto químico no solo está relacionado tanto com sua solubilidade em água como com sua partição água/matéria orgânica, espera-se que modelos que sejam capazes de combinar essas duas informações possam gerar resultados mais realistas. Este trabalho de tese é constituído de dois artigos. No primeiro artigo, foi feito um estudo para verificar a influência da escolha do algoritmo de logP na modelagem de logKoc. Foram construídos modelos que relacionam logKoc com logP a partir de diferentes algoritmos livres disponíveis. Todos os modelos foram avaliados quanto às suas qualidades estatísticas e poder de predição. Os resultados obtidos mostraram claramente que uma escolha arbitrária deste algoritmo pode não levar ao melhor modelo de predição. Por outro lado, uma boa escolha pode conduzir à obtenção de modelos simples com qualidades estatísticas e poder de predição comparáveis a de modelos mais complexos. No segundo artigo, o objetivo foi a proposição de uma abordagem alternativa para a modelagem de logKoc, utilizando um descritor simples de solubilidade, aqui designado como logaritmo da solubilidade corrigida pela partição octanol/água (logSP). Assim, foram construídos modelos com tal descritor e também com os descritores convencionais logP e logS, isolados ou associados com outras variáveis explicativas de fácil interpretação físico-química. Os modelos obtidos foram validados e comparados com outros modelos publicados anteriormente. Os resultados mostraram que o uso do descritor logSp em substituição aos descritores convencionais conduziu à obtenção de modelos simples com qualidades estatísticas e poder de predição superiores a de outros modelos mais complexos encontrados na literatura.

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Alexandre, Mike Abidine. "Optimisation du comportement mécanique de composites structuraux PEKK/Fibres de carbone par ensimage oligomères de PEKK." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30207.

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L'objectif de cette recherche est la conception et l'analyse d'un ensimage pour composites structuraux PEKK/Fibres de carbone (FC) continues. Les oligomères de PEKK (oPEKK) ont été synthétisés en laboratoire pour définir les caractéristiques physico-chimiques permettant leur utilisation comme agent d'ensimage. A partir de ce cahier des charges, un oligomère " pilote " a pu être synthétisé afin de mener des études sur la formulation de l'ensimage. A partir d'une étude quantitative de relation structure-propriété (QSPR) et des réseaux de neurone artificiels (ANN), le développement et l'optimisation d'une formulation d'ensimage " solvantfree " ont été réalisés Le dépôt de cet ensimage a été effectué selon deux protocoles : nous avons ainsi réalisé un " ensimage laboratoire " et " ensimage sur pilote ". Les performances mécaniques des composites PEKK/FC ensimés oPEKK ont été étudiées par analyse mécanique dynamique (AMD) ; quel que soit le protocole, l'ensimage optimise les performances mécaniques de manière significative. Il est intéressant de souligner que l'" ensimage sur pilote " est plus efficace que l'" ensimage laboratoire ". Outre, l'intérêt de l'ensimage au niveau de la mise en œuvre des composites, le transfert de contraintes fibre/ matrice est optimisé ce qui se traduit par une augmentation des modules mécaniques conservatif et dissipatif
The objective of this research is to design and analyze a sizing for PEKK / continuous carbon fiber (CF) structural composites. PEKK oligomers (oPEKK) were synthesized in the laboratory to define the physicochemical characteristics allowing their use as a sizing agent. From these specifications, a "pilot" oligomer was synthesized in order to carry out studies on the sizing formulation. From a study of quantitative structure-property relationship (QSPR) and artificial neural networks (ANN), the development and optimization of a "solvent-free" sizing formulation was performed. The deposit of this sizing was achieved according to two protocols: we thus produced a "laboratory sizing" and "pilot sizing". Mechanical performances of PEKK / CF without and with oPEKK sizing composites were studied by dynamic mechanical analysis (DMA). Whatever the protocol is, the sizing optimizes the mechanical performances significantly. It is interesting to note that "pilot sizing" is more efficient than "laboratory sizing". Besides the advantage of sizing for fiber placement in composite processing, the fiber / matrix stress transfer is optimized. Then, it results in an increase of both storage and loss modulus

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Stöcker, Hartmut. "Struktur-Eigenschafts-Korrelationen in Strontiumtitanat." Doctoral thesis, Technische Universitaet Bergakademie Freiberg Universitaetsbibliothek "Georgius Agricola", 2011. http://nbn-resolving.de/urn:nbn:de:bsz:105-qucosa-78565.

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Als Modellsystem für Oxide mit Perowskitstruktur ist Strontiumtitanat besonders geeignet, um generalisierbare Erkenntnisse über die Auswirkungen von Defekten zu gewinnen und ausgehend davon Struktur-Eigenschafts-Korrelationen zu diskutieren. Durch den Einsatz verschiedener oberflächensensitiver Methoden lässt sich im Ausgangszustand eine erhöhte Konzentration von Liniendefekten an der Oberfläche nachweisen, die sich durch Temperaturbehandlung verkleinert. Die Defektchemie bei hohen Temperaturen wird zur Simulation der elektrischen Leitfähigkeit in Abhängigkeit vom umgebenden Sauerstoff-Partialdruck genutzt. Die Dotierung des oxidischen Halbleitermaterials ist von Eigendefekten abhängig, wobei Sauerstoff-Leerstellen Donatorniveaus bilden und Strontium-Leerstellen Akzeptorcharakter besitzen. Neben der Diffusionsbewegung dieser Eigendefekte bei hohen Temperaturen kann bei niedrigen Temperaturen ein elektrisches Feld deren Umverteilung bewirken. Damit zeigt sich die Leitfähigkeit abhängig von externen elektrischen Feldern, aber auch weitere Eigenschaften sind auf diesem Wege modifizierbar. Im Rahmen der Arbeit werden strukturelle Änderungen, Valenz-Änderungen und veränderte mechanische Eigenschaften nachgewiesen, die jeweils abhängig vom elektrischen Feld schaltbar sind. Schließlich wird das gezielte Ausnutzen struktureller Defekte für Speicherzellen, die den schaltbaren Widerstand von Metall-SrTiO3-Kontakten zur Grundlage haben, vorgestellt. Die Anwendbarkeit des oxidischen Halbleiters als resistives Speicherelement beruht wiederum auf der Kopplung von Sauerstoff-Leerstellen an das elektrische Feld
Being a model system for oxides with pervovskite-type of structure, strontium titanate can be used to gain generalizable insights into the consequences of defects and to discuss resulting structure-property relationships. By employing different surface sensitive methods, an increased concentration of line defects is found at the surface that reduces on temperature treatment. The defect chemistry at elevated temperatures is used to simulate the electric conductivity depending on the oxygen partial pressure during annealing. Doping of the oxidic semiconductor depends on intrinsic defects, whereby oxygen vacancies form donor states and strontium vacancies have acceptor character. Beside the diffusion movement of these intrinsic defects at elevated temperatures, at low temperatures an electric field may cause their redistribution. Hence, the conductivity becomes dependent on external electric fields but also other properties can be altered in this way. Within this work, structural changes, valence changes and changing mechanical properties are shown to be switchable by the electric field. Finally, the dedicated usage of structural defects is demonstrated on memory cells that employ the switchable resistance of metal-SrTiO3 junctions. The applicability of the oxidic semiconductor as a resistive memory element is again based on the coupling between oxygen vacancies and the electric field

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Youmbi, Foka Marcel [Verfasser], Tim [Akademischer Betreuer] Clark, Tim [Gutachter] Clark, and Birgit [Gutachter] Strodel. "3D-QSAR/QSPR Based Surface-Dependent Modeling Approach Derived From Semi-Empirical Quantum Mechanical Calculations / Marcel Youmbi Foka ; Gutachter: Tim Clark, Birgit Strodel ; Betreuer: Tim Clark." Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2018. http://d-nb.info/1175206490/34.

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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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Merabtine, Yacine. "Etude des relations entre la structure des molécules odorantes et leurs équilibres rétention-libération entre phase vapeur et gels laitiers." Phd thesis, Université de Bourgogne, 2010. http://tel.archives-ouvertes.fr/tel-00583420.

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Une approche intégrée physicochimie et relations structure-activité a été mise en œuvre afin d'étudier le phénomène rétention-libération des composés d'arôme dans un gel laitier allégé additionné de pectine. Notre objectif était d'identifier les propriétés moléculaires qui régissent ce phénomène en supposant que la modification de la structure entraîne forcement un changement dans la rétention-libération des composés d'arôme. Dans ce but, nous avons déterminé les coefficients de partage de 28 composés d'arôme dans l'eau, dans des gels de pectine et dans des gels laitiers avec ou sans de pectine, à l'équilibre en utilisant la méthode PRV (Phase Ratio Variation). Nous avons ensuite effectué une étude des relations structure-rétention en évaluant les corrélations entre les coefficients de partage et quatre descripteurs traduisant quatre propriétés moléculaires : l'hydrophobie globale, la surface moléculaire, la polarisabilité et la densité de charge négative. Notre démarche d'étude des relations structure-activité (Structure-Activity Relationships, SAR) consistait à étudier des composés d'arôme appartenant à une gamme de structures variée, dans un même ensemble, puis en sous-groupes en fonction d'une particularité structurale donnée afin de révéler les particularités de la structure qui influent sur le phénomène rétention-libération. La comparaison des rétentions entre les milieux n'a pas montré l'existence d'un effet pectine. Les études des relations structure-activité ont montré l'impact de certaines particularités structurales telles que la ramification et la double liaison sur la rétention. Elles ont également montré que l'hydrophobie globale des molécules n'était pas la propriété moléculaire la plus à même d'expliquer les phénomènes impliqués dans les interactions de molécules odorantes avec les constituants du milieu (eau ou gel laitier). La surface et la polarisabilité rendent mieux compte des rétentions des composés d'arôme. Les corrélations impliquant la surface, la polarisabilité et l'hydrophobie globale, confirment que les interactions de type van der Waals (essentiellement Keesom et London) sont favorables à la rétention dans les gels laitiers et défavorables à la rétention dans l'eau. De même, les corrélations impliquant la densité de charge montrent que les interactions polaires sont favorables à la rétention dans l'eau. Notre choix de départ, qui consistait à faire varier la structure des composés d'arôme afin d'apprécier son effet sur le phénomène rétention-libération des composés d'arôme, s'est avéré concluant, et le groupe de 28 composés permet effectivement de mener une étude quantitative des relations structure-propriété. Cette démarche QSAR pourra se transposer à des systèmes alimentaires simples ou complexes.

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Luca, Aurélie de. "Espaces chimiques optimaux pour la recherche par similarité, la classification et la modélisation de réactions chimiques représentées par des graphes condensés de réactions." Thesis, Strasbourg, 2015. http://www.theses.fr/2015STRAF027.

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Cette thèse vise à développer une approche basée sur le concept de Graphe Condensé de Réaction (GCR) capable de (i) sélectionner un espace optimal de descripteurs séparant au mieux différentes classes de réactions, et (ii) de préparer de nouveaux descripteurs pour la modélisation « structure–réactivité ». Cette méthodologie a été appliquée à la recherche par similarité dans une base de données contenant 8 classes de réaction différentes; et à la cartographie de son espace chimique en utilisant des cartes de Kohonen et de cartes topographiques génératives. La seconde partie de la thèse porte sur le développement de modèles prédictifs pour le pKa et pour des conditions optimales pour différents types de réaction de Michael impliquant à la fois les descripteurs d’effet électronique et des descripteurs calculés sur les GCR
This thesis aims to develop an approach based on the Condensed Graph of Reaction (CGR) method able to (i) select an optimal descriptor space the best separating different reaction classes, and (ii) to prepare special descriptors to be used in obtaining predictive structure-reactivity models. This methodology has been applied to similarity search studies in a database containing 8 different reaction classes, and to visualization of its chemical space using Kohonen maps and Generative Topographic Mapping. Another part of the thesis concerns development of predictive models for pKa and for optimal conditions for different types of Michael reaction involving both CGR-based and Electronic Effect Descriptors

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Liu, Jiangping. "Prediction of Fluid Dielectric Constants." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2787.

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The dielectric constant or relative static permittivity of a material represents the capacitance of the material relative to a vacuum and is important in many industrial applications. Nevertheless, accurate experimental values are often unavailable and current prediction methods lack accuracy and are often unreliable. A new QSPR (quantitative structure-property relation) correlation of dielectric constant for pure organic chemicals is developed and tested. The average absolute percent error is expected to be less than 3% when applied to hydrocarbons and non-polar compounds and less than 18% when applied to polar compounds with dielectric constant values ranging from 1.0 to 50.0. A local composition model is developed for mixture dielectric constants based on the Nonrandom-Two-Liquid (NRTL) model commonly used for correlating activity coefficients in vapor-liquid equilibrium data regression. It is predictive in that no mixture dielectric constant data are used and there are no adjustable parameters. Predictions made on 16 binary and six ternary systems at various compositions and temperatures compare favorably to extant correlations data that require experimental values to fit an adjustable parameter in the mixing rule and are significantly improved over values predicted by Oster's equation that also has no adjustable parameters. In addition, molecular dynamics (MD) simulations provide an alternative to analytic relations. Results suggest that MD simulations require very accurate force field models, particularly with respect to the charge distribution within the molecules, to yield accurate pure chemical values of dielectric constant, but with the development of more accurate pure chemical force fields, it appears that mixture simulations of any number of components are likely possible. Using MD simulations, the impact of different portions of the force field on the calculated dielectric constant were examined. The results obtained suggest that rotational polarization arising from the permanent dipole moments makes the dominant contribution to dielectric constant. Changes in the dipole moment due to angle bending and bond stretching (distortion polarization) have less impact on dielectric constant than rotational polarization due to permanent dipole alignment, with angle bending being more significant than bond stretching.

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Lukowicz, Thomas. "Synergistic solubilisation of fragrances in binary surfactant systems and prediction of their EACN value with COSMO-RS." Thesis, Lille 1, 2015. http://www.theses.fr/2015LIL10090/document.

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Les solvo-surfactants appartiennent à une nouvelle classe de molécules amphiphiles qui présentent à la fois les propriétés de tensioactifs et de solvants. Ils sont en effet capables de former des agrégats et peuvent ainsi solubiliser des composés hydrophobes. De plus, ces molécules présentent une volatilité importante, ce qui les rend particulièrement intéressantes pour des applications où cette propriété est décisive, notamment au cours de la solubilisation aqueuse de parfums. Dans un système solvo-surfactant/huile/eau (SHE), le comportement de phase est fortement influencé par l'hydrophobicité de l'huile. Le nombre équivalent de carbones d'alcane (EACN) de différentes huiles polaires est ainsi étudié. La diminution de l'EACN en comparaison avec les n-alcanes est reliée à leur fonctionnalisation et elle est rationnalisée grâce au paramètre d'empilement effectif. Les EACN de 94 huiles différentes ont été utilisés dans une analyse de régression multilinéaire basée sur les sigma moments de COSMO-RS, dans le but d'établir un modèle QSPR capable de prédire l'EACN d'hydrocarbones. Enfin, l'influence synergique de tensioactifs ioniques sur un système SHE est déterminée avec plusieurs huiles d'EACN différents. Il est montré que le tensioactif ionique augmente fortement la température de stabilité du pseudo système ternaire de même que l'efficacité de solubilisation de l'huile. Cependant, cette efficacité atteint un maximum à un certain ratio molaire en tensioactif ionique car ce dernier empêche le système de s'inverser. Ainsi, une microémulsion bicontinue, connue pour solubiliser une grande quantité d'huile et d'eau, ne peut pas être formée
Solvo-surfactants are a relatively new class of amphiphiles, which exhibit properties of both, surfactants and solvents. They are able to form aggregates, wherein they can solubilise hydrophobic compounds. Furthermore they exhibit volatile characteristics, which make them interesting for applications where volatility is a key factor, such as aqueous fragrance solubilisations. In a solvo-surfactant/oil/water (SOW) system, the phase behaviour is strongly influenced by the hydrophobicity of the oil. Therefore the equivalent alkane carbon number (EACN) of several polar oils, such as dialkylethers, 2-alkanones, 1-chloroalkanes etc. was determined and the decrease in EACN with respect to n-alkanes was related to its functionalization, as well as rationalised with the effective packing parameter for each corresponding type of oil. The EACN of all 94 oils were then used in a multilinear regression analysis, based on COSMO-RS -moments, in order to establish a QSPR model, which is able to predict the EACN of any hydrocarbon oil. The influence of ionic surfactants was finally investigated in a SOW system, with various oils of different EACN. It was found that the ionic surfactant increases strongly the temperature stability of the (pseudo-)ternary system, as well as the efficiency to solubilise the oil. However the efficiency undergoes a maximum for a certain molar fraction of ionic surfactant, since the latter prevents the system to inverse. Thus a bicontinuous microemulsion cannot be formed, which is known to solubilise high amounts of oil and water

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Enot, David. "La modélisation moléculaire en tant qu'outil prédictif et de compréhension du comportement thermotrope et lyotrope de sucres. Approche QSPR de la lipophilie de 1,2-dithiole-3-thiones et 1,2-dithiole-3-ones." Rennes 1, 2001. http://www.theses.fr/2001REN10117.

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Le travail présenté dans ce mémoire concerne l'étude des propriétés agrégatives de sucres et de la lipophilie de composés en série dithiolique par modélisation moléculaire. Dans une première partie, nous avons mis en oeuvre les outils de la mécanique moléculaire pour relier le comportement thermotrope et lyotrope d'une famille de bolaamphiphiles à la structure propre de ces composés. Une première approche a été l'examen des données crsitallographiques existantes sur des analogues structuraux des bolaformes pour déterminer les raisons énergétiques et stériques sui orientent la formation des assemblages supramoléculaires. Dans un second temps, les méthodes de recherche conformationnelle et de dynamique moléculaire dans le vide et en solution ont été mises à profit pour analyser le comportement des molécules. Sur cette base, une tentative de liaison entre l'architecture des molécules et la nature des mésophases observées a été effectuée. Enfin, l'étude du comportement agrégatif de sucres amphiphiles a été réalisée suivant une approche QSPR

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Fourches, Denis. "Modèles multiples en QSAR/QSPR : Développement de nouvelles approches et leurs applications au design "in silico" de nouveaux extractants de métaux, aux propriétés ADMETox ainsi qu'à différentes activités biologiques de molécules organiques." Université Louis Pasteur (Strasbourg) (1971-2008), 2007. http://www.theses.fr/2007STR13119.

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Cette thèse est consacrée à l’amélioration des performances des modèles structure-propriété QSAR, grâce au développement de modèles multiples, basées sur les descripteurs fragmentaux, et à leurs applications à différents domaines de la chimie (complexation et extraction de métaux, propriétés ADMETox, activités biologiques). Dans une première partie, les principales notions et méthodes de la chemoinformatique sont récapitulées. Dans une seconde partie, la plateforme de logiciels ISIDA (In Silico Design and Data Analysis) est présentée. Lors de cette thèse, deux approches à modèles multiples ont été développées : la stratégie « Diviser pour Conquérir » et l’approche Stepwise k-Nearest Neighbors. Dans une troisième partie, les méthodes d’ISIDA ont été appliquées avec succès à la modélisation de différentes propriétés chimiques et biologiques. Les tests expérimentaux d’extractants de métaux conçus grâce aux calculs ont confirmé les performances d’ISIDA
This thesis work concerns the improvement of prediction performances of QSAR structureproperty models, using consensus modelling strategies based on fragment descriptors, and also, to their applications for « in silico » design of metal binders, ADMETox properties and different biological activities of organic compounds. In the first part, some important concepts and methodologies of chemoinformatics are described. In the second part, the ensemble of programs ISIDA (In Silico Design and Data Analysis) is introduced. During this thesis work, two consensus approaches have been suggested: the « Divide and Conquer » strategy and the Stepwise k- Nearest Neighbors approach. Applications of new strategies lead to significant improvement of predictions accuracy, compared to the conventional models. In the third part, all ISIDA methods have been successfully applied to model various chemical and biological properties. Experimentally proven predictions demonstrate the robustness of the methods

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