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Dissertationen zum Thema „QSAR Model“

Autor: Grafiati

Veröffentlicht am 18. Mai 2024

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1

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

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2

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

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3

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

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

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

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

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

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

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

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

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7

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

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

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

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

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

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

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

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

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Holm, Lotta. "The MHC-glycopeptide-T cell interaction in collagen induced arthritis : a study using glycopeptides, isosteres and statistical molecular design in a mouse model for rheumatoid arthritis." Doctoral thesis, Umeå : Department of Chemistry, Umeå University, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-899.

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12

Alwassil, Osama I. "Elaboration and Design of α7 nAChR Negative Allosteric Modulators". VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3902.

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α7 Neuronal nicotinic acetylcholine receptors are one of two major classes of receptors responsible for cholinergic neurotransmission in the central nervous system. The existence of α7 neuronal nAChRs in different regions of the nervous system suggests their involvement in certain essential physiological functions as well as in disorders such as Alzheimer’s disease (AD), drug dependence, and depression. This project was aimed toward the discovery and development of small–molecule arylguanidines that modulate α7 nAChR function with improved subtype-selectivity through an allosteric approach. Id

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Reddy, Badinehal Asrith. "COMMERCIALIZATION OF A QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP TOOL - SARCHITECT." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1295637833.

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CONCU, RICCARDO. "New QSAR models based on Markov Chains to predict protein functions." Doctoral thesis, Università degli Studi di Cagliari, 2010. http://hdl.handle.net/11584/266281.

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15

Peterson, Shane. "Improved CoMFA Modeling by Optimization of Settings : Toward the Design of Inhibitors of the HCV NS3 Protease." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8140.

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16

Zhong, Shifa. "Permanganate Reaction Kinetics and Mechanisms and Machine Learning Application in Oxidative Water Treatment." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1618686803768471.

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17

Hobocienski, Bryan Christopher. "Locality-Dependent Training and Descriptor Sets for QSAR Modeling." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1577716259011585.

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18

Simões, Rodolfo da Silva. "Técnicas de transferência de aprendizagem aplicadas a modelos QSAR para regressão." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-07062018-120939/.

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Para desenvolver um novo medicamento, pesquisadores devem analisar os alvos biológicos de uma dada doença, descobrir e desenvolver candidatos a fármacos para este alvo biológico, realizando em paralelo, testes em laboratório para validar a eficiência e os efeitos colaterais da substância química. O estudo quantitativo da relação estrutura-atividade (QSAR) envolve a construção de modelos de regressão que relacionam um conjunto de descritores de um composto químico e a sua atividade biológica com relação a um ou mais alvos no organismo. Os conjuntos de dados manipulados pelos pesquisadores para

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Martins, Ulisses Nicola. "Síntese e desenvolvimento de um modelo de QSAR para derivados do (-)-borneol contra larvas de Aedes aegypti." Universidade Federal de Sergipe, 2016. https://ri.ufs.br/handle/riufs/3945.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES<br>Aedes aegypti is the main transmitter of vector-borne diseases such, dengue, chikungunya and zika. Hence no vaccine exists as well as drugs to reduce viremia, major strategy to prevent these diseases is controlling vector spreading. The main larvicides used are organophosphates and pyrethroids, however the indiscriminate use of these compounds gave rise Ae. aegypti resistant strains. A viable alternative to classic insecticides / larvicides is the phytochemical research of molecules, like terpenoids, component of essential

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Geylan, Gökçe. "Training Machine Learning-based QSAR models with Conformal Prediction on Experimental Data from DNA-Encoded Chemical Libraries." Thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447354.

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DNA-encoded chemical libraries (DEL) allows an exhaustive chemical space sampling with a large-scale data consisting of compounds produced through combinatorial synthesis. This novel technology was utilized in the early drug discovery stages for robust hit identification and lead optimization. In this project, the aim was to build a Machine Learning- based QSAR model with conformal prediction for hit identification on two different target proteins, the DEL was assayed on. An initial investigation was conducted on a pilot project with 1000 compounds and the analyses and the conclusions drawn fr

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Bloyet, Nicolas. "Caractérisation et plongement de sous-graphes colorés : application à la construction de modèles structures à activité (QSAR)." Thesis, Lorient, 2019. http://www.theses.fr/2019LORIS546.

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Dans le domaine de la chimie, il est intéressant de pouvoir estimer des propriétés physico- chimiques de molécules, notamment pour des applications industrielles. Celles-ci sont difficiles à estimer par simulations physique, présentant une complexité temporelle prohibitive. L'émergence des données (publiques ou privées) ouvre toutefois de nouvelles perspectives pour le traitement de ces problèmes par des méthodes statistiques et d'apprentissage automatique. La principale difficulté réside dans la caractérisation des molécules : celles-ci s'apparentent davantage à un réseau d'atomes (autrement

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Wang, Fang. "Chlorine Contribution to Quantitative Structure and Activity Relationship Models of Disinfection By-Products' Quantum Chemical Descriptors and Toxicities." FIU Digital Commons, 2009. http://digitalcommons.fiu.edu/etd/174.

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Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: 1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbi

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Pedreros, Riquelme América Beatriz. "Diseño racional de antibacterianos de núcleo 8-aril- mercapto-pirimidoisoquinolinquinonas basado en las herramientas de química medicinal de gráfica de craig y modelo 3D-QSAR/COMFA." Tesis, Universidad de Chile, 2017. http://repositorio.uchile.cl/handle/2250/144766.

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Memoria para optar al título de Químico Farmacéutico<br>A lo largo del tiempo, los antibacterianos han sido ampliamente utilizados en el tratamiento y control de enfermedades infecciosas. Actualmente la lucha contra varias enfermedades de origen bacteriano se ha vuelto un problema de salud a nivel mundial, debido a la resistencia bacteriana. Sumado a lo anterior, la tendiente disminución en investigación y desarrollo de nuevos antibacterianos nos insta a destinar los esfuerzos en investigar nuevos compuestos activos, idealmente, frente a bacterias multirresistentes, con una estructura química

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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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Submitted by Edineia Teixeira (edineia.teixeira@unioeste.br) on 2017-09-04T17:30:26Z No. of bitstreams: 2 Carlos_Olguin2017.pdf: 2821259 bytes, checksum: 4f44c019ceff1c4613be9b0b525a188e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)<br>Made available in DSpace on 2017-09-04T17:30:26Z (GMT). No. of bitstreams: 2 Carlos_Olguin2017.pdf: 2821259 bytes, checksum: 4f44c019ceff1c4613be9b0b525a188e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-02-17<br>The soil sorption coefficient normalized for organic carbon c

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AbdulHameed, Mohamed Diwan Mohideen. "COMPUTATIONAL DESIGN OF 3-PHOSPHOINOSITIDE DEPENDENT KINASE-1 INHIBITORS AS POTENTIAL ANTI-CANCER AGENTS." UKnowledge, 2009. http://uknowledge.uky.edu/gradschool_diss/757.

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Computational drug design methods have great potential in drug discovery particularly in lead identification and lead optimization. 3-Phosphoinositide dependent kinase-1 (PDK1) is a protein kinase and a well validated anti-cancer target. Inhibitors of PDK1 have the potential to be developed as anti-cancer drugs. In this work, we have applied various novel computational drug design strategies to design and identify new PDK1 inhibitors with potential anti-cancer activity. We have pursued novel structure-based drug design strategies and identified a new binding mode for celecoxib and its derivati

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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

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Ruark, Christopher Daniel. "The Guinea Pig Model For Organophosphate Toxicology and Therapeutic Development." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1432890247.

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Cypcar, Christopher Charles. "Investigation of structure-property relationships of nylon 6-co-7 and linear alkyl model amide compounds and molecular modeling quantitative structure-property relationship (QSPR) for glass temperature predictions." Aix-Marseille 3, 1997. http://www.theses.fr/1997AIX30035.

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29

Michielan, Lisa. "Advance Methodologies in Linear and Nonlinear Quantitative Structure-Activity Relationships (QSARs): from Drug Design to In Silico Toxicology Applications." Doctoral thesis, Università degli studi di Padova, 2010. http://hdl.handle.net/11577/3422242.

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Novel computational strategies are continuously being demanded by the pharmaceutical industry to assist, improve and speed up the drug discovery process. In this scenario chemoinformatics provide reliable mathematical tools to derive quantitative structure-activity relationships (QSARs), able to describe the correlation between molecular descriptors and various experimental profiles of the compounds. In the last years, nonlinear machine learning approaches have demonstrated a noteworthy predictive capability in several QSAR applications, confirming their superiority over the traditional linear

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Schaal, Wesley. "Computational Studies of HIV-1 Protease Inhibitors." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2002. http://publications.uu.se/theses/91-554-5213-2/.

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Durán, Alcaide Ángel. "Development of high-performance algorithms for a new generation of versatile molecular descriptors. The Pentacle software." Doctoral thesis, Universitat Pompeu Fabra, 2010. http://hdl.handle.net/10803/7201.

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The work of this thesis was focused on the development of high-performance algorithms for a new generation of molecular descriptors, with many advantages with respect to its predecessors, suitable for diverse applications in the field of drug design, as well as its implementation in commercial grade scientific software (Pentacle). As a first step, we developed a new algorithm (AMANDA) for discretizing molecular interaction fields which allows extracting from them the most interesting regions in an efficient way. This algorithm was incorporated into a new generation of alignmentindependent mol

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Martínez, Brito Izacar Jesús. "Quantitative structure fate relationships for multimedia environmental analysis." Doctoral thesis, Universitat Rovira i Virgili, 2010. http://hdl.handle.net/10803/8590.

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Key physicochemical properties for a wide spectrum of chemical pollutants are unknown. This thesis analyses the prospect of assessing the environmental distribution of chemicals directly from supervised learning algorithms using molecular descriptors, rather than from multimedia environmental models (MEMs) using several physicochemical properties estimated from QSARs. Dimensionless compartmental mass ratios of 468 validation chemicals were compared, in logarithmic units, between: a) SimpleBox 3, a Level III MEM, propagating random property values within statistical distributions of widely reco

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Chen, Kuan-Ju, and 陳冠如. "Applying 3D-QSAR technique to construct the pharmacophore model of farnesyltransferase inhibitors." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/52165476443295609708.

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碩士<br>國立清華大學<br>分子醫學研究所<br>96<br>Abstract A set of 68 imidazole and cyanophenyl containing farnesyltransferase (FTase) inhibitors were subjected to three-dimensional quantitative structure-activity relationship (3D-QSAR) studies using the comparative molecular field analysis (CoMFA) , comparative molecular similarity indices analysis (CoMSIA), and a pharmacophore building method, the Catalyst program. The structures of these inhibitors were generated theoretically, and the conformations used in the 3D-QSAR studies were defined by docking them into the known structure of FTase binding pocket th

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Chi-Hung, Tsai. "Applying Support Vector Machines to Protein Disulfide Connectivity Prediction and QSAR Model Construction." 2006. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-1710200607485500.

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Tsai, Chi-Hung, and 蔡其杭. "Applying Support Vector Machines to Protein Disulfide Connectivity Prediction and QSAR Model Construction." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/97920504275014104395.

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博士<br>國立臺灣大學<br>資訊工程學研究所<br>95<br>Support Vector Machine (SVM) is widely adopted in the field of machine learning and pattern recognition, and recently the application of SVM techniques to bioinformatics is also very promising. In this dissertation, we applied SVM to two important issues in bioinformatics: protein disulfide connectivity prediction and quantitative-structure activity relationship (QSAR) model construction. For disulfide connectivity prediction, we implemented an algorithm which infers pair-wise bonding probability by SVM, and introduced a descriptor which derived from the seque

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Chang, Li-Jen, and 張立人. "Integrating GEMDOCK with GEMPLS and GEMkNN for QSAR model of huAChE and AGHO." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/26000984055629466054.

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碩士<br>國立交通大學<br>生物資訊研究所<br>93<br>Molecular docking and quantitative structure activity relationships (QSAR) are the core technologies in computer-aided drug design. These technologies would help to save much time and cost to find out potential leads for the target protein in drug discovery. In this study, we introduced molecular docking tool, GEMDOCK to generate the atom-based protein-ligand interaction profile. We utilized the interaction profile to be descriptor and integrate with GEMPLS and GEMkNN for QSAR model of human acetylcholinesterase (huAChE) and Arthrobacter globiformis histamine o

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Palczewska, Anna Maria, Daniel Neagu, and Mick J. Ridley. "Using Pareto points for model identification in predictive toxicology." Thesis, 2013. http://hdl.handle.net/10454/9709.

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no<br>Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, th

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Suško, Jurij V. [Verfasser]. "Applicability domain of QSAR models / Iurii Sushko." 2011. http://d-nb.info/1011026651/34.

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Poiares, João Pedro da Silva Gonçalves. "Development of a QSAR models for the prediction of plasma protein binding." Master's thesis, 2014. http://hdl.handle.net/10437/5858.

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Orientação: Paulo Paixão<br>One of the most important factors, affecting the pharmacokinetic profile of a drug is binding to plasma protein. As such, this study aimed at the development of a quantitative structure–activity relationship model, to predict the fraction unbound in plasma (fub) for four species, using artificial neural network ensemble (ANNE). To this end a database of 363 drugs was used, and molecular descriptors were determined. The dataset was divided in two groups, a train and an external validation, to avoid overfitting. The ANNE optimization reduced the descriptors required

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"Modelo QSAR de Compuestos Análogos a la Isoniazida y Estudio de la Enzima KatG." Tesis, Universidad de las Américas Puebla, 2005. http://catarina.udlap.mx/u_dl_a/tales/documentos/lqu/castellanos_u_a/.

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Pires, Cristiana Lages. "Permeability through Caco-2 cell monolayers as a model for BBB: implementation and preliminary evaluation using model compounds." Master's thesis, 2018. http://hdl.handle.net/10316/86489.

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Dissertação de Mestrado em Química Medicinal apresentada à Faculdade de Ciências e Tecnologia<br>In early stages of drug discovery, the capacity of a compound to cross biological membranes is a determining factor in the selection of drug candidates. This is very important in the case of orally administered drugs once they must be absorbed at the gastrointestinal tract in order to reach the target of interest. Compounds targeted to the central nervous system have also to overcome the blood-brain barrier (BBB), which imposes even more constraints on the drug properties.In general, passive permea

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Wagner, Steffen [Verfasser]. "Sesquiterpenlactone : neuronale Netze als QSAR-Modell sowie pharmakokinetische Untersuchungen am Beispiel von Arnica montana / vorgelegt von Steffen Wagner." 2006. http://d-nb.info/980669685/34.

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"Statistical contribution to the virtual multicriteria optimisation of combinatorial molecules libraries and to the validation and application of QSAR models." Université catholique de Louvain, 2008. http://edoc.bib.ucl.ac.be:81/ETD-db/collection/available/BelnUcetd-01032008-172816/.

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Palczewska, Anna Maria, X. Fu, Paul R. Trundle, et al. "Towards model governance in predictive toxicology." 2013. http://hdl.handle.net/10454/9708.

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no<br>Efficient management of toxicity information as an enterprise asset is increasingly important for the chemical, pharmaceutical, cosmetics and food industries. Many organisations focus on better information organisation and reuse, in an attempt to reduce the costs of testing and manufacturing in the product development phase. Toxicity information is extracted not only from toxicity data but also from predictive models. Accurate and appropriately shared models can bring a number of benefits if we are able to make effective use of existing expertise. Although usage of existing models may pr

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Santos, Rodrigo Daniel Garrilha. "Construction of machine learning models to predict pharmacology properties of molecules." Master's thesis, 2019. http://hdl.handle.net/10451/41459.

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Tese de mestrado, Bioinformática e Biologia Computacional, Universidade de Lisboa, Faculdade de Ciências, 2019<br>O processo de desenvolvimento de drogas é altamente condicionado pela qualidade dos modelos com os quais se realiza a seleção dos primeiros compostos. Este trabalho procurou avaliar vários metodologias e descobrir qual a melhor abordagem para a construção de modelos de QSAR (relação quantitativa estrutura-propriedade/atividade) usando um conjunto grande de problemas. Usando um banco de dados de modelação de problemas desenvolvidos no projeto de pesquisa MIMED, 500 conjunto de dados

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Zhang, K., M. H. Abraham, and Xiangli Liu. "An Equation for the Prediction of Human Skin Permeability of Neutral Molecules, Ions and Ionic Species." 2017. http://hdl.handle.net/10454/11486.

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yes<br>Experimental values of permeability coefficients, as log Kp, of chemical compounds across human skin were collected by carefully screening the literature, and adjusted to 37 °C for the effect of temperature. The values of log Kp for partially ionized acids and bases were separated into those for their neutral and ionic species, forming a total data set of 247 compounds and species (including 35 ionic species). The obtained log Kp values have been regressed against Abraham solute descriptors to yield a correlation equation with R2 = 0.866 and SD = 0.432 log units. The equation can provid

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