Дисертації з теми "In silico drug prediction"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся з топ-50 дисертацій для дослідження на тему "In silico drug prediction".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Переглядайте дисертації для різних дисциплін та оформлюйте правильно вашу бібліографію.
Ahlin, Gustav. "In vitro and in silico prediction of drug-drug interactions with transport proteins." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-107492.
Повний текст джерелаCarrió, Gaspar Pau 1982. "Development of advanced strategies for the prediction of toxicity endpoints in drug development." Doctoral thesis, Universitat Pompeu Fabra, 2015. http://hdl.handle.net/10803/328418.
Повний текст джерелаLa manca de seguretat és una de les raons principals per la qual els candidats a fàrmacs són descartats. La fase en què els possibles efectes tòxics són identificats és crítica: descartar un candidat en fase clínica implica la pèrdua d'anys d'esforços i enormes inversions econòmiques. Encara pitjor és identificar efectes tòxics un cop el fàrmac està comercialitzat, quan es poden haver produït greus efectes secundaris en pacients. Per aquestes raons hi ha la necessitat de desenvolupar mètodes capaços d'avaluar la seguretat dels candidats a fàrmacs en les primeres etapes. Entre aquests, els mètodes in silico tenen molts avantatges, com no requerir la disponibilitat del compost, no perdre cap quantitat en cas que ja s'hagi sintetitzat, ser ràpid, econòmic i no fer ús de l'experimentació amb animals. Per desgràcia, els mètodes de predicció in silico aplicats a criteris d'avaluació de toxicitat no produeixen els resultats adequats. Les raons són objecte de debat, però raons probables són la complexitat dels fenòmens biològics en estudi i la gran diversitat estructural els fàrmacs candidats, entre d'altres. L'objectiu d'aquesta tesi és millorar els mètodes de predicció in silico emprats en l’avaluació de criteris d'interès en el desenvolupament de fàrmacs amb especial èmfasi en els de toxicitat. Presentem una nova metodologia general anomenada ADAN (Applicability Domain Analysis) per avaluar la fiabilitat de les prediccions obtingudes amb mètodes in silico. A més, proposem una estratègia unificada de l’ús de mètodes de predicció in silico emprats en aquest camp; com alertes estructurals, read-across, QSAR local i global. La estratègia incorpora criteris racionals per la seva utilització. Els bons resultats obtinguts amb dades representatives confirmen la validesa de les metodologies.
Vildhede, Anna. "In vitro and in silico Predictions of Hepatic Transporter-Mediated Drug Clearance and Drug-Drug Interactions in vivo." Doctoral thesis, Uppsala universitet, Institutionen för farmaci, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-241376.
Повний текст джерелаBergström, Christel A. S. "Computational and Experimental Models for the Prediction of Intestinal Drug Solubility and Absorption." Doctoral thesis, Uppsala University, Department of Pharmacy, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3593.
Повний текст джерелаNew effective experimental techniques in medicinal chemistry and pharmacology have resulted in a vast increase in the number of pharmacologically interesting compounds. However, the number of new drugs undergoing clinical trial has not augmented at the same pace, which in part has been attributed to poor absorption of the compounds.
The main objective of this thesis was to investigate whether computer-based models devised from calculated molecular descriptors can be used to predict aqueous drug solubility, an important property influencing the absorption process. For this purpose, both experimental and computational studies were performed. A new small-scale shake flask method for experimental solubility determination of crystalline compounds was devised. This method was used to experimentally determine solubility values used for the computational model development and to investigate the pH-dependent solubility of drugs. In the computer-based studies, rapidly calculated molecular descriptors were used to predict aqueous solubility and the melting point, a solid state characteristic of importance for the solubility. To predict the absorption process, drug permeability across the intestinal epithelium was also modeled.
The results show that high quality solubility data of crystalline compounds can be obtained by the small-scale shake flask method in a microtiter plate format. The experimentally determined pH-dependent solubility profiles deviated largely from the profiles predicted by a traditionally used relationship, highlighting the risk of data extrapolation. The in silico solubility models identified the non-polar surface area and partitioned total surface areas as potential new molecular descriptors for solubility. General solubility models of high accuracy were obtained when combining the surface area descriptors with descriptors for electron distribution, connectivity, flexibility and polarity. The used descriptors proved to be related to the solvation of the molecule rather than to solid state properties. The surface area descriptors were also valid for permeability predictions, and the use of the solubility and permeability models in concert resulted in an excellent theoretical absorption classification. To summarize, the experimental and computational models devised in this thesis are improved absorption screening tools applicable to the lead optimization in the drug discovery process.
Carlert, Sara. "Investigation and Prediction of Small Intestinal Precipitation of Poorly Soluble Drugs : a Study Involving in silico, in vitro and in vivo Assessment." Doctoral thesis, Uppsala universitet, Institutionen för farmaci, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-178053.
Повний текст джерелаWeston, Anne. "In silico prediction of protein function." Thesis, King's College London (University of London), 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.412922.
Повний текст джерелаSandelin, Albin. "In silico prediction of CIS-regulatory elements /." Stockholm, 2004. http://diss.kib.ki.se/2004/91-7349-879-3/.
Повний текст джерелаCereto, Massagué Adrià. "Development of tools for in silico drug discovery." Doctoral thesis, Universitat Rovira i Virgili, 2017. http://hdl.handle.net/10803/454678.
Повний текст джерелаEl cribado virtual es un método quimioinformático que consiste en la criba de moléculas bioactivas de grandes bases de datos de moléculas pequeñas. Esto permite a los investigadores ahorrarse el coste de probar experimentalmente cientos o miles de compuestos candidatos, reduciéndolos hasta cantidades manejables. Para la validación de los métodos de cribado virtual hacen falta bibliotecas de moléculas señuelo. El software DecoyFinder fue desarrollado como aplicación gráfica de fácil uso para la construcción de bibliotecas de moléculas señuelo, y fue posteriormente ampliado con los hallazgos de investigación posterior sobre la construcción i rendimiento de bibliotecas de moléculas señuelo. El Protein Data Bank (PDB) es muy útil porque proporciona estructuras tridimensionales para complejos proteina-ligando, y por tanto, información sobre como interactúan. Para los métodos de cribado virtual que dependen de ellas, es extremadamente importante su fiabilidad. VHELIBS fue desarrollado como herramienta para inspeccionar e identificar, fácil e intuitivamente, las estructuras fiables del PDB, basándose en como de bueno es su encaje con sus correspondientes mapas de densidad electrónica. Mientras que el cribado virtual intenta encontrar nuevas moléculas bioactivas para determinadas dianas, el enfoque inverso también se utiliza: a partir de una molécula, buscar dianas donde presente actividad biológica no documentada. Este cribado inverso es conocido en inglés como “in silico target fishing”, o pesca de dianas “in silico”, y es especialmente útil en el ámbito de la reutilización de fármacos. Al comenzar esta tesis, no había ninguna plataforma de “target fishing” de libre acceso, y aunque durante los años se han desarrollado algunas, en todos los casos su predicción de bioactividad es cualitativa. Por eso se desarrolló una plataforma propia de “target fishing” de libre acceso, con la implementación de un nuevo método que proporciona la primera predicción cuantitativa de bioactividad para este tipo de plataforma.
Virtual screening is a cheminformatics method that consists of screening large small-molecule databases for bioactive molecules. This enables the researcher to avoid the cost of experimentally testing hundreds or thousands of compounds by reducing the number of candidate molecules to be tested to manageable numbers. For their validation, virtual screening approaches need decoy molecule libraries. DecoyFinder was developed as an easy to use graphical application for decoy library building, and later updated after some research into decoy library building and their performance when used for 2D similarity approaches. The Protein Data Bank (PDB) is very useful because it provides 3D structures for protein-ligand complexes and, therefore, information on how certain ligands bind and interact with their targets. For virtual screening apporaches relying on these structures, it is of the utmost importance that the data available on the PDB for the ligand and its binding site are reliable. VHELIBS was developed as a tool to easily and intuitively inspect and identify reliable PDB structures based on the goodness of fitting between ligands and binding sites and their corresponding electron density map. While virtual screening aims to find new bioactive molecules for certain targets, the opposite approach is also used: starting from a given molecule, to search for a biological target for which it presents previously undocumented bioactivity. This reverse screening is known as in silico or computational target fishing or reverse pharmacognosy, and it is specially useful for drug repurposing or repositioning. When this thesis was started, there were no freely available target fishing platforms, but some have been developed during the years. However, they are qualitative in the nature of their activity prediction, and thus we set out to develop a freely accessible target fishing web service implementing a novel method which provides the first quantitative activity prediction: Anglerfish.
Acoca, Stephane. "In silico methods in drug discovery and development." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=110376.
Повний текст джерелаLes méthodes the modélisation sont devenues un outil inestimable dans le processus de découverte et de développement de nouveaux médicaments. Au cours de cette thèse va être décrit le développement et l'application de méthodes utilisés à chaque stage de la découverte et du développement de produits pharmaceutiques. Le Chapitre 2 est un aperçu sur l'utilisation de méthodes computationnelles vers le développement de deux nouveaux inhibiteurs des protéines Bcl-2, Obatoclax et ABT-737, en développement pour le traitement du Cancer. L'étude propose certains mécanismes d'ABT-737 qui expliquent ca sélectivité envers les membres de la famille Bcl-2. De plus, nous proposons un mécanisme d'attachement pour Obatoclax qui conforme aux données expérimentales. Le Chapitre suivant adresse l'utilisation du dépistage virtuel pour l'identification de nouvelles molécules mère. La Ligase de l'Edition d'ARN du Trypanosoma brucei a été choisie comme cible pour le développement de traitements contre des infections dû au Trypanosome et C35 a été identifié comme nouvel inhibiteur de l'enzyme. En outre, notre recherche démontre que l'action de C35 s'étends a l'inhibition de plusieurs enzymes nécessaires pour le mécanisme d'édition de l'ARN en plus de compromettre l'intégrité du complexe multi-protéinique qui l'effectue. Le Chapitre suivant prends regard a l'utilisation de donnes dérivant de la spectrométrie de masse pour but d'accélérer la découverte de molécules bioactives venant de sources naturelles. Nous avons développé un algorithme qui analyse les données MS/MS pour but de dériver la formule moléculaire du composé. Le nouvel algorithme a obtenu un taux de succès s'élevant à 95% sur un ensemble test de 91 molécules. Le dernier Chapitre de la thèse explore l'utilisation de simulations de dynamique moléculaire pour générer en ensemble conformationel de protéines cible pour son utilisation dans le dépistage virtuel. Les ensembles conformationel ont étés généré pour une série test obtenu d'un répertoire attitré 'Directory for Useful Decoys'. Les résultats démontrent que les ensembles conformationel dérivés de la dynamique moléculaire ont apporté des améliorations remarquables sur deux des cibles testées dû à une capacité accrue de placement approprié des molécules dans un site qui est autrement très restreint. Le dernier Chapitre de cette thèse est une discussion générale sur le travail accomplie et une proposition sur la manière dont tous les éléments sont intégrer dans un protocole de découverte et de développement de produits pharmaceutiques.
Kundu, Kousik [Verfasser], and Rolf [Akademischer Betreuer] Backofen. "In Silico Prediction of Modular Domain-Peptide Interactions." Freiburg : Universität, 2015. http://d-nb.info/1115861883/34.
Повний текст джерелаDörr, Alexander [Verfasser]. "In Silico Approaches for Polypharmacological Drug Design / Alexander Dörr." München : Verlag Dr. Hut, 2018. http://d-nb.info/1156510287/34.
Повний текст джерелаDickson, Callum. "In silico modelling of membranes and drug membrane interactions." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/25070.
Повний текст джерелаVernikos, Georgios. "In silico prediction of genomic islands in microbial genomes." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612465.
Повний текст джерелаThovarai, Vishal. "In silico drug design of potential novel anti malarial agents /." Online version of thesis, 2009. http://hdl.handle.net/1850/8689.
Повний текст джерелаElisée, Eddy. "Towards in silico prediction of mutations related to antibiotic resistance." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS350.
Повний текст джерелаAntibiotic resistance is a global concern threatening worldwide health. Indeed, if we don't change our overconsumption of antibiotics, the current situation could worsen until a "post-antibiotic" era in which existing treatment would be ineffective against microbial infections. Despite the natural occurrence of antibiotic resistance, the misuse of antibiotics is speeding up the process. Furthermore, presence of multi-resistant pathogens negates the effect of modern treatments and usual surgeries (caesarean sections, organ transplantations...) might be riskier in the future, or even lethal. That's why, common guidelines have to be edicted by health authorities in order to control antibiotic use at every level of society, from individuals to healthcare industry including health professionals and agriculture sector. As for scientific research, new strategies have to be considered in order to limit the spread of antibiotic resistance. In that context, the presented thesis aimed at developing a protocol to predict, by free energy calculations, β-lactamase mutations which could promote the hydolysis of β-lactams antibiotics. In order to achieve that, we developed several methodological approaches including: (1) new parameters for zinc enzymes implemented in OPLS-AA force field and thereafter validated using molecular dynamics simulations of representative zinc-containing metalloenzymes, (2) a protocol to parameterize covalent ligands in order to analyze the dynamical behavior of some β-lactams in CMY-136, a novel β-lactamase recently characterized in our laboratory, and (3) a pmx-based free energy protocol. The latter was also assessed through several international blinded prediction challenges, and finally used to find out why carbamylation of the catalytic serine is not observed in certain OXA enzymes. Throughout this work, we made significant improvements in our protocol, and now everything is in place for an exhaustive prediction of possible mutations in β-lactamases
Hamad, Mustafa. "In-Silico Prediction of the Physical Performance of Pharmaceutical Crystals." Thesis, Curtin University, 2021. http://hdl.handle.net/20.500.11937/86652.
Повний текст джерелаElkaïm, Judith. "Drug design in silico : criblage virtuel de protéines à visée thérapeutique." Thesis, Bordeaux 1, 2011. http://www.theses.fr/2011BOR14444/document.
Повний текст джерелаThe process of drug discovery is long and tedious. Besides, it is relatively inefficient in terms of hit rate. The identification of candidates through experimental testing is expensive and requires extensive data on the mechanisms of the target protein in order to develop efficient assays. Virtual screening can considerably accelerate the process by quickly evaluating large databases of compounds and determining the most likely to bind to a target. Some success stories have emerged in the field over the last few years.The objectives of this work were first, to compare common tools and strategies for structure-based virtual screening, and second, to apply those tools to actual target proteins implied notably in carcinogenesis.In order to evaluate the docking and scoring programs available, the protein kinase GSK3 and a test set of known ligands were used as a model to perform methodological studies. In particular the influence of the flexibility of the protein was explored via relaxed structures of the receptor or the insertion of torsions on the side chains of residues located in the binding site. Studies concerning the automatic generation of 3D structures for the ligands and the use of consensus scoring also provided insights on the usability of these tools while performing a virtual screening.Virtual screening of the human protein Pontin, an ATPase implied in tumor cell growth for which no inhibitors were known, allowed the prioritization of compounds from commercial databases. These compounds were tested in an enzymatic assay via a collaboration, and led to the identification of four molecules capable of inhibiting the ATPase activity of Pontin. Additional screens of in-house oriented databases also provided at least one innovative inhibitor for this protein. On the contrary, a study of the human PLA2-X, a phospholipase that requires a Ca2+ atom to bind to its active site in order to catalyze the hydrolysis of its substrate, revealed the limits of our docking tools that could not handle the metal ion and the need for new tools
Boateng, Rita Afriyie. "In silico characterization of plasmodial transketolases as potential malaria drug target." Thesis, Rhodes University, 2018. http://hdl.handle.net/10962/63540.
Повний текст джерелаYella, Jaswanth. "Machine Learning-based Prediction and Characterization of Drug-drug Interactions." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin154399419112613.
Повний текст джерелаKastenmüller, Gabriele. "In silico prediction and comparison of metabolic capabilities in sequenced genomes /." München : Hut, 2009. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=018929163&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
Повний текст джерелаLin, Frank Po-Yen Centre for Health Informatics Faculty of Medicine UNSW. "In silico virulence prediction and virulence gene discovery of Streptococcus agalactiae." Awarded By:University of New South Wales. Centre for Health Informatics, 2009. http://handle.unsw.edu.au/1959.4/44382.
Повний текст джерелаGuest, Eleanor. "Assessment of algorithms for the prediction of metabolic drug-drug interactions." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/assessment-of-algorithms-for-the-prediction-of-metabolic-drugdrug-interactions(591ca23c-c75d-445e-a786-9b67689f9cd4).html.
Повний текст джерелаMelarkode, Vattekatte Akhila. "3D structures of camelid antibodies : in-silico analyses, prediction and their dynamics." Thesis, La Réunion, 2019. http://www.theses.fr/2019LARE0020.
Повний текст джерелаAntibodies are the new blockbusters for drug design. Camelid antibodies, namely VHH domains are projected to be the next generation therapeutics and diagnostics. To improve our knowledge on VHHs, it is essential to have a better view on the sequence, structure and dynamics relationships to improve their affinity and stability. In a first-step a deep analysis of the sequence and the structure of VHHs are done using classical approaches, but also a structural alphabet named Protein Blocks. In a second step, the sequence-structure relationship of these domains is assessed looking at diverse sequence and structures conservations. In a third step, the specificity and difficulty to obtain pertinent structural models are underlined using different approaches, showing unexpected results. Next, the largest scale Molecular dynamic simulations of VHH had been done and shown a large variety in the behaviours of VHH domains dynamics. Finally, the thesis is wrapped up listing significant conclusions from the above chapters and with future perspectives
Burt, Howard James. "In Vitro Assessment and Prediction of Time-Dependent CYP3A4 Drug-Drug Interactions." Thesis, University of Manchester, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.503677.
Повний текст джерелаYang, Jiansong. "Quantitative prediction of metabolic drug-drug interactions : in vitro - in vivo extrapolation." Thesis, University of Sheffield, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.422638.
Повний текст джерелаCochrane, Wolf. "In silico synthesis of analogous lead libraries for drug design by molecular enumeration." Diss., Pretoria : [s.n.], 2007. http://upetd.up.ac.za/thesis/available/etd-04212008-135220/.
Повний текст джерелаMontes, Matthieu. "Développement et applications de méthodes de drug-design et de criblage in silico." Paris 5, 2007. http://www.theses.fr/2007PA05P611.
Повний текст джерелаVirtual ligand screening methods based on the structure of the receptor are extensively used to facilitate the discovery of lead compounds. Using different docking/scoring packages, we optimized a hierarchical virtual ligand screening protocol developed in our lab. This new protocol has been validated on different targets with different binding site properties. This multi-step hierarchical protocol has been applied to two targets of therapeutic importance, namely, the dual specificity phosphatase CDC25 and the 20S proteasome. Using ADME-tox filtered compound collections processed in our lab, we identified several new active molecules on these two difficult targets. These promising micromolar inhibitors displaying novel and growable scaffolds can lead to new potential drugs for cancer treatment
López, del Río Ángela. "Data preprocessing and quality diagnosis in deep learning-based in silico bioactivity prediction." Doctoral thesis, Universitat Politècnica de Catalunya, 2021. http://hdl.handle.net/10803/672385.
Повний текст джерелаEl descubrimiento de fármacos es un proceso costoso en tiempo y recursos. Consiste en la identificación de una diana y la exploración de fármacos candidatos apropiados para ella. Las técnicas computacionales optimizan este proceso, ayudando a identificar las mejores moléculas candidatas mediante el modelado de sus interacciones con la diana. Estas técnicas están en constante mejora gracias al desarrollo de algoritmos, al incremento del poder computacional y al aumento de bases de datos moleculares públicas. Particularmente, el aprendizaje automático proporciona modelos predictivos de distintas propiedades bioquímicas. El deep learning (aprendizaje profundo) es una aproximación del aprendizaje automático basada en las redes neuronales multicapa. Durante los últimos diez años el deep learning ha superado a los modelos predictivos clásicos en la mayoría de dominios, incluído el descubrimiento de fármacos. Algunas de sus aplicaciones son la predicción de propiedades moleculares, la generación de nuevos compuestos, la predicción de la estructura secundaria de proteínas y la predicción de unión entre compuestos y dianas. Sin embargo, algunos estudios apuntan a que el rendimiento reportado por los modelos de deep learning de predicción de unión entre dianas y compuestos podría deberse más al sesgo de los datos que a su capacidad de generalización, dando más peso a la novedad que a la valoración crítica. Además, la flexibilidad del deep learning da pie a una falta de consenso en la representación de sus entradas, dificultando su comparación en un marco común. Los datos de bioactividad tienen una disponibilidad limitada debido a su coste y suelen estar desbalanceados, lo cual puede dificultar el proceso de aprendizaje del modelo. El diagnóstico de estos problemas no es sencillo porque los modelos de deep learning son considerados cajas negras. El objetivo de esta tesis es mejorar los modelos de deep learning para el descubrimiento computacional de fármacos, centrándose en la representación de la entrada, el control del sesgo de los datos, la corrección de su desbalance y el diagnóstico de los modelos. Primero, esta tesis evalúa el efecto de diferentes estrategias de validación en los modelos de clasificación de la unión diana-compuesto para encontrar las estimaciones de rendimiento más realistas. La estrategia basada en el agrupamiento de las moléculas demostró ser la más parecida a una validación prospectiva y por tanto, más consistente que la validación cruzada aleatoria (demasiado optimista) o que un conjunto de test externo proveniente de otra base de datos (demasiado pesimista). Segundo, esta tesis se centra en el relleno de las secuencias de entrada, utilizado para establecer una longitud común de las mismas. Este relleno consiste normalmente en añadir ceros al final de cada secuencia, sin una justificación formal detrás esta decisión. Aquí, se compararon estrategias de relleno novedosas y clásicas en una tarea de clasificación de enzimas. Los resultados mostraron que la posición del relleno tiene un efecto sobre el rendimiento de los modelos de aprendizaje profundo, por lo que se le debería dar más atención. Tercero, esta tesis estudia el efecto del desbalance de los datos en los modelos de clasificación de actividad diana-compuesto y su atenuación mediante técnicas de remuestreo. Se evaluó el rendimiento de un modelo para diferentes combinaciones de sobremuestreo de la clase minoritaria y agrupamiento de las moléculas. Los resultados demostraron que el agrupamiento de los datos, seguido por su remuestreo en los conjuntos de entrenamiento y validación, es la estrategia con mejor rendimiento. Por último, esta tesis proporciona una forma sistemática de diagnosticar modelos de deep learning, identificando los factores que rigen sus predicciones. Estos modelos lineales explicativos permitieron la toma de decisiones informadas y cuantitativas en cada uno
Enginyeria biomèdica
Piechota, Przemyslaw. "Development of in silico models for the prediction of toxicity incorporating ADME information." Thesis, Liverpool John Moores University, 2015. http://researchonline.ljmu.ac.uk/4554/.
Повний текст джерелаAldib, Iyas. "Rational drug design approach of the myeloperoxidase inhibition: From in silico to pharmacological activity." Doctoral thesis, Universite Libre de Bruxelles, 2016. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/241515.
Повний текст джерелаDoctorat en Sciences biomédicales et pharmaceutiques (Pharmacie)
info:eu-repo/semantics/nonPublished
Tooley, Adam. "Investigating in vitro methods to aid prediction of in vivo drug-drug interactions." Thesis, University of Sheffield, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.427244.
Повний текст джерелаNigsch, Florian. "Computational prediction of molecular properties for drug discovery." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611123.
Повний текст джерелаWu, Bolin. "PREDICTION OF DRUG INDICATION LIST BY MACHINE LEARNING." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447232.
Повний текст джерелаGraham, Helen Sarah. "Prediction of drug distribution in rat and human." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/prediction-of-drug-distribution-in-rat-and-human(231d1935-4fde-4b2d-8338-4e74091224f3).html.
Повний текст джерелаDurdagi, Serdar [Verfasser]. "In silico drug design studies of bioactive cannabinoid and [60]fullerene derivatives / Serdar Durdagi." Berlin : Freie Universität Berlin, 2009. http://d-nb.info/1023624370/34.
Повний текст джерелаFu, Wai. "In silico prediction of cis-regulatory elements of genes involved in hypoxic-ischaemic insult." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B36986896.
Повний текст джерелаFu, Wai, and 符慧. "In silico prediction of cis-regulatory elements of genes involved in hypoxic-ischaemic insult." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B36986896.
Повний текст джерелаBinti, Mohamad Zobir Siti Zuraidah. "Towards understanding mode-of-action of traditional medicines by using in silico target prediction." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/270866.
Повний текст джерелаRufino, Stephen Duarte. "Analysis, comparison and prediction of protein structure." Thesis, Birkbeck (University of London), 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.243648.
Повний текст джерелаYe, Zhu Yi Fan. "Deep learning for pharmaceutical formulation prediction." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3952123.
Повний текст джерелаDing, Zhenyu. "Molecular prediction of drug response using machine learning methods." Morgantown, W. Va. : [West Virginia University Libraries], 2008. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5544.
Повний текст джерелаTitle from document title page. Document formatted into pages; contains viii, 65 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 63-65).
Kilford, Peter John. "In vitro assessment and prediction of drug glucuronidation clearance." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.499830.
Повний текст джерелаYaddanapudi, Suryanarayana. "Machine Learning Based Drug-Disease Relationship Prediction and Characterization." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1565349706029458.
Повний текст джерелаAtreya, Ravi Viswanathan. "Drug target prediction in pancreatic cancer using model organisms." Thesis, The University of Arizona, 2009. http://hdl.handle.net/10150/192261.
Повний текст джерелаBakal, Mehmet. "Relation Prediction over Biomedical Knowledge Bases for Drug Repositioning." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/90.
Повний текст джерелаChang, Cheng. "In silico approaches for studying transporter and receptor structure-activity relationships." Connect to this title online, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1117553995.
Повний текст джерелаTitle from first page of PDF file. Document formatted into pages; contains xvii, 271 p.; also includes graphics. Includes bibliographical references (p. 245-269). Available online via OhioLINK's ETD Center
Mpangase, Phelelani Thokozani. "Integrating protein annotations for the in silico prioritization of putative drug target proteins in malaria." Diss., University of Pretoria, 2012. http://hdl.handle.net/2263/24715.
Повний текст джерелаDissertation (MSc)--University of Pretoria, 2012.
Biochemistry
unrestricted
Turpeinen, M. (Miia). "Cytochrome P450 enzymes—in vitro, in vivo, and in silico studies." Doctoral thesis, University of Oulu, 2006. http://urn.fi/urn:isbn:9514282205.
Повний текст джерелаSalentin, Sebastian. "In Silico Identification of Novel Cancer Drugs with 3D Interaction Profiling." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-226435.
Повний текст джерелаHobbs, Michael. "The interplay of multiple ADME mechanisms : prediction of hepatic drug-drug interactions from in vitro." Thesis, University of Strathclyde, 2016. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=27533.
Повний текст джерела