Global ETD Search

11	Computational Method for Drug Target Search and Application in Drug Discovery Chen, Yuzong, Li, Zerong, Ung, C.Y. 01 1900 (has links) Ligand-protein inverse docking has recently been introduced as a computer method for identification of potential protein targets of a drug. A protein structure database is searched to find proteins to which a drug can bind or weakly bind. Examples of potential applications of this method in facilitating drug discovery include: (1) identification of unknown and secondary therapeutic targets of a drug, (2) prediction of potential toxicity and side effect of an investigative drug, and (3) probing molecular mechanism of bioactive herbal compounds such as those extracted from plants used in traditional medicines. This method and recent results on its applications in solving various drug discovery problems are reviewed. / Singapore-MIT Alliance (SMA) drug binding computer-aided drug design ligand-protein interactions molecular modeling adverse drug reactions therapeutic effects medicinal plant drug natural product
12	Computer-Assisted Carbohydrate Structural Studies and Drug Discovery Lundborg, Magnus January 2011 (has links) Carbohydrates are abundant in nature and have functions ranging from energy storage to acting as structural components. Analysis of carbohydrate structures is important and can be used for, for instance, clinical diagnosis of diseases as well as in bacterial studies. The complexity of glycans makes it difficult to determine their structures. NMR spectroscopy is an advanced method that can be used to examine carbohydrates at the atomic level, but full assignments of the signals require much work. Reliable automation of this process would be of great help. Herein studies of Escherichia coli O-antigen polysaccharides are presented, both a structure determination by NMR and also research on glycosyltransferases which assemble the polysaccharides. The computer program CASPER has been improved to assist in carbohydrate studies and in the long run make it possible to automatically determine structures based only on NMR data. Detailed computer studies of glycans can shed light on their interactions with proteins and help find inhibitors to prevent unwanted binding. The WaaG glycosyltransferase is important for the formation of E. coli lipopolysaccharides. Molecular docking analyses of structures confirmed to bind this enzyme have provided information on how inhibitors could be composed. Noroviruses cause gastroenteritis, such as the winter vomiting disease, after binding human histo-blood group antigens. In one of the projects, fragment-based docking, followed by molecular dynamics simulations and binding free energy calculations, was used to find competitive binders to the P domain of the capsid of the norovirus VA387. These novel structures have high affinity and are a very good starting point for developing drugs against noroviruses. The protein targets in these two projects are carbohydrate binding, but the techniques are general and can be applied to other research projects. / At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 4: Submitted. Paper 5: Manuscript. Paper 6. Manuscript. Carbohydrates molecular docking molecular dynamics simulation fragment-based virtual screening NMR spectroscopy computer-aided drug design computer-aided structure elucidation glycosyltransferases Escherichia coli Organic chemistry Organisk kemi
13	Ranking And Classification of Chemical Structures for Drug Discovery : Development of Fragment Descriptors And Interpolation Scheme Kandel, Durga Datta January 2013 (has links) (PDF) Deciphering the activity of chemical molecules against a pathogenic organism is an essential task in drug discovery process. Virtual screening, in which few plausible molecules are selected from a large set for further processing using computational methods, has become an integral part and complements the expensive and time-consuming in vivo and in vitro experiments. To this end, it is essential to extract certain features from molecules which in the one hand are relevant to the biological activity under consideration, and on the other are suitable for designing fast and robust algorithms. The features/representations are derived either from physicochemical properties or their structures in numerical form and are known as descriptors. In this work we develop two new molecular-fragment descriptors based on the critical analysis of existing descriptors. This development is primarily guided by the notion of coding degeneracy, and the ordering induced by the descriptor on the fragments. One of these descriptors is derived based on the simple graph representation of the molecule, and attempts to encode topological feature or the connectivity pattern in a hierarchical way without discriminating atom or bond types. Second descriptor extends the first one by weighing the atoms (vertices) in consideration with the bonding pattern, valence state and type of the atom. Further, the usefulness of these indices is tested by ranking and classifying molecules in two previously studied large heterogeneous data sets with regard to their anti-tubercular and other bacterial activity. This is achieved by developing a scoring function based on clustering using these new descriptors. Clusters are obtained by ordering the descriptors of training set molecules, and identifying the regions which are (almost) exclusively coming from active/inactive molecules. To test the activity of a new molecule, overlap of its descriptors in those cluster (interpolation) is weighted. Our results are found to be superior compared to previous studies: we obtained better classification performance by using only structural information while previous studies used both structural features and some physicochemical parameters. This makes our model simple, more interpretable and less vulnerable to statistical problems like chance correlation and over fitting. With focus on predictive modeling, we have carried out rigorous statistical validation. New descriptors utilize primarily the topological information in a hierarchical way. This can have significant implications in the design of new bioactive molecules (inverse QSAR, combinatorial library design) which is plagued by combinatorial explosion due to use of large number of descriptors. While the combinatorial generation of molecules with desirable properties is still a problem to be satisfactorily solved, our model has potential to reduce the number of degrees of freedom, thereby reducing the complexity. Drug Discovery Molecular-Fragment Descriptors Chemical Molecules Interpolation Interpolation Chemical Structures Chemical Molecules Computer-aided Drug Discovery Small Molecules Fragment Descriptors computer-aided Drug Design Medicinal Chemistry
14	Applications of Deep Neural Networks in Computer-Aided Drug Design Ahmadreza Ghanbarpour Ghouchani (10137641) 01 March 2021 (has links) <div>Deep neural networks (DNNs) have gained tremendous attention over the recent years due to their outstanding performance in solving many problems in different fields of science and technology. Currently, this field is of interest to many researchers and growing rapidly. The ability of DNNs to learn new concepts with minimal instructions facilitates applying current DNN-based methods to new problems. Here in this dissertation, three methods based on DNNs are discussed, tackling different problems in the field of computer-aided drug design.</div><div><br></div><div>The first method described addresses the problem of prediction of hydration properties from 3D structures of proteins without requiring molecular dynamics simulations. Water plays a major role in protein-ligand interactions and identifying (de)solvation contributions of water molecules can assist drug design. Two different model architectures are presented for the prediction the hydration information of proteins. The performance of the methods are compared with other conventional methods and experimental data. In addition, their applications in ligand optimization and pose prediction is shown.</div><div><br></div><div>The design of de novo molecules has always been of interest in the field of drug discovery. The second method describes a generative model that learns to derive features from protein sequences to design de novo compounds. We show how the model can be used to generate molecules similar to the known for the targets the model have not seen before and compare with benchmark generative models.</div><div><br></div><div>Finally, it is demonstrated how DNNs can learn to predict secondary structure propensity values derived from NMR ensembles. Secondary structure propensities are important in identifying flexible regions in proteins. Protein flexibility has a major role in drug-protein binding, and identifying such regions can assist in development of methods for ligand binding prediction. The prediction performance of the method is shown for several proteins with two or more known secondary structure conformations.</div> Computational Biology Cheminformatics Computational Chemistry Structural Biology Computer-Aided Drug Design Deep Neural Network (DNN) protein hydration sites De novo drug design Protein secondary structure prediction machine learning
15	Applications of Cheminformatics for the Analysis of Proteolysis Targeting Chimeras and the Development of Natural Product Computational Target Fishing Models Cockroft, Nicholas T. January 2019 (has links) No description available. Pharmacy Sciences Chemistry Computer Science Molecules Molecular Chemistry Cheminformatics Drug Discovery Drug Design Medicinal Chemistry PROTAC Computer-Aided Drug Design Computational Target Fishing Natural Products Machine Learning
16	Modeling and Analysis of Ligand Docking to Norovirus Capsid Protein for the Computer-Aided Drug Design CHHABRA, MONICA 28 August 2008 (has links) No description available. Bioinformatics Biomedical Research Computer Science Molecules Virology norovirus docking norwalk virus va387 nlv virtual high throughput screening computer aided drug design antiviral drug drug design gastroenteritis cadd
17	Modelización molecular de los receptores de adenosina y sus ligandos en el marco de diseño de fármacos asistido por ordenador Gutiérrez de Terán Castañón, Hugo 03 May 2004 (has links) El objetivo de la presente tesis es el de aportar conocimiento sobre la bioquímica y la farmacología de los receptores de adenosina, así como entender las relaciones entre estructura química y actividad farmacológica de los ligandos existentes para estos receptores. Con este objetivo se han empleado distintas técnicas y metodologías del diseño de fármacos asistido por ordenador. Los resultados presentados en este trabajo incluyen:· El desarrollo de una estrategia original para la selección de una muestra que cubra adecuadamente la diversidad molecular existente en una base de datos de compuestos químicos· La construcción de un modelo de la región transmembrana del receptor A1 humano de adenosina, en el que se ha localizado y caracterizado un sitio de unión de agonistas compatible con los datos experimentales.· Predicciones teóricas de las energías de unión de ligandos, realizadas a partir de los complejos agonista-receptor predichos sobre el modelo mencionado, obteniendo un grado de acuerdo con los datos experimentales que resulta esperanzador / The goal of the present thesis is to gain knowledge about the biochemistry and pharmacology of adenosine receptors, as well as to understand structure-activity relationships for the existing ligands for this receptors. In order to achieve this goal, we have used several techniques and methodologies from the computer-aided drug design field. Results presented in this work include:· The development of an original strategy of selection of a maximum diversity sample that adequately covers the original molecular diversity contained in a compound database· The building of the transmembrane region of a human A1 adenosine receptor model. In such a model, an agonists binding site has been located and characterized, showing agreement with experimental data.· The resulting ligand-receptor complexes have been studied with computational approaches for the prediction of ligand-binding free energies. A nice correlation with experimental results was observed modelización molecular exploración de acoplamiento de ligandos receptores acoplados a proteína G computer-aided drug design docking exploration G protein-coupled receptors molecular diversity adenosine molecular modeling diversidad molecular adenosina 577
18	Homology modeling and structural analysis of the antipsychotic drugs receptorome López Muñoz, Laura 22 June 2010 (has links) Classically it was assumed that the compounds with therapeutic effect exert their action interacting with a single receptor. Nowadays it is widely recognized that the pharmacological effect of most drugs is more complex and involves a set of receptors, some associated to their positive effects and some others to the side effects and toxicity. Antipsychotic drugs are an example of effective compounds characterized by a complex pharmacological profile binding to several receptors (mainly G protein-coupled-receptors, GPCR). In this work we will present a detailed study of known antipsychotic drugs and the receptors potentially involved in their binding profile, in order to understand the molecular mechanisms of the antipsychotic pharmacologic effects.The study started with obtaining homology models for all the receptors putatively involved in the antipsychotic drugs receptorome, suitable for building consistent drug-receptor complexes. These complexes were structurally analyzed and compared using multivariate statistical methods, which in turn allowed the identification of the relationship between the pharmacological properties of the antipsychotic drugs and the structural differences in the receptor targets. The results can be exploited for the design of safer and more effective antipsychotic drugs with an optimum binding profile. / Tradicionalmente se asumía que los fármacos terapéuticamente efectivos actuaban interaccionando con un único receptor. Actualmente está ampliamente reconocido que el efecto farmacológico de la mayoría de los fármacos es más complejo y abarca a un conjunto de receptores, algunos asociados a los efectos terapéuticos y otros a los secundarios y toxicidad. Los fármacos antipsicóticos son un ejemplo de compuestos eficaces que se caracterizan por unirse a varios receptores simultáneamente (principalmente a receptores unidos a proteína G, GPCR). El trabajo de la presente tesis se ha centrado en el estudio de los mecanismos moleculares que determinan el perfil de afinidad de unión por múltiples receptores de los fármacos antipsicóticos.En primer lugar se construyeron modelos de homología para todos los receptores potencialmente implicados en la actividad farmacológica de dichos fármacos, usando una metodología adecuada para construir complejos fármaco-receptor consistentes. La estructura de estos complejos fue analizada y se llevó a cabo una comparación mediante métodos estadísticos multivariantes, que permitió la identificación de asociaciones entre la actividad farmacológica de los fármacos antipsicóticos y diferencias estructurales de los receptores diana. Los resultados obtenidos tienen interés para ser explotados en el diseño de fármacos antipsicóticos con un perfil farmacológico óptimo, más seguros y eficaces. Receptor 5-HT2A Receptor D3 Receptor D2 Ligando Clozapina Derivados Benzofuranonas Risperidona Olanzapina esquizofrenia métodos de regresión campos de interacción Molecular (MIF) Perfil Multireceptorial modelado por homología Docking sitio de unión interacciones moleculares selectividad afinidad de unión Diana Meta-Diana efectos secundarios fármaco antipsicótico típico fármaco antipsicótico atípico agonista antagonista membrana receptoroma rhodopsina estructura de rayos X descriptores moleculares farmacología análisis multivariante procedimiento integrado Computer-aided drug design Integrated approach Multivariate analysis Pharmacology Molecular descriptors X-ray structure Rhodopsin Receptorome Membrane Antagonist Agonist Atypical antipsychotic drug Typical antipsychotic drug Side effects Off-target Target Selectivity Binding affinity Molecular interactions Binding site Homology modeling Docking Multireceptor profile Molecular interaction Field (MIF) Partial Least Squares (PLS) Principal Component Analysis (PCA) Schizophrenia Benzolactam Derivatives Benzofuranone Derivatives Risperidone Olanzapine Clozapine Ligand Adrenergic receptors Muscarinic receptors Histamine receptors Serotonin receptors Dopamine receptors beta2-Adrenergic receptor D2 receptor D3 receptor 5-HT2A receptor G protein-coupled receptors (GPCR) 57

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