Relevant bibliographies by topics / Virtual docking

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Virtual docking'

Author: Grafiati
Published: 9 March 2023

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Journal articles on the topic "Virtual docking"

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Klauda, Jeffery B. "Virtual Issue on Docking." Journal of Physical Chemistry B 125, no. 21 (2021): 5455–57. http://dx.doi.org/10.1021/acs.jpcb.1c03303.

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Xie, Xin Hua, Jun Jie Zhou, Gang Zhao, and Wan Qing Li. "The Research on Virtual Prototype of Rod-Cone Docking Mechanism." Applied Mechanics and Materials 684 (October 2014): 354–57. http://dx.doi.org/10.4028/www.scientific.net/amm.684.354.

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The achievement of space orbit service space autonomous docking technology. At present, many kinds of autonomous docking mechanism are appeared according to different service object. In order to widen the application range from lives of docking mechanism, design a space rod - cone docking mechanism in this paper. The mechanism can be in the context of a larger space, achieve self-occupied docking. And based on the ADAMS-View to complete the mechanism dynamics virtual prototype. According to the space orbit autonomous docking technology, set up virtual prototype mainly docking detection parameters, Analysis of dynamic characteristics in the whole of the docking process and complete the mechanism.
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Tuccinardi, Tiziano. "Docking-Based Virtual Screening: Recent Developments." Combinatorial Chemistry & High Throughput Screening 12, no. 3 (2009): 303–14. http://dx.doi.org/10.2174/138620709787581666.

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Poli, Giulio, and Tiziano Tuccinardi. "Consensus Docking in Drug Discovery." Current Bioactive Compounds 16, no. 3 (2020): 182–90. http://dx.doi.org/10.2174/1573407214666181023114820.

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Background: Molecular docking is probably the most popular and profitable approach in computer-aided drug design, being the staple technique for predicting the binding mode of bioactive compounds and for performing receptor-based virtual screening studies. The growing attention received by docking, as well as the need for improving its reliability in pose prediction and virtual screening performance, has led to the development of a wide plethora of new docking algorithms and scoring functions. Nevertheless, it is unlikely to identify a single procedure outperforming the other ones in terms of reliability and accuracy or demonstrating to be generally suitable for all kinds of protein targets. Methods: In this context, consensus docking approaches are taking hold in computer-aided drug design. These computational protocols consist in docking ligands using multiple docking methods and then comparing the binding poses predicted for the same ligand by the different methods. This analysis is usually carried out calculating the root-mean-square deviation among the different docking results obtained for each ligand, in order to identify the number of docking methods producing the same binding pose. Results: The consensus docking approaches demonstrated to improve the quality of docking and virtual screening results compared to the single docking methods. From a qualitative point of view, the improvement in pose prediction accuracy was obtained by prioritizing ligand binding poses produced by a high number of docking methods, whereas with regards to virtual screening studies, high hit rates were obtained by prioritizing the compounds showing a high level of pose consensus. Conclusion: In this review, we provide an overview of the results obtained from the performance assessment of various consensus docking protocols and we illustrate successful case studies where consensus docking has been applied in virtual screening studies.
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Krüger, Jens, Richard Grunzke, Sonja Herres-Pawlis, et al. "Performance Studies on Distributed Virtual Screening." BioMed Research International 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/624024.

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Virtual high-throughput screening (vHTS) is an invaluable method in modern drug discovery. It permits screening large datasets or databases of chemical structures for those structures binding possibly to a drug target. Virtual screening is typically performed by docking code, which often runs sequentially. Processing of huge vHTS datasets can be parallelized by chunking the data because individual docking runs are independent of each other. The goal of this work is to find an optimal splitting maximizing the speedup while considering overhead and available cores on Distributed Computing Infrastructures (DCIs). We have conducted thorough performance studies accounting not only for the runtime of the docking itself, but also for structure preparation. Performance studies were conducted via the workflow-enabled science gateway MoSGrid (Molecular Simulation Grid). As input we used benchmark datasets for protein kinases. Our performance studies show that docking workflows can be made to scale almost linearly up to 500 concurrent processes distributed even over large DCIs, thus accelerating vHTS campaigns significantly.
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Blanes-Mira, Clara, Pilar Fernández-Aguado, Jorge de Andrés-López, Asia Fernández-Carvajal, Antonio Ferrer-Montiel, and Gregorio Fernández-Ballester. "Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening." Molecules 28, no. 1 (2022): 175. http://dx.doi.org/10.3390/molecules28010175.

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The rapid advances of 3D techniques for the structural determination of proteins and the development of numerous computational methods and strategies have led to identifying highly active compounds in computer drug design. Molecular docking is a method widely used in high-throughput virtual screening campaigns to filter potential ligands targeted to proteins. A great variety of docking programs are currently available, which differ in the algorithms and approaches used to predict the binding mode and the affinity of the ligand. All programs heavily rely on scoring functions to accurately predict ligand binding affinity, and despite differences in performance, none of these docking programs is preferable to the others. To overcome this problem, consensus scoring methods improve the outcome of virtual screening by averaging the rank or score of individual molecules obtained from different docking programs. The successful application of consensus docking in high-throughput virtual screening highlights the need to optimize the predictive power of molecular docking methods.
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Wallach, Izhar, and Ryan Lilien. "Virtual Decoy Sets for Molecular Docking Benchmarks." Journal of Chemical Information and Modeling 51, no. 2 (2011): 196–202. http://dx.doi.org/10.1021/ci100374f.

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Schneider, Gisbert, and Hans-Joachim Böhm. "Virtual screening and fast automated docking methods." Drug Discovery Today 7, no. 1 (2002): 64–70. http://dx.doi.org/10.1016/s1359-6446(01)02091-8.

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Schneider, Gisbert, and Hans-Joachim Böhm. "Virtual screening and fast automated docking methods." Drug Discovery Today 7 (January 2002): 64–70. http://dx.doi.org/10.1016/s1359-6446(02)00004-1.

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Ellingson, Sally R., and Jerome Baudry. "High-throughput virtual molecular docking with AutoDockCloud." Concurrency and Computation: Practice and Experience 26, no. 4 (2012): 907–16. http://dx.doi.org/10.1002/cpe.2926.

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Dissertations / Theses on the topic "Virtual docking"

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Totrov, Maxim. "Computational studies on protein-ligand docking." Thesis, Open University, 1999. http://oro.open.ac.uk/58005/.

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This thesis describes the development and refinement of a number of techniques for molecular docking and ligand database screening, as well as the application of these techniques to predict the structures of several protein-ligand complexes and to discover novel ligands of an important receptor protein. Global energy optimisation by Monte-Carlo minimisation in internal co-ordinates was used to predict bound conformations of eight protein-ligand complexes. Experimental X-ray crystallography structures became available after the predictions were made. Comparison with the X-ray structures showed that the docking procedure placed 30 to 70% of the ligand molecule correctly within 1.5A from the native structure. The discrimination potential for identification of high-affinity ligands was derived and optimised using a large set of available protein-ligand complex structures. A fast boundary-element solvation electrostatic calculation algorithm was implemented to evaluate the solvation component of the discrimination potential. An accelerated docking procedure utilising pre-calculated grid potentials was developed and tested. For 23 receptors and 63 ligands extracted from X-ray structures, the docking and discrimination protocol was capable of correct identification of the majority of native receptor-ligand couples. 51 complexes with known structures were predicted. 35 predictions were within 3A from the native structure, giving correct overall positioning of the ligand, and 26 were within 2A, reproducing a detailed picture of the receptor-ligand interaction. Docking and ligand discrimination potential evaluation was applied to screen the database of more than 150000 commercially available compounds for binding to the fibroblast growth factor receptor tyrosine kinase, the protein implicated in several pathological cell growth aberrations. As expected, a number of compounds selected by the screening protocol turned out to be known inhibitors of the tyrosine kinases. 49 putative novel ligands identified by the screening protocol were experimentally tested and five compounds have shown inhibition of phosphorylation activity of the kinase. These compounds can be used as leads for further drug development.
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Lewis, Stephanie N. "Refinement of the Docking Component of Virtual Screening for PPAR." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/23675.

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Exploration of peroxisome proliferator-activated receptor-gamma (PPAR") as a drug target holds applications for treating a wide variety of chronic inflammation-related diseases. Type 2 diabetes (T2D), which is a metabolic disease influenced by chronic inflammation, is quickly reaching epidemic proportions. Although some treatments are available to control T2D, more efficacious compounds with fewer side effects are in great demand. Drugs targeting PPAR" typically are compounds that function as agonists toward this receptor, which means they bind to and activate the protein. Identifying compounds that bind to PPAR" (i.e. binders) using computational docking methods has proven difficult given the large binding cavity of the protein, which yields a large target area and variations in ligand positions within the binding site. We applied a combined computational and experimental concept for characterizing PPAR" and identifying binders. The goal was to establish a time- and cost-effective way to screen a large, diverse compound database potentially containing natural and synthetic compounds for PPAR" agonists that are more efficacious and safer than currently available T2D treatments. The computational molecular modeling methods used include molecular docking, molecular dynamics, steered molecular dynamics, and structure- and ligand-based pharmacophore modeling. Potential binders identified in the computational component funnel into wet-lab experiments to confirm binding, assess activation, and test preclinical efficacy in a mouse model for T2D and other chronic inflammation diseases. The initial process used provided "-eleostearic acid as a compound that ameliorates inflammatory bowel disease in a pre-clinical trial. Incorporating pharmacophore analyses and binding interaction information improved the method for use with a diverse ligand database of thousands of compounds. The adjusted methods showed enrichment for full agonist binder identification. Identifying lead compounds using our method would be an efficient means of addressing the need for alternative T2D treatments.
Ph. D.
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AraÃjo, Sergio Xavier Barbosa. "Virtual screeningÂde possÃveis inibidores daÂtrans-enoil-ACP-redutase deÂMycobacterium tuberculosis." Universidade Federal do CearÃ, 2013. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=10971.

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nÃo hÃ
A tuberculose à uma das principais causas de mortalidade no mundo, porÃm à uma doenÃa negligenciada por ser endÃmica de paÃses em desenvolvimento. Um dos principais pontos de tratamento da tuberculose à a morte do bacilo causador, o Mycobacterium tuberculosis, atravÃs da interrupÃÃo da produÃÃo de Ãcidos micÃlicos, componentes da parede celular do bacilo, usando como um dos alvos a enzima InhA, porÃm esta rota tambÃm à a principal causa de resistÃncia. O presente trabalho se propÃe a estudar a enzima InhA, realizando modelagens in silico de interaÃÃes entre a enzima e ligantes selecionados. Os ligantes estudados fazem parte de duas bibliotecas distintas, sendo uma de compostos orgÃnicos selecionados por sua similaridade com o substrato da enzima. A outra biblioteca à composta de complexos metÃlicos com o nÃcleo pentacianoferrato, variando-se o ligante auxiliar. A justificativa para esta classe de compostos ser utilizada se dà pelo fato de o complexo pentacianoisoniazidaferato (II) ter apresentado atividade anti-tuberculose tanto in vitro como por via oral em ratos. Os ensaios de docking foram realizados utilizando-se duas abordagens, uma completamente rÃgida e outra em que a proteÃna era rÃgida e o ligante era flexÃvel. Ambos os ensaios apresentaram boa correlaÃÃo entre os seus resultados, independentemente da funÃÃo de avaliaÃÃo utilizada. Observou-se que as melhores estruturas em termos de inibiÃÃo possuÃam uma quantidade razoÃvel de interaÃÃes hidrofÃbicas, de modo a manterem-se estÃveis no sÃtio de ligaÃÃo da enzima que possui baixa polaridade.
Tuberculosis is found among the main causes of mortality in the World, although is a neglected disease since it is endemic in developing countries. The main route of therapy of tuberculosis is the inhibition of InhA, enzyme that catalyses the production of mycolic acids, which is a component of bacillus cellular wall. This reaction also is the main point of resistance against TB drugs. In this work proposed the study of InhA enzyme, working specifically in silico modeling of enzyme-ligant interactions. These ligands distinguish themselves between two distinct libraries, one of them containing organic compounds selected by its structural similarity with the enzyme substrate, NADH. Due in vitro and orally activity in murine model against tuberculosis exhibited by the compound pentacianoisoniazideferrate (II), another library, containing the pentacianoferrate II moiety bind to an auxiliary ligand studied against que InhA target. The essays realized using ligand rigid and flexible docking both, although the protein always considered rigid. Both essays had acceptable correlation within its results, regardless the scoring function used. The leading inhibitors structures had in common a high stabilization of ligand-enzyme complex due hydrophobic interactions, something expected due polarity of the enzyme binding site
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Bologna, Fabio <1992&gt. "Development of reverse docking protocols for virtual screening in nanomedicine." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amsdottorato.unibo.it/9932/1/bologna_fabio_tesi.pdf.

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One of the first computational chemistry tools that have been used in pharmaceutical research is molecular docking, which infers the interaction between two molecules, usually a small active compound and a receptor, on the basis of their 3D structures. During my three years of PhD studies I worked mainly on re-purposing molecular docking tools to investigate the interactions between a single molecule of interest and a collection of proteins of pharmacological interest. Since this process is essentially the inverse of what is usually done in pharmaceutical research, the technique is called reverse screening. Reverse screening can help in the identification of new therapeutical targets, in predicting toxicological effects and unwanted interactions or in the design of new therapeutic platforms based on the conjugation between a synthetic compound and a protein carrier. In this work, reverse screening has been applied to porphine and phthalocyanine, two chemically related photosensitizers employed in photodynamic therapy, and Gd@C82, the most promising endohedral gadofullerene for theranostic applications. For each of these two types of molecule, a reverse docking protocols has been designed and has allowed the discovery of new potential pharmaceutical targets and carrier systems that can help overcome the physico-chemical limitations to their widespread usage in nanomedicine. Finally, the application of reverse screening protocols to study the interactions between small nanoparticles (d < 5 nm) and 2D materials against bio-molecules was explored. Although theoretically possible, the sheer number of atoms that must be considered and the nanoparticle size result in a plethora of problems since docking tools have been designed with small active molecules in mind. Nevertheless, a new protocol has been devised to perform reverse screening of gold, silver and silica nanoparticles and 2D materials and it has been successfully tested on a small number of proteins.
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Elkaïm, Judith. "Drug design in silico : criblage virtuel de protéines à visée thérapeutique." Thesis, Bordeaux 1, 2011. http://www.theses.fr/2011BOR14444/document.

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Les processus qui mènent à la découverte de nouveaux médicaments sont longs et fastidieux, et les taux de succès sont relativement faibles. L’identification de candidats par le biais de tests expérimentaux s’avère coûteuse, et nécessite de connaître en profondeur les mécanismes d'action de la protéine visée afin de mettre en place des essais efficaces. Le criblage virtuel peut considérablement accélérer ces processus en permettant une évaluation rapide de chimiothèques de plusieurs milliers de molécules afin de déterminer lesquelles sont les plus susceptibles de se lier à une cible. Ces dernières années ont ainsi été témoins de quelques success stories dans ce domaine.Le premier objectif de ce travail était de comparer différents outils et stratégies couramment utilisés dans le criblage virtuel “structure-based”, puis de les appliquer à des cibles protéiques à visée thérapeutique, en particulier dans le cadre du cancer.La protéine kinase GSK3 et un test set de ligands connus ont servi de modèle pour différentes études méthodologiques ayant pour but d’évaluer les programmes de docking et de scoring à notre disposition. En particulier, l’utilisation de plusieurs structures relaxées du récepteur ou l’insertion de torsions sur certains résidus du site actif pendant le docking ont permis d’évaluer l’influence de la flexibilité de la protéine. L’utilité et la pertinence d’outils permettant de générer automatiquement les structures 3D des ligands et de méthodes de consensus scoring ont également été étudiées.Un criblage virtuel de la Pontine, une ATPase impliquée dans la croissance tumorale pour laquelle aucun inhibiteur n’était connu, a permis la sélection de candidats issus de banques de données commerciales. Ces molécules ont été testées dans un essai enzymatique par le biais d’une collaboration, et quatre d’entre elles se sont révélées capable d’inhiber l’activité ATPase de la Pontine. Le criblage de bases de ligands synthétisés et imaginés dans l’équipe a également fourni un inhibiteur original. Au contraire, l’étude de la sPLA2-X humaine, une phospholipase dont l’activité catalytique est dépendante d’un atome de Ca2+ localisé au sein du site actif, a montré les limites de nos outils de docking qui n’ont pas été capables de gérer cet ion métallique et mis en évidence la nécessité de mettre en place d’autres outils
The 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
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Lantz, Mikael. "A targeted evaluation of OpenEye’s methods for virtual ligand screens and docking." Thesis, University of Skövde, School of Humanities and Informatics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-959.

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The process of drug discovery is very slow and expensive. There is a need for reliable in silico methods; however the performance of these methods differs.

This work presents a targeted study on how the drug discovery methods used in OpenEye’s tools ROCS, EON and FRED perform on targets with small ligands. It was examined if 12 compounds (markers) somewhat similar to AMP could be detected by ROCS in a random data set comprised of 1000 compounds. It was also examined if EON could find any electrostatic similarities between the queries and the markers. The performance of FRED with respect to re-generation of bound ligand modes was examined on ten different protein/ligand complexes from the Brookhaven Protein Data Bank. It was also examined if FRED is suitable as a screening tool since several other docking methods are used in such a way. Finally it was also examined if it was possible to reduce the time requirements of ROCS when running multiconformer queries by using a combination of single conformer queries coupled with multiconformer queries.

The conclusions that could be drawn from this project were that FRED is not a good screening tool, but ROCS performs well as such. It was also found that the scoring functions are the weak spots of FRED. EON is probably very sensitive to the conformers used but can in some cases strengthen the results from ROCS. A novel and simple way to reduce the time complexity with multiconformer queries to ROCS was discovered and was shown to work well.

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Buonfiglio, Rosa <1985&gt. "Computational strategies to include protein flexibility in Ligand Docking and Virtual Screening." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amsdottorato.unibo.it/6330/1/Tesi_Buonfiglio.pdf.

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The dynamic character of proteins strongly influences biomolecular recognition mechanisms. With the development of the main models of ligand recognition (lock-and-key, induced fit, conformational selection theories), the role of protein plasticity has become increasingly relevant. In particular, major structural changes concerning large deviations of protein backbones, and slight movements such as side chain rotations are now carefully considered in drug discovery and development. It is of great interest to identify multiple protein conformations as preliminary step in a screening campaign. Protein flexibility has been widely investigated, in terms of both local and global motions, in two diverse biological systems. On one side, Replica Exchange Molecular Dynamics has been exploited as enhanced sampling method to collect multiple conformations of Lactate Dehydrogenase A (LDHA), an emerging anticancer target. The aim of this project was the development of an Ensemble-based Virtual Screening protocol, in order to find novel potent inhibitors. On the other side, a preliminary study concerning the local flexibility of Opioid Receptors has been carried out through ALiBERO approach, an iterative method based on Elastic Network-Normal Mode Analysis and Monte Carlo sampling. Comparison of the Virtual Screening performances by using single or multiple conformations confirmed that the inclusion of protein flexibility in screening protocols has a positive effect on the probability to early recognize novel or known active compounds.
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Buonfiglio, Rosa <1985&gt. "Computational strategies to include protein flexibility in Ligand Docking and Virtual Screening." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amsdottorato.unibo.it/6330/.

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The dynamic character of proteins strongly influences biomolecular recognition mechanisms. With the development of the main models of ligand recognition (lock-and-key, induced fit, conformational selection theories), the role of protein plasticity has become increasingly relevant. In particular, major structural changes concerning large deviations of protein backbones, and slight movements such as side chain rotations are now carefully considered in drug discovery and development. It is of great interest to identify multiple protein conformations as preliminary step in a screening campaign. Protein flexibility has been widely investigated, in terms of both local and global motions, in two diverse biological systems. On one side, Replica Exchange Molecular Dynamics has been exploited as enhanced sampling method to collect multiple conformations of Lactate Dehydrogenase A (LDHA), an emerging anticancer target. The aim of this project was the development of an Ensemble-based Virtual Screening protocol, in order to find novel potent inhibitors. On the other side, a preliminary study concerning the local flexibility of Opioid Receptors has been carried out through ALiBERO approach, an iterative method based on Elastic Network-Normal Mode Analysis and Monte Carlo sampling. Comparison of the Virtual Screening performances by using single or multiple conformations confirmed that the inclusion of protein flexibility in screening protocols has a positive effect on the probability to early recognize novel or known active compounds.
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Tunca, Guzin. "A virtual screening procedure combining pharmacophore filtering and molecular docking with the LIE method." Doctoral thesis, Universitat Autònoma de Barcelona, 2012. http://hdl.handle.net/10803/284031.

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Actualment, el cribratge virtual juga un paper central en el món del descobriment de fàrmacs. L’anàlisi in silico permet el cribatge de milions de molècules petites i la tria de les més prometedores per a les proves experimentals. Per trobar candidats que puguin esdevenir fàrmacs, és crucial reunir una sèrie d’eines computacionals individuals i complementàries. En aquesta tesi, es descriu un procediment automatitzat de cribatge virtual que combina el modelat de farmacòfors i el seu ús en cerques, mètodes d’alt rendiment d’acoblament molecular, puntuació de consens i estimació d'energia lliure d'unió mitjançant el mètode d’energia d'interacció lineal (LIE) a partir de simulacions de dinàmica molecular. Un dels objectius d'aquesta tesi ha estat el de construir una metodologia flexible i versàtil de cribratge virtual, que permeti la integració de diferents eines en les diferents etapes de l’estudi. El procediment, que es va iniciar com la combinació d'un senzill filtre per tamany, la simulació de l’acoblament molecular i una puntuació de consens, ha derivat en un procediment computacional elaborat i automatitzat amb l'addició de cerques basades en farmacòfor i l'estimació de l'energia lliure d'unió mitjançant el mètode LIE. Aquest mètode integrat té l’objectiu de compensar les debilitats individuals de les diferents tècniques usades i permet avaluar i comparar el rendiment i la l’exactitud d'aquestes tècniques. Una altra fita important ha estat l'aplicació del procediment computacional a proteïnes diana concretes per tal d’avaluar-ne la capacitat de trobar molècules que puguin ser candidats a fàrmacs. Tests experimentals realitzats per a la β-Glucosidasa àcida i la hidrolasa de Bleomicina humanes indiquen que diverses molècules petites seleccionades pel procediment computacional tenen activitat inhibitòria micromolar. El mètode LIE emprat en aquest treball es va aplicar sobre més de deu mil complexos proteïna-lligand per a tres proteïnes diana diferents, el que és, al nostre entendre, la primera aplicació del mètode LIE a aquesta escala.
Virtual screening plays a central role in the world of drug discovery today. In silico testing allows to screen millions of small molecules and to choose only the most promising ones for experimental testing. To find potential drug candidates, it is crucial to bring together individual and complementary computational tools. In this thesis, I describe an automated virtual screening procedure that combines pharmacophore modeling and searches, high-throughput molecular docking, consensus scoring and binding free energy estimation with the linear interaction energy (LIE) method through molecular dynamics simulations. One goal of this thesis was to build an evolving and versatile virtual screening methodology, which enables integration of different tools at different steps. The procedure that started as a combination of a simple size filter, molecular docking and consensus scoring, advanced into an elaborate and automated computational workflow with the addition of pharmacophore searches and binding free energy estimation with LIE. This integrated method intends to compensate for weaknesses of individual structure-based techniques and allows the evaluation and comparison of the performance and accuracy of these techniques. Another important goal was to apply the computational workflow to target proteins and find hits that could be drug candidates. Experimental testing performed for human acid β-Glucosidase and bleomycin hydrolase indicate that several small molecules selected by the computational workflow display micromolar inhibitory activity. The standard LIE method used in this work was applied to more than ten thousand ligand-protein complexes for three different targets, which is, to our knowledge, the first time application of LIE at such large scale.
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Tai, Hio Kuan. "Protein-ligand docking and virtual screening based on chaos-embedded particle swarm optimization algorithm." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3948431.

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Books on the topic "Virtual docking"

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Caballero, Julio. Virtual Screening and Drug Docking. Elsevier Science & Technology Books, 2022.

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Caballero, Julio. Virtual Screening and Drug Docking. Elsevier Science & Technology, 2022.

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Book chapters on the topic "Virtual docking"

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Bitencourt-Ferreira, Gabriela, and Walter Filgueira de Azevedo. "Molegro Virtual Docker for Docking." In Methods in Molecular Biology. Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9752-7_10.

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Rognan, Didier. "Virtual screening by molecular docking." In Chemogenomics and Chemical Genetics. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19615-7_16.

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Martínez-Medina, Mauricio, Miguel González-Mendoza, and Oscar Herrera-Alcántara. "Jeffrey Divergence Applied to Docking Virtual." In Advances in Soft Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02837-4_26.

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Morris, Garrett M. "Chapter 7. Docking and Virtual Screening." In Drug Discovery. Royal Society of Chemistry, 2012. http://dx.doi.org/10.1039/9781849735377-00171.

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Karthikeyan, Muthukumarasamy, and Renu Vyas. "Docking and Pharmacophore Modelling for Virtual Screening." In Practical Chemoinformatics. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1780-0_4.

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Kontoyianni, Maria. "Docking and Virtual Screening in Drug Discovery." In Methods in Molecular Biology. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7201-2_18.

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dos Santos, Ricardo N., Leonardo G. Ferreira, and Adriano D. Andricopulo. "Practices in Molecular Docking and Structure-Based Virtual Screening." In Methods in Molecular Biology. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7756-7_3.

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Rognan, Didier. "Docking Methods for Virtual Screening: Principles and Recent Advances." In Methods and Principles in Medicinal Chemistry. Wiley-VCH Verlag GmbH & Co. KGaA, 2011. http://dx.doi.org/10.1002/9783527633326.ch6.

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Beuming, Thijs, Bart Lenselink, Daniele Pala, Fiona McRobb, Matt Repasky, and Woody Sherman. "Docking and Virtual Screening Strategies for GPCR Drug Discovery." In Methods in Molecular Biology. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2914-6_17.

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Beuscher IV, Albert E., and Arthur J. Olson. "Chapter 14. Iterative Docking Strategies for Virtual Ligand Screening." In Computational and Structural Approaches to Drug Discovery. Royal Society of Chemistry, 2007. http://dx.doi.org/10.1039/9781847557964-00242.

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Conference papers on the topic "Virtual docking"

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Ellingson, Sally R., and Jerome Baudry. "High-throughput virtual molecular docking." In the second international workshop. ACM Press, 2011. http://dx.doi.org/10.1145/1996023.1996028.

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Ellingson, Sally R., Sivanesan Dakshanamurthy, Milton Brown, Jeremy C. Smith, and Jerome Baudry. "Accelerating virtual high-throughput ligand docking." In the 3rd international workshop. ACM Press, 2012. http://dx.doi.org/10.1145/2483954.2483961.

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Liu, Zijing, Xianbin Ye, Xiaoming Fang, Fan Wang, Hua Wu, and Haifeng Wang. "Docking-based Virtual Screening with Multi-Task Learning." In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2021. http://dx.doi.org/10.1109/bibm52615.2021.9669513.

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Stenholt, Rasmus, and Claus B. Madsen. "Shaping 3-D boxes: A full 9 degree-of-freedom docking experiment." In 2011 IEEE Virtual Reality (VR). IEEE, 2011. http://dx.doi.org/10.1109/vr.2011.5759445.

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Li, Yan, Dong Joon Kim, Ann M. Bode, and Zigang Dong. "Abstract 2904: Docking-based virtual screening of Chk1 inhibitors." In Proceedings: AACR 102nd Annual Meeting 2011‐‐ Apr 2‐6, 2011; Orlando, FL. American Association for Cancer Research, 2011. http://dx.doi.org/10.1158/1538-7445.am2011-2904.

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Li, Hongjian, Kwong-Sak Leung, and Man-Hon Wong. "idock: A multithreaded virtual screening tool for flexible ligand docking." In 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2012. http://dx.doi.org/10.1109/cibcb.2012.6217214.

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Xiuli Lu, Shuchao Chen, Yong Zhang, Fangtong Li, Xiaohui Sun, and Bing Gao. "Study of expanded application of molecular docking on virtual screening." In 2011 International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE). IEEE, 2011. http://dx.doi.org/10.1109/rsete.2011.5966261.

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Cavasotto, Claudio N. "Ligand Docking and Virtual Screening in Structure-based Drug Discovery." In FROM PHYSICS TO BIOLOGY: The Interface between Experiment and Computation - BIFI 2006 II International Congress. AIP, 2006. http://dx.doi.org/10.1063/1.2345621.

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Yu, Yangtao, Ye Dai, FaMing Pan, and Long Ma. "Virtual prototyping simulation of minitype universal docking mechanism based on ADAMS." In Mechanical Engineering and Information Technology (EMEIT). IEEE, 2011. http://dx.doi.org/10.1109/emeit.2011.6023834.

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Steed, Anthony, Sebastian Friston, Vijay Pawar, and David Swapp. "Docking Haptics: Dynamic Combinations Of Grounded And Worn Devices." In 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). IEEE, 2020. http://dx.doi.org/10.1109/vrw50115.2020.00164.

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