Relevant bibliographies by topics / Virtual screening; Drug discovery

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

Academic literature on the topic 'Virtual screening; Drug discovery'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Virtual screening; Drug discovery"

1

Stumpfe, Dagmar, Peter Ripphausen, and Jürgen Bajorath. "Virtual compound screening in drug discovery." Future Medicinal Chemistry 4, no. 5 (2012): 593–602. http://dx.doi.org/10.4155/fmc.12.19.

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McInnes, Campbell. "Virtual screening strategies in drug discovery." Current Opinion in Chemical Biology 11, no. 5 (2007): 494–502. http://dx.doi.org/10.1016/j.cbpa.2007.08.033.

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Degliesposti, Gianluca, Corinne Portioli, Marco Daniele Parenti, and Giulio Rastelli. "BEAR, a Novel Virtual Screening Methodology for Drug Discovery." Journal of Biomolecular Screening 16, no. 1 (2010): 129–33. http://dx.doi.org/10.1177/1087057110388276.

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BEAR (binding estimation after refinement) is a new virtual screening technology based on the conformational refinement of docking poses through molecular dynamics and prediction of binding free energies using accurate scoring functions. Here, the authors report the results of an extensive benchmark of the BEAR performance in identifying a smaller subset of known inhibitors seeded in a large (1.5 million) database of compounds. BEAR performance proved strikingly better if compared with standard docking screening methods. The validations performed so far showed that BEAR is a reliable tool for
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PHAM, Quan Minh, and Long Quoc PHAM. "VIRTUAL SCREENING STATEGIES IN DRUG DISCOVERY – A BRIEF OVERVIEW." Vietnam Journal of Science and Technology 59, no. 4 (2021): 415. http://dx.doi.org/10.15625/2525-2518/59/4/16003.

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Computer-aided drug design has now become a compulsory tool in the drug discovery and development process which uses computational approaches to discover potential compounds with expected biological activities. Firstly, this review provides a comprehensive introduction of the virtual screening technique, knowledge and advances in both SBVS and LBVS strategies also presented. Secondly, recent database of compounds provided worldwide and drug-like parameters which are helpful in supporting the VS process will be discussed. These information will provides a good platform to estimate the advance o
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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
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A. Srinivas Reddy, S. Priyadarshini Pati, P. Praveen Kumar, H.N. Pradeep, and G. Narahari Sastry. "Virtual Screening in Drug Discovery - A Computational Perspective." Current Protein & Peptide Science 8, no. 4 (2007): 329–51. http://dx.doi.org/10.2174/138920307781369427.

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Liu, Lei, and Ralf Jockers. "Structure-Based Virtual Screening Accelerates GPCR Drug Discovery." Trends in Pharmacological Sciences 41, no. 6 (2020): 382–84. http://dx.doi.org/10.1016/j.tips.2020.04.001.

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Green, Darren VS. "Virtual screening of chemical libraries for drug discovery." Expert Opinion on Drug Discovery 3, no. 9 (2008): 1011–26. http://dx.doi.org/10.1517/17460441.3.9.1011.

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Leung, Chung-Hang, and Dik-Lung Ma. "Recent advances in virtual screening for drug discovery." Methods 71 (January 2015): 1–3. http://dx.doi.org/10.1016/j.ymeth.2014.12.012.

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Cerqueira, Nuno M. F. S. A., Diana Gesto, Eduardo F. Oliveira, et al. "Receptor-based virtual screening protocol for drug discovery." Archives of Biochemistry and Biophysics 582 (September 2015): 56–67. http://dx.doi.org/10.1016/j.abb.2015.05.011.

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Dissertations / Theses on the topic "Virtual screening; Drug discovery"

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Ebejer, Jean-Paul. "Data driven approaches to improve the drug discovery process : a virtual screening quest in drug discovery." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:96d73300-f767-4ed6-8dda-a13a4aeb40e0.

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Drug discovery has witnessed an increase in the application of in silico methods to complement existing in vitro and in vivo experiments, in an attempt to 'fail fast' and reduce the high attrition rates of clinical phases. Computer algorithms have been successfully employed for many tasks including biological target selection, hit identification, lead optimization, binding affinity determination, ADME and toxicity prediction, side-effect prediction, drug repurposing, and, in general, to direct experimental work. This thesis describes a multifaceted approach to virtual screening, to computation
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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
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Evans, Matthew Darold. "Drug candidate discovery by high-throughput virtual screening of protein binding sites /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2006. http://uclibs.org/PID/11984.

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Reynolds, Jonathan James. "Structure-based drug discovery against a novel antimalarial drug target, S-adenosylmethionine decarboxylase/ornithine decarboxylase." Diss., University of Pretoria, 2012. http://hdl.handle.net/2263/27172.

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Malaria is one of the most life-threatening diseases affecting mankind, with over 3 billion people being at risk of infection, with most of these people living in Africa, South America and Asia. As the malaria parasite is rapidly becoming resistant to many of the possible treatments on the market, it is of upmost importance to identify new possible drug targets and describe drugs against these that are inexpensive, easy to manufacture and have a long shelf-life in order to combat malaria. One such target is the polyamine pathway. The polyamines putrescine, spermidine, and spermine are crucial
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Pevzner, Yuri. "Development and application of web-based open source drug discovery platforms." Scholar Commons, 2015. https://scholarcommons.usf.edu/etd/5550.

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Computational modeling approaches have lately been earning their place as viable tools in drug discovery. Research efforts more often include computational component and the usage of the scientific software is commonplace at more stages of the drug discovery pipeline. However, as software takes on more responsibility and the computational methods grow more involved, the gap grows between research entities that have the means to maintain the necessary computational infrastructure and those that lack the technical expertise or financial means to obtain and include computational component in thei
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Lindh, Martin. "Computational Modelling in Drug Discovery : Application of Structure-Based Drug Design, Conformal Prediction and Evaluation of Virtual Screening." Doctoral thesis, Uppsala universitet, Avdelningen för organisk farmaceutisk kemi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-328505.

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Structure-based drug design and virtual screening are areas of computational medicinal chemistry that use 3D models of target proteins. It is important to develop better methods in this field with the aim of increasing the speed and quality of early stage drug discovery. The first part of this thesis focuses on the application of structure-based drug design in the search for inhibitors for the protein 1-deoxy-D-xylulose-5-phosphate reductoisomerase (DXR), one of the enzymes in the DOXP/MEP synthetic pathway. This pathway is found in many bacteria (such as Mycobacterium tuberculosis) and in the
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Cereto, Massagué Adrià. "Development of tools for in silico drug discovery." Doctoral thesis, Universitat Rovira i Virgili, 2017. http://hdl.handle.net/10803/454678.

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El cribratge virtual és un mètode quimioinformàtic que consisteix en cribrar molècules bioactives de grans bases de dades de molècules petites. Això permet als investigadors d’estalviar-se el cost de provar experimentalment cents o milers de compostos candidats, reduïnt-ne el nombre fins a quantitats manejables. Per a la validació dels mètodes de cribratge virtual calen biblioteques de molècules cimbell. El programari DecoyFinder fou desenvolupat com a aplicació gràfica de fàcil ús per a la construcció de biblioteques de molècules cimbell, i fou posteriorment ampliat amb les troballes de recer
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Jacobsson, Micael. "Structure-Based Virtual Screening : New Methods and Applications in Infectious Diseases." Doctoral thesis, Uppsala universitet, Avdelningen för organisk farmaceutisk kemi, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-9302.

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A drug discovery project typically starts with a pharmacological hypothesis: that the modulation of a specific molecular biological mechanism would be beneficial in the treatment of the targeted disease. In a small-molecule project, the next step is to identify hits, i.e. molecules that can effect this modulation. These hits are subsequently expanded into hit series, which are optimised with respect to pharmacodynamic and pharmacokinetic properties, through medicinal chemistry. Finally, a drug candidate is clinically developed into a new drug. This thesis concerns the use of structure-based vi
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Thorman, Alexander W. "Rational Design of Novel BCL2A1 Inhibitors for Treatment of Autoimmune Diseases: An Integration of Virtual Screening, Transcriptomics and Protein Biophysics." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1543580409766192.

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Chee, Xavier. "Rational development of new inhibitors of lipoteichoic acid synthase." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/269766.

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Staphyloccocus aureus is an opportunisitic pathogen that causes soft skin and tissue infections (SSTI) such as endocarditis, osteomyelitis and meningitis. In recent years, the re-emergence of antibiotic-resistant S. aureus such as MRSA presents a formidable challenge for infection management worldwide. Amidst this global epidemic of antimicrobial resistance, several research efforts have turned their focus towards exploiting the cell-wall biosynthesis pathway for novel anti-bacterial targets. Recently, the lipoteichoic acid (LTA) biosynthesis pathway has emerged as a potential anti-bacterial t
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Books on the topic "Virtual screening; Drug discovery"

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Virtual screening: Principles, challenges, and practical guidelines. Wiley-VCH, 2011.

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Taha, Mutasem Omar. Virtual screening. Intech, 2012.

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Microarray methods for drug discovery. Springer, 2010.

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Rankovic, Zoran. Lead generation approaches in drug discovery. John Wiley, 2010.

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Lead generation approaches in drug discovery. John Wiley, 2010.

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Mylonakis, Eleftherios, and George Tegos. Antimicrobial drug discovery: Emerging strategies. CABI, 2012.

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Spilker, Bert. Multinational drug companies: Issues in drug discovery and development. Raven Press, 1989.

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Gardner, Jack. Early stage drug discovery 2004: Combi-chem and high-throughput screening revisited. Edited by Heffner Steven and Kalorama Information LLC. Kalorama Information, 2004.

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Structural genomics and drug discovery: Methods and protocols. Humana Press, 2014.

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Will, Yvonne. Drug discovery toxicology: From target assessment to translational biomarkers. John Wiley & Sons, Inc., 2016.

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Book chapters on the topic "Virtual screening; Drug discovery"

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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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Binkowski, T. Andrew, Wei Jiang, Benoit Roux, Wayne F. Anderson, and Andrzej Joachimiak. "Virtual High-Throughput Ligand Screening." In Structural Genomics and Drug Discovery. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0354-2_19.

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Cerqueira, Nuno M. F. S. A., Sérgio F. Sousa, Pedro A. Fernandes, and Maria João Ramos. "Virtual Screening of Compound Libraries." In Ligand-Macromolecular Interactions in Drug Discovery. Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60761-244-5_4.

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Costanzi, Stefano. "Chapter 18. Structure-based Virtual Screening for Ligands of G Protein-coupled Receptors." In Drug Discovery. Royal Society of Chemistry, 2011. http://dx.doi.org/10.1039/9781849733441-00359.

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Stumpfe, Dagmar, and Jürgen Bajorath. "CRITICAL ASSESSMENT OF VIRTUAL SCREENING FOR HIT IDENTIFICATION." In Chemoinformatics for Drug Discovery. John Wiley & Sons, Inc, 2013. http://dx.doi.org/10.1002/9781118742785.ch6.

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Bajorath, Jürgen. "Molecular Similarity Methods and QSAR Models as Tools for Virtual Screening." In Drug Discovery Handbook. John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471728780.ch3.

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Bologa, Cristian G., Marius M. Olah, and Tudor I. Oprea. "Chemical Database Preparation for Compound Acquisition or Virtual Screening." In Bioinformatics and Drug Discovery. Humana Press, 2006. http://dx.doi.org/10.1385/1-59259-964-8:375.

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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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Imrie, Fergus, Anthony R. Bradley, and Charlotte M. Deane. "Chapter 8. Virtual Screening with Convolutional Neural Networks." In Artificial Intelligence in Drug Discovery. Royal Society of Chemistry, 2020. http://dx.doi.org/10.1039/9781788016841-00151.

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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 screening; Drug discovery"

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Babaria, Khushboo, Sanya Ambegaokar, Shubhankar Das, and Hemant Palivela. "Algorithms for ligand based virtual screening in drug discovery." In 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT). IEEE, 2015. http://dx.doi.org/10.1109/icatcct.2015.7457004.

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Bahi, Meriem, and Mohamed Batouche. "Deep Learning for Ligand-Based Virtual Screening in Drug Discovery." In 2018 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS). IEEE, 2018. http://dx.doi.org/10.1109/pais.2018.8598488.

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Sanchez, Santiago Garcia, Erney Ramirez Aportela, Jose Ignacio Garzon, Pablo Chacon, Antonio Sanz Montemayor, and Raul Cabido. "FRODRUG: A Virtual Screening GPU Accelerated Approach for Drug Discovery." In 2014 22nd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). IEEE, 2014. http://dx.doi.org/10.1109/pdp.2014.64.

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Sid, Karima, and Mohamed Chawki Batouche. "Big Data Analytics Techniques in Virtual Screening for Drug Discovery." In BDCA'17: 2nd international Conference on Big Data, Cloud and Applications. ACM, 2017. http://dx.doi.org/10.1145/3090354.3090363.

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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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Guerrero, Gines D., Horacio E. Perez-S´nchez, Jose M. Cecilia, and Jose M. Garcia. "Parallelization of Virtual Screening in Drug Discovery on Massively Parallel Architectures." In 2012 20th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP 2012). IEEE, 2012. http://dx.doi.org/10.1109/pdp.2012.26.

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Sharma, A., and S. Gokarn. "Drug discovery against sleeping sickness disease via computational virtual screening approach." In ICWET '10: International Conference and Workshop on Emerging Trends in Technology. ACM, 2010. http://dx.doi.org/10.1145/1741906.1742091.

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Preeja, M. P., and K. P. Soman. "Fast computation of marginalized walk kernel for virtual screening in drug discovery." In the 1st Amrita ACM-W Celebration. ACM Press, 2010. http://dx.doi.org/10.1145/1858378.1858394.

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Yueli, Dong, Guo Quan, Kang Ling, and Zhao Xiaoyu. "A New Drug Discovery Platform for Distributed Virtual Screening Based on Data Management System." In 2013 5th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2013. http://dx.doi.org/10.1109/cicn.2013.122.

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Kim, Jincheol B., Nguyen Dang Nhan, Sehoon Lee, Soonwook Hwang, and Vincent Breton. "DrugScreener-G: Towards an Integrated Environment for Grid-Enabled Large-Scale Virtual Screening and Drug Discovery." In 2008 IEEE Fourth International Conference on eScience (eScience). IEEE, 2008. http://dx.doi.org/10.1109/escience.2008.26.

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