Academic literature on the topic 'In silico screening'
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Journal articles on the topic "In silico screening"
Recanatini, Maurizio, Giovanni Bottegoni, and Andrea Cavalli. "In silico antitarget screening." Drug Discovery Today: Technologies 1, no. 3 (December 2004): 209–15. http://dx.doi.org/10.1016/j.ddtec.2004.10.004.
Full textRudisser, Simon, and Wolfgang Jahnke. "NMR and In silico Screening." Combinatorial Chemistry & High Throughput Screening 5, no. 8 (December 1, 2002): 591–603. http://dx.doi.org/10.2174/1386207023329987.
Full textSeifert, Markus H. J., Kristina Wolf, and Daniel Vitt. "Virtual high-throughput in silico screening." BIOSILICO 1, no. 4 (September 2003): 143–49. http://dx.doi.org/10.1016/s1478-5382(03)02359-x.
Full textReddy, Bandi Deepa, and Ch M. Kumari Chitturi. "Screening and Identification of Microbial Derivatives for Inhibiting Legumain: An In silico Approach." Journal of Pure and Applied Microbiology 12, no. 3 (September 30, 2018): 1623–30. http://dx.doi.org/10.22207/jpam.12.3.69.
Full textZaini, Vikra Ardiansyah, Purwantiningsih Sugita, Luthfan Irfana, and Suminar Setiati Achmadi. "In Silico Screening Anticancer of Six Triterpenoids toward miR-494 and TNF-α Targets." Jurnal Kimia Sains dan Aplikasi 23, no. 4 (April 7, 2020): 117–23. http://dx.doi.org/10.14710/jksa.23.4.117-123.
Full textMa, Dik-Lung, Victor Pui-Yan Ma, Daniel Shiu-Hin Chan, Ka-Ho Leung, Hai-Jing Zhong, and Chung-Hang Leung. "In silico screening of quadruplex-binding ligands." Methods 57, no. 1 (May 2012): 106–14. http://dx.doi.org/10.1016/j.ymeth.2012.02.001.
Full textLin, Li-Chiang, Adam H. Berger, Richard L. Martin, Jihan Kim, Joseph A. Swisher, Kuldeep Jariwala, Chris H. Rycroft, et al. "In silico screening of carbon-capture materials." Nature Materials 11, no. 7 (May 27, 2012): 633–41. http://dx.doi.org/10.1038/nmat3336.
Full textVidal, David, and Jordi Mestres. "In Silico Receptorome Screening of Antipsychotic Drugs." Molecular Informatics 29, no. 6-7 (July 9, 2010): 543–51. http://dx.doi.org/10.1002/minf.201000055.
Full textArvidson, Kirk B., Luis G. Valerio, Marilyn Diaz, and Ronald F. Chanderbhan. "In Silico Toxicological Screening of Natural Products." Toxicology Mechanisms and Methods 18, no. 2-3 (January 2008): 229–42. http://dx.doi.org/10.1080/15376510701856991.
Full textFukunishi, Yoshifumi, Satoru Kubota, and Haruki Nakamura. "Noise Reduction Method for Molecular Interaction Energy: Application to in Silico Drug Screening and in Silico Target Protein Screening." Journal of Chemical Information and Modeling 46, no. 5 (July 28, 2006): 2071–84. http://dx.doi.org/10.1021/ci060152z.
Full textDissertations / Theses on the topic "In silico screening"
Dunkel, Mathias [Verfasser]. "3D Konformationsdatenbanken für das in silico Screening / Mathias Dunkel." Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2008. http://d-nb.info/1023262142/34.
Full textBarakat, Nora Hisham. "Combining in vivo and in silico screening for protein stability." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2007. http://wwwlib.umi.com/cr/ucsd/fullcit?p3258327.
Full textTitle from first page of PDF file (viewed May 29, 2007). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 137-152).
Füllbeck, Melanie. "In silico und in vitro Screening von Proteinliganden zur Apoptoseinduktion." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2007. http://dx.doi.org/10.18452/15702.
Full textNowadays, cancer research is focused on the overcoming of survival strategies of malign tumors. In the present work, computer-based methods lead to the identification of novel apoptosis inducing molecules, whose potency should be validated in in vitro experiments. Novel compounds, which induce apoptosis in cancer cells, could be identified on the basis of three projects. Inhibitors for the COP9 signalosome (CSN) associated kinases CK2 and PKD could be discovered using curcumin and emodin as lead compounds. Investigations concerning the mechanism-of-action of betulinic acid (BA) should give information about the function of the Bcl-2 protein family in the BA induced cell death. The experiments, which are focused on the mitochondrial signalling pathway, revealed that BA induces apoptosis in an almost independent manner with regards to the pro- and anti-apoptotic Bcl-2 proteins, but dependent on the presence of activated caspases. Via an in silico screening and the utilisation of a new property filter, novel BA analogues could be identified. For the first time, the data of the National Cancer Institute (NCI) is employed to evaluate results from the in silico screening. In the third project two novel Bcl-2 inhibitors have been identified via in silico screenings, docking experiments and in vitro screenings, which are performed at the moment. The insertion of a photo-switchable compound to the amino acid side chain of an alpha-helical peptide from the pro-apoptotic protein Bid triggered the effect of a modulator, which results in a controllable activation and initiation of apoptosis in tumor cells. In silico screenings as a reliable method corroborated that a systematical evaluation of the virtual hits could decrease the time and costs of experimental testings. The identified hits could serve as novel lead compounds for further in silico screenings or enter the next steps in the development of novel drugs using optimisation methods.
Harding, Simon D. "Database analysis of protein-peptide interactions and in silico screening for peptidomimetics." Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/10935.
Full textSalentin, 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.
Full textYoungs, Louise Claire. "Evaluation of in silico and in vitro screening methods for characterising endocrine disrupting chemical hazards." Thesis, Cranfield University, 2014. http://dspace.lib.cranfield.ac.uk/handle/1826/9717.
Full textElkaïm, Judith. "Drug design in silico : criblage virtuel de protéines à visée thérapeutique." Thesis, Bordeaux 1, 2011. http://www.theses.fr/2011BOR14444/document.
Full textThe 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
Šramel, Peter. "A synthesis and biological screening of predicted inhibitors of Tyrosine Kinases, e.g. KDR, designed in silico." Thesis, Strasbourg, 2017. http://www.theses.fr/2017STRAF064.
Full textProtein kinases represent a group of enzymes responsible for phosphorylation - transfer of aphosphate group from adenosine triphosphate (ATP) to tyrosine or serine/threonine residues. Protein phosphorylation is one of the most important tools regulating a cell activity. A cell "signalization" through an endothelial receptor tyrosine kinase VEGFR2 TK (KDR) is the important pathway influencing growth of a tumor. Small-molecule inhibitors of VEGFR2 TK (VEGFR2 TKls) have become an important tool for the treatment of various types of cancer. This dissertation thesis resulted in a discovery of 16 biologically active N,5-diaryloxazol-2-amines (IC50, VEGFR2 TK). Very good results were achieved especially with compounds 189, 191, 211, 214, 220, 221, 223 and 4 exhibiting the activity under 500 nM
Tichauer, Ruth Elena. "In silico screening of NRas protein oncogenic mutations : new structural and physico-chemical insights into the catalytic activity." Electronic Thesis or Diss., Toulouse 3, 2019. http://www.theses.fr/2019TOU30028.
Full textRas subfamily of small GTPase proteins holds a key position in cell proliferation pathways. Indeed, the transmission of cell growth signals is controlled by proteins belonging to it. In their GTP-bound conformation, these proteins interact and activate downstream effectors of cell replication and differentiation. The hydrolysis reaction that takes place in their center, terminates these interactions, thereby leading to the GDP-bound inactive state. Point mutations of key residues lead to a hydrolysis rate drop that keeps Ras in a GTP-bound active state. Now, high concentrations of active Ras have been associated to abnormal cell proliferation, emblematic of cancerous tissues dissemination. With this into consideration, the elucidation of Ras mechanisms for accelerating GTP cleavage appears as a major step in the development of cancer targeted therapies that would consist in restoring the hydrolysing capabilities within oncogenic Ras to a wild-type rate. In an attempt to gain insight into Ras catalysing properties at the atomic level, unconstrained Molecular Dynamics (MD) simulations describing the G domain at different levels of theory (Molecular Mechanics (MM), Semi-empirical and Density Functional Theory (DFT)) were carried out for NRas member in its wild-type and Gln 61 mutated forms. These simulations were coupled to biomechanic characterisations of the complexes under inspection employing the static modes approach. The latter method, allows the identification of hot spots {\it i.e.} responsive residues of the biomolecule, that have a mechanical influence on the GTPase function of the protein. Hence, they could serve as suitable sites to host drug-like molecules containing specific chemical groups that would facilitate GTP hydrolysis. The obtained results show that water molecules positioning is crucial for efficiently catalysing the reaction that takes place in NRas center. Indeed, the precise positioning observed within the wild-type is lost within the mutants studied here. Furthermore, the active site structural modifications undergone upon Gln 61 substitutions, together with solvent distribution in it, impact directly GTP electronic density. The latter is accommodated to a GDP-like state within the wild-type protein only, as experimentally determined in previous investigations. Thus, oncogenic Gln 61 mutations impair this major catalysing effect. Among three engineered NRas proteins of the Q61R mutated form, proposed during this thesis, one is presented during the defence while the three are described in the manuscript. The chemical groups inserted at the identified site enable the recovery of water distribution as within the wild-type. To end, during the defence only, an alternative reaction pathway of the enzymatic reaction is proposed
Cereto, Massagué Adrià. "Development of tools for in silico drug discovery." Doctoral thesis, Universitat Rovira i Virgili, 2017. http://hdl.handle.net/10803/454678.
Full textEl 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.
Books on the topic "In silico screening"
Uchida, Shizuka. Annotating new genes: From in silico screening to experimental validation. Oxford: Woodhead Publishing Limited, 2012.
Find full textAtta-ur-Rahman and M. Iqbal Choudhary, eds. Frontiers in Cardiovascular Drug Discovery: Volume 4. BENTHAM SCIENCE PUBLISHERS, 2019. http://dx.doi.org/10.2174/97816810839951180401.
Full textLin, C. W., N. F. Chiu, and C. C. Chang. Modulation design of plasmonics for diagnostic and drug screening. Edited by A. V. Narlikar and Y. Y. Fu. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199533060.013.18.
Full textNarlikar, A. V., and Y. Y. Fu, eds. Oxford Handbook of Nanoscience and Technology. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199533060.001.0001.
Full textBook chapters on the topic "In silico screening"
Luque, F. J., and X. Barril. "In Silico Screening." In Protein Surface Recognition, 211–35. Chichester, UK: John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470972137.ch8.
Full textEgbuna, Chukwuebuka, Santwana Palai, Israel Ehizuelen Ebhohimen, Andrew G. Mtewa, Jonathan C. Ifemeje, Genevieve D. Tupas, and Toskë L. Kryeziu. "Screening of Natural Antidiabetic Agents." In Phytochemistry: An in-silico and in-vitro Update, 203–35. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6920-9_11.
Full textK. C., Dhanya, Aditya Menon, and Laxmi Shanker Rai. "In-vitro Models in Anticancer Screening." In Phytochemistry: An in-silico and in-vitro Update, 251–65. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6920-9_13.
Full textLi, Shawn S. C., and Lei Li. "SH2 Ligand Prediction–Guidance for In-Silico Screening." In Methods in Molecular Biology, 77–81. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6762-9_5.
Full textThrithamarassery Gangadharan, Nandu, Ananda Baskaran Venkatachalam, and Shiburaj Sugathan. "High-Throughput and In Silico Screening in Drug Discovery." In Bioresources and Bioprocess in Biotechnology, 247–73. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3573-9_11.
Full textMerlitz, Holger, and Wolfgang Wenzel. "High Throughput in-silico Screening against Flexible Protein Receptors." In Computational Science and Its Applications – ICCSA 2004, 465–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24767-8_48.
Full textFischer, B., H. Merlitz, and W. Wenzel. "Increasing Diversity in In-silico Screening with Target Flexibility." In Lecture Notes in Computer Science, 186–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11560500_17.
Full textMyrianthopoulos, Vassilios, George Lambrinidis, and Emmanuel Mikros. "In Silico Screening of Compound Libraries Using a Consensus of Orthogonal Methodologies." In Methods in Molecular Biology, 261–77. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8630-9_15.
Full textAkhoon, Bashir A., Krishna P. Singh, Madhumita Karmakar, Suchi Smita, Rakesh Pandey, and Shailendra K. Gupta. "Virtual screening and prediction of the molecular mechanism of bioactive compoundsin silico." In Biotechnology of Bioactive Compounds, 371–94. Chichester, UK: John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118733103.ch15.
Full textSrivastava, Supriya, and Puniti Mathur. "In Silico Modeling and Screening Studies of PfRAMA Protein: Implications in Malaria." In Recent Studies on Computational Intelligence, 91–101. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8469-5_8.
Full textConference papers on the topic "In silico screening"
Arrigo, Patrizio, Norbert Maggi, and Carmelina Ruggiero. "In silico screening of Rac1 ligand specificity." In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2008. http://dx.doi.org/10.1109/iembs.2008.4650110.
Full textFukunishi, Yoshifumi. "In Silico Drug Screening Based on a Protein-Compound Affinity Matrix." In 2008 International Conference on Biocomputation, Bioinformatics, and Biomedical Technologies (BIOTECHNO). IEEE, 2008. http://dx.doi.org/10.1109/biotechno.2008.10.
Full textPal, Rajesh, Gauri Misra, and Puniti Mathur. "In silico screening of small molecule modulators of Zika virus proteins." In 2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence (Confluence). IEEE, 2017. http://dx.doi.org/10.1109/confluence.2017.7943179.
Full textSoonwook Hwang, Sehoon Lee, Sangdo Lee, Jincheol Kim, Hanh Thi Thanh Nguyen, Doman Kim, Vincent Breton, and Jean Salzemann. "A grid-enabled problem solving environment for in-silico screening in drug discovery." In 2010 5th International Conference on Computer Sciences and Convergence Information Technology (ICCIT 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccit.2010.5711070.
Full textLida Zhu, Fengji Liang, Juan Liu, Simon Rayner, Yinghui Li, Shanguang Chen, and Jianghui Xiong. "Dynamic remodeling of context-specific miRNAs regulation networks facilitate in silico cancer drug screening." In 2011 IEEE International Conference on Systems Biology (ISB). IEEE, 2011. http://dx.doi.org/10.1109/isb.2011.6033168.
Full textde Jonge, Marc R., H. Maarten Vinkers, Joop H. van Lenthe, Frits Daeyaert, Ian J. Bush, Huub J. J. van Dam, Paul Sherwood, et al. "Ab Initio potential grid based docking: From High Performance Computing to In Silico Screening." In COMPLIFE 2007: The Third International Symposium on Computational Life Science. AIP, 2007. http://dx.doi.org/10.1063/1.2793399.
Full textBai, Jieyun, Yaosheng Lu, Roshan Sharma, and Jichao Zhao. "In Silico Screening of the Key Electrical Remodelling Targets in Atrial Fibrillation-induced Sinoatrial Node Dysfunction." In 2019 Computing in Cardiology Conference. Computing in Cardiology, 2019. http://dx.doi.org/10.22489/cinc.2019.001.
Full textOmix Yu-Chian Chen. "Pharmacoinformatics approach to the discovery of novel selective COX-2 inhibitors by in silico virtual screening." In 2008 IEEE International Joint Conference on Neural Networks (IJCNN 2008 - Hong Kong). IEEE, 2008. http://dx.doi.org/10.1109/ijcnn.2008.4633958.
Full textCabrera, H. S., I. C. Medina, and L. L. Tayo. "In silico screening of inhibitors of p53-MDM2 protein complex through homology modelling and molecular docking." In 4TH ELECTRONIC AND GREEN MATERIALS INTERNATIONAL CONFERENCE 2018 (EGM 2018). Author(s), 2018. http://dx.doi.org/10.1063/1.5080888.
Full textOphelia, E. Felix Anto, P. L. Sujatha, and P. Kumarasamy. "Screening of bioactive compounds from natural remedies for photoaging, to target Ap-1; an in silico approach." In 2016 2nd International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB). IEEE, 2016. http://dx.doi.org/10.1109/aeeicb.2016.7538338.
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