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Literatura académica sobre el tema "Virtual ligand screen"
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Artículos de revistas sobre el tema "Virtual ligand screen"
Yuniarti, Nunung, Sudi Mungkasi, Sri Hartati Yuliani y Enade Perdana Istyastono. "Development of a Graphical User Interface Application to Identify Marginal and Potent Ligands for Estrogen Receptor Alpha". Indonesian Journal of Chemistry 19, n.º 2 (9 de abril de 2019): 531. http://dx.doi.org/10.22146/ijc.34561.
Texto completoGeanes, Alexander R., Hykeyung P. Cho, Kellie D. Nance, Kevin M. McGowan, P. Jeffrey Conn, Carrie K. Jones, Jens Meiler y Craig W. Lindsley. "Ligand-based virtual screen for the discovery of novel M5 inhibitor chemotypes". Bioorganic & Medicinal Chemistry Letters 26, n.º 18 (septiembre de 2016): 4487–91. http://dx.doi.org/10.1016/j.bmcl.2016.07.071.
Texto completoChimenti, Michael S., Stacie L. Bulfer, R. Jeffrey Neitz, Adam R. Renslo, Matthew P. Jacobson, Thomas L. James, Michelle R. Arkin y Mark J. S. Kelly. "A Fragment-Based Ligand Screen Against Part of a Large Protein Machine". Journal of Biomolecular Screening 20, n.º 6 (17 de febrero de 2015): 788–800. http://dx.doi.org/10.1177/1087057115570550.
Texto completoMazzolari, Angelica, Silvia Gervasoni, Alessandro Pedretti, Laura Fumagalli, Rosanna Matucci y Giulio Vistoli. "Repositioning Dequalinium as Potent Muscarinic Allosteric Ligand by Combining Virtual Screening Campaigns and Experimental Binding Assays". International Journal of Molecular Sciences 21, n.º 17 (19 de agosto de 2020): 5961. http://dx.doi.org/10.3390/ijms21175961.
Texto completoRavi D, Chaitanya Kumar K, Mothilal K y Mahender K. "High throughput virtual screening of cyclooxygenase-2 by using database". International Journal of Pharmaceutical Research and Life Sciences 8, n.º 2 (10 de diciembre de 2020): 97–100. http://dx.doi.org/10.26452/ijprls.v8i2.1358.
Texto completoRiniker, Sereina, Gregory A. Landrum, Floriane Montanari, Santiago D. Villalba, Julie Maier, Johanna M. Jansen, W. Patrick Walters y Anang A. Shelat. "Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria". F1000Research 6 (17 de julio de 2017): 1136. http://dx.doi.org/10.12688/f1000research.11905.1.
Texto completoRiniker, Sereina, Gregory A. Landrum, Floriane Montanari, Santiago D. Villalba, Julie Maier, Johanna M. Jansen, W. Patrick Walters y Anang A. Shelat. "Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria". F1000Research 6 (19 de febrero de 2018): 1136. http://dx.doi.org/10.12688/f1000research.11905.2.
Texto completoBepari, Asim Kumar y Hasan Mahmud Reza. "Identification of a novel inhibitor of SARS-CoV-2 3CL-PRO through virtual screening and molecular dynamics simulation". PeerJ 9 (13 de abril de 2021): e11261. http://dx.doi.org/10.7717/peerj.11261.
Texto completoDvorácskó, Szabolcs, László Lázár, Ferenc Fülöp, Márta Palkó, Zita Zalán, Botond Penke, Lívia Fülöp, Csaba Tömböly y Ferenc Bogár. "Novel High Affinity Sigma-1 Receptor Ligands from Minimal Ensemble Docking-Based Virtual Screening". International Journal of Molecular Sciences 22, n.º 15 (29 de julio de 2021): 8112. http://dx.doi.org/10.3390/ijms22158112.
Texto completoScotti, Marcus Tullius, Mateus Feitosa Alves, Chonny Alexander Herrera-Acevedo y Luciana Scotti. "Virtual Screening Studies for Discovery of Novel Inhibitors of Inflammatory Process Targets". Current Pharmaceutical Design 24, n.º 14 (13 de julio de 2018): 1617–38. http://dx.doi.org/10.2174/1381612824666180403122410.
Texto completoTesis sobre el tema "Virtual ligand screen"
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.
Texto completoThe 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.
Koch, Daniel C. "Ligand selective regulation of cell growth by the Ah receptor through activation of TGFβ signaling". Thesis, 2013. http://hdl.handle.net/1957/37899.
Texto completoGraduation date: 2013
Access restricted to the OSU Community at author's request from March 28, 2013 - March 28, 2015