Relevant bibliographies by topics / 3D Virtual Screening

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  1. Journal articles

Academic literature on the topic '3D Virtual Screening'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "3D Virtual Screening"

1

Wolber, Gerhard. "3D pharmacophore elucidation and virtual screening." Drug Discovery Today: Technologies 7, no. 4 (2010): e203-e204. http://dx.doi.org/10.1016/j.ddtec.2010.12.004.

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2

Ren, Ji-Xia, Rui-Tao Zhang, and Hui Zhang. "Identifying Novel ATX Inhibitors via Combinatory Virtual Screening Using Crystallography-Derived Pharmacophore Modelling, Docking Study, and QSAR Analysis." Molecules 25, no. 5 (2020): 1107. http://dx.doi.org/10.3390/molecules25051107.

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Abstract:
Autotaxin (ATX) is considered as an interesting drug target for the therapy of several diseases. The goal of the research was to detect new ATX inhibitors which have novel scaffolds by using virtual screening. First, based on two diverse receptor-ligand complexes, 14 pharmacophore models were developed, and the 14 models were verified through a big test database. Those pharmacophore models were utilized to accomplish virtual screening. Next, for the purpose of predicting the probable binding poses of compounds and then carrying out further virtual screening, docking-based virtual screening was performed. Moreover, an excellent 3D QSAR model was established, and 3D QSAR-based virtual screening was applied for predicting the activity values of compounds which got through the above two-round screenings. A correlation coefficient r2, which equals 0.988, was supplied by the 3D QSAR model for the training set, and the correlation coefficient r2 equaling 0.808 for the test set means that the developed 3D QSAR model is an excellent model. After the filtering was done by the combinatory virtual screening, which is based on the pharmacophore modelling, docking study, and 3D QSAR modelling, we chose nine potent inhibitors with novel scaffolds finally. Furthermore, two potent compounds have been particularly discussed.
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3

Muegge, Ingo, and Qiang Zhang. "3D virtual screening of large combinatorial spaces." Methods 71 (January 2015): 14–20. http://dx.doi.org/10.1016/j.ymeth.2014.06.009.

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4

Seidel, Thomas, Gökhan Ibis, Fabian Bendix, and Gerhard Wolber. "Strategies for 3D pharmacophore-based virtual screening." Drug Discovery Today: Technologies 7, no. 4 (2010): e221-e228. http://dx.doi.org/10.1016/j.ddtec.2010.11.004.

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5

Ghemtio, Leo, Violeta I. Perez-Nueno, Vincent Leroux, et al. "Recent Trends and Applications in 3D Virtual Screening." Combinatorial Chemistry & High Throughput Screening 15, no. 9 (2012): 749–69. http://dx.doi.org/10.2174/138620712803519707.

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6

Wei, Jing, Wanlu Qu, Yingda Ye, and Qingzhi Gao. "3D Pharmacophore Based Virtual Screening of A2A Adenosine Receptor Antagonists." Protein & Peptide Letters 17, no. 3 (2010): 332–39. http://dx.doi.org/10.2174/092986610790780260.

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7

Mavridis, Lazaros, Brian D. Hudson, and David W. Ritchie. "Toward High Throughput 3D Virtual Screening Using Spherical Harmonic Surface Representations." Journal of Chemical Information and Modeling 47, no. 5 (2007): 1787–96. http://dx.doi.org/10.1021/ci7001507.

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8

Doddareddy, Munikumar Reddy, Hyunah Choo, Yong Seo Cho, et al. "3D pharmacophore based virtual screening of T-type calcium channel blockers." Bioorganic & Medicinal Chemistry 15, no. 2 (2007): 1091–105. http://dx.doi.org/10.1016/j.bmc.2006.10.013.

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9

Diller, David J., Nancy D. Connell, and William J. Welsh. "Avalanche for shape and feature-based virtual screening with 3D alignment." Journal of Computer-Aided Molecular Design 29, no. 11 (2015): 1015–24. http://dx.doi.org/10.1007/s10822-015-9875-y.

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10

McMasters, Daniel R., Margarita Garcia-Calvo, Vladimir Maiorov, et al. "Spiroimidazolidinone NPC1L1 inhibitors. 1: Discovery by 3D-similarity-based virtual screening." Bioorganic & Medicinal Chemistry Letters 19, no. 11 (2009): 2965–68. http://dx.doi.org/10.1016/j.bmcl.2009.04.031.

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