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Journal articles on the topic 'Virtual screening'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Musumeci, Daniele, Christopher A. Hunter, Rafel Prohens, Serena Scuderi, and James F. McCabe. "Virtual cocrystal screening." Chemical Science 2, no. 5 (2011): 883. http://dx.doi.org/10.1039/c0sc00555j.

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2

Sun, Hongmao. "Pharmacophore-Based Virtual Screening." Current Medicinal Chemistry 15, no. 10 (2008): 1018–24. http://dx.doi.org/10.2174/092986708784049630.

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3

Talevi, Alan, Luciana Gavernet, and Luis Bruno-Blanch. "Combined Virtual Screening Strategies." Current Computer Aided-Drug Design 5, no. 1 (2009): 23–37. http://dx.doi.org/10.2174/157340909787580854.

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4

Markt, P., S. Herdlinger, and D. Schuster. "Virtual Screening Against Obesity." Current Medicinal Chemistry 18, no. 14 (2011): 2158–73. http://dx.doi.org/10.2174/092986711795656162.

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5

Hirata, Shuzo, and Katsuyuki Shizu. "High-throughput virtual screening." Nature Materials 15, no. 10 (2016): 1056–57. http://dx.doi.org/10.1038/nmat4750.

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6

Walters, W. Patrick, Matthew T. Stahl, and Mark A. Murcko. "Virtual screening—an overview." Drug Discovery Today 3, no. 4 (1998): 160–78. http://dx.doi.org/10.1016/s1359-6446(97)01163-x.

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7

Crunkhorn, Sarah. "Novel virtual screening approach." Nature Reviews Drug Discovery 16, no. 1 (2017): 18. http://dx.doi.org/10.1038/nrd.2016.272.

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8

Muegge, Ingo, and Scott Oloff. "Advances in virtual screening." Drug Discovery Today: Technologies 3, no. 4 (2006): 405–11. http://dx.doi.org/10.1016/j.ddtec.2006.12.002.

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9

Glen, Robert C, and Gisbert Schneider. "Challenges in Virtual Screening." QSAR & Combinatorial Science 25, no. 12 (2006): 1131. http://dx.doi.org/10.1002/qsar.200690032.

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10

Mestres, J. "Virtual screening: a real screening complement to high-throughput screening." Biochemical Society Transactions 30, no. 4 (2002): 797–99. http://dx.doi.org/10.1042/bst0300797.

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Virtual screening is being routinely used as an integral part of today's hit-identification strategies for, on one hand, prioritizing large corporate screening collections and, on the other hand, to extend the scope of screening to external databases. A brief description of the essential elements required for virtual screening and an application example to the identification of agonist hits for the oestrogen receptor subtype ERα are presented.

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11

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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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

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12

Wildman, Scott. "Approaches to Virtual Screening and Screening Library Selection." Current Pharmaceutical Design 19, no. 26 (2013): 4787–96. http://dx.doi.org/10.2174/1381612811319260009.

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13

Melville, James, Edmund Burke, and Jonathan Hirst. "Machine Learning in Virtual Screening." Combinatorial Chemistry & High Throughput Screening 12, no. 4 (2009): 332–43. http://dx.doi.org/10.2174/138620709788167980.

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14

Muegge, Ingo. "Synergies of Virtual Screening Approaches." Mini-Reviews in Medicinal Chemistry 8, no. 9 (2008): 927–33. http://dx.doi.org/10.2174/138955708785132792.

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15

Muegge, Ingo, and Istvan J. Enyedy. "Virtual Screening for Kinase Targets." Current Medicinal Chemistry 11, no. 6 (2004): 693–707. http://dx.doi.org/10.2174/0929867043455684.

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16

Sunseri, Jocelyn, and David Ryan Koes. "Virtual Screening with Gnina 1.0." Molecules 26, no. 23 (2021): 7369. http://dx.doi.org/10.3390/molecules26237369.

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Virtual screening—predicting which compounds within a specified compound library bind to a target molecule, typically a protein—is a fundamental task in the field of drug discovery. Doing virtual screening well provides tangible practical benefits, including reduced drug development costs, faster time to therapeutic viability, and fewer unforeseen side effects. As with most applied computational tasks, the algorithms currently used to perform virtual screening feature inherent tradeoffs between speed and accuracy. Furthermore, even theoretically rigorous, computationally intensive methods may

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17

Crunkhorn, Sarah. "Screening ultra-large virtual libraries." Nature Reviews Drug Discovery 21, no. 2 (2022): 95. http://dx.doi.org/10.1038/d41573-022-00002-8.

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18

Pérez-Sianes, Javier, Horacio Pérez-Sánchez, and Fernando Díaz. "Virtual Screening Meets Deep Learning." Current Computer-Aided Drug Design 15, no. 1 (2018): 6–28. http://dx.doi.org/10.2174/1573409914666181018141602.

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Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right

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19

Xie, Xiang-Qun Sean. "Exploiting PubChem for virtual screening." Expert Opinion on Drug Discovery 5, no. 12 (2010): 1205–20. http://dx.doi.org/10.1517/17460441.2010.524924.

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20

Schneider, Gisbert. "Virtual screening: an endless staircase?" Nature Reviews Drug Discovery 9, no. 4 (2010): 273–76. http://dx.doi.org/10.1038/nrd3139.

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21

Shoichet, Brian K. "Virtual screening of chemical libraries." Nature 432, no. 7019 (2004): 862–65. http://dx.doi.org/10.1038/nature03197.

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22

Krumrine, Jennifer R., Andrew T. Maynard, and Charles L. Lerman. "Statistical Tools for Virtual Screening." Journal of Medicinal Chemistry 48, no. 23 (2005): 7477–81. http://dx.doi.org/10.1021/jm0501026.

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23

Utsintong, Maleeruk, Todd T. Talley, Palmer W. Taylor, Arthur J. Olson та Opa Vajragupta. "Virtual Screening Against α-Cobratoxin". Journal of Biomolecular Screening 14, № 9 (2009): 1109–18. http://dx.doi.org/10.1177/1087057109344617.

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α-Cobratoxin (Cbtx), the neurotoxin isolated from the venom of the Thai cobra Naja kaouthia , causes paralysis by preventing acetylcholine (ACh) binding to nicotinic acetylcholine receptors (nAChRs). In the current study, the region of the Cbtx molecule that is directly involved in binding to nAChRs is used as the target for anticobratoxin drug design. The crystal structure (1YI5) of Cbtx in complex with the acetylcholine binding protein (AChBP), a soluble homolog of the extracellular binding domain of nAChRs, was selected to prepare an α-cobratoxin active binding site for docking. The amino a

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24

JOHNSON, KATE. "Virtual Screening May Reduce Polypectomies." Family Practice News 36, no. 22 (2006): 43. http://dx.doi.org/10.1016/s0300-7073(06)74202-5.

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25

Walters, W. Patrick, and Renxiao Wang. "New Trends in Virtual Screening." Journal of Chemical Information and Modeling 59, no. 9 (2019): 3603–4. http://dx.doi.org/10.1021/acs.jcim.9b00728.

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26

Koh, J. T. "Making virtual screening a reality." Proceedings of the National Academy of Sciences 100, no. 12 (2003): 6902–3. http://dx.doi.org/10.1073/pnas.1332743100.

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27

Walters, W. Patrick, and Renxiao Wang. "New Trends in Virtual Screening." Journal of Chemical Information and Modeling 60, no. 9 (2020): 4109–11. http://dx.doi.org/10.1021/acs.jcim.0c01009.

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28

Lengauer, Thomas, Christian Lemmen, Matthias Rarey, and Marc Zimmermann. "Novel technologies for virtual screening." Drug Discovery Today 9, no. 1 (2004): 27–34. http://dx.doi.org/10.1016/s1359-6446(04)02939-3.

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29

Schilling, D., D. Hartmann, A. Rosenbaum, and JF Riemann. "Virtual Colonoscopy: Suitable for Screening?" Zeitschrift für Gastroenterologie 42, no. 10 (2004): 1236–37. http://dx.doi.org/10.1055/s-2004-813770.

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30

Irwin, John J. "Community benchmarks for virtual screening." Journal of Computer-Aided Molecular Design 22, no. 3-4 (2008): 193–99. http://dx.doi.org/10.1007/s10822-008-9189-4.

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31

Yang, Tianyi, Johnny C. Wu, Chunli Yan, et al. "Virtual screening using molecular simulations." Proteins: Structure, Function, and Bioinformatics 79, no. 6 (2011): 1940–51. http://dx.doi.org/10.1002/prot.23018.

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32

Комаров, Юрий Игоревич, Алексей Михайлович Беляев, Наталья Николаевна Хилько, et al. "The Future of Population Screening and Virtual Cancer Screening." Voprosy onkologii 70, no. 6 (2025): 1005–16. https://doi.org/10.37469/0507-3758-2024-70-6-1005-1016.

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Онкологические заболевания остаются одной из ведущих причин заболеваемости и смертности в мире. Программы скрининга рака молочной железы, шейки матки и колоректального рака доказали свою эффективность в снижении смертности. Однако их успех часто ограничивается доступностью ресурсов, охватом целевой группы населения и способностью системы здравоохранения к лечению пациентов с ранними формами заболеваний. Организованный скрининг, опирающийся на четкие протоколы, стандартизированные интервалы и постоянный мониторинг, представляет собой эффективный подход, однако требует значительных организационн

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33

Chen, Zhi, Hong-lin Li, Qi-jun Zhang, et al. "Pharmacophore-based virtual screening versus docking-based virtual screening: a benchmark comparison against eight targets." Acta Pharmacologica Sinica 30, no. 12 (2009): 1694–708. http://dx.doi.org/10.1038/aps.2009.159.

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34

Li, Peng, Lili Yin, Bo Zhao, and Yuezhongyi Sun. "Virtual Screening of Drug Proteins Based on Imbalance Data Mining." Mathematical Problems in Engineering 2021 (May 22, 2021): 1–10. http://dx.doi.org/10.1155/2021/5585990.

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To address the imbalanced data problem in molecular docking-based virtual screening methods, this paper proposes a virtual screening method for drug proteins based on imbalanced data mining, which introduces machine learning technology into the virtual screening technology for drug proteins to deal with the imbalanced data problem in the virtual screening process and improve the accuracy of the virtual screening. First, to address the data imbalance problem caused by the large difference between the number of active compounds and the number of inactive compounds in the docking conformation gen

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35

Yin, Lili, Xiaokang Du, Chao Ma, and Hengwen Gu. "Virtual Screening of Drug Proteins Based on the Prediction Classification Model of Imbalanced Data Mining." Processes 10, no. 7 (2022): 1420. http://dx.doi.org/10.3390/pr10071420.

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We propose a virtual screening method based on imbalanced data mining in this paper, which combines virtual screening techniques with imbalanced data classification methods to improve the traditional virtual screening process. First, in the actual virtual screening process, we apply k-means and smote heuristic oversampling method to deal with imbalanced data. Meanwhile, to enhance the accuracy of the virtual screening process, a particle swarm optimization algorithm is introduced to optimize the parameters of the support vector machine classifier, and the concept of ensemble learning is brough

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36

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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37

Gimeno, Aleix, María Ojeda-Montes, Sarah Tomás-Hernández, et al. "The Light and Dark Sides of Virtual Screening: What Is There to Know?" International Journal of Molecular Sciences 20, no. 6 (2019): 1375. http://dx.doi.org/10.3390/ijms20061375.

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Virtual screening consists of using computational tools to predict potentially bioactive compounds from files containing large libraries of small molecules. Virtual screening is becoming increasingly popular in the field of drug discovery as in silico techniques are continuously being developed, improved, and made available. As most of these techniques are easy to use, both private and public organizations apply virtual screening methodologies to save resources in the laboratory. However, it is often the case that the techniques implemented in virtual screening workflows are restricted to thos

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38

Stahura, Florence, and Jurgen Bajorath. "Virtual Screening Methods that Complement HTS." Combinatorial Chemistry & High Throughput Screening 7, no. 4 (2004): 259–69. http://dx.doi.org/10.2174/1386207043328706.

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39

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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40

Ferrucci, Joseph T. "Colon Cancer Screening with Virtual Colonoscopy." American Journal of Roentgenology 177, no. 5 (2001): 975–88. http://dx.doi.org/10.2214/ajr.177.5.1770975.

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41

Jalaie, Mehran, and Veerabahu Shanmugasundaram. "Virtual Screening: Are We There Yet?" Mini-Reviews in Medicinal Chemistry 6, no. 10 (2006): 1159–67. http://dx.doi.org/10.2174/138955706778560157.

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42

Seifert, Markus, and Martin Lang. "Essential Factors for Successful Virtual Screening." Mini-Reviews in Medicinal Chemistry 8, no. 1 (2008): 63–72. http://dx.doi.org/10.2174/138955708783331540.

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43

Sun, Lidan, Hai Qian, and Wenlong Huang. "Virtual Screening for Cholesterol Absorption Inhibitors." Medicinal Chemistry 11, no. 1 (2014): 2–12. http://dx.doi.org/10.2174/1573406410666140428152436.

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44

Douguet, Dominique. "Ligand-Based Approaches in Virtual Screening." Current Computer Aided-Drug Design 4, no. 3 (2008): 180–90. http://dx.doi.org/10.2174/157340908785747456.

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45

Carpenter, Kristy A., David S. Cohen, Juliet T. Jarrell, and Xudong Huang. "Deep learning and virtual drug screening." Future Medicinal Chemistry 10, no. 21 (2018): 2557–67. http://dx.doi.org/10.4155/fmc-2018-0314.

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46

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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47

Knox, Andrew JS, Yidong Yang, David G. Lloyd, and Mary J. Meegan. "Virtual screening of the estrogen receptor." Expert Opinion on Drug Discovery 3, no. 8 (2008): 853–66. http://dx.doi.org/10.1517/17460441.3.8.853.

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48

Willett, P. "Similarity-based approaches to virtual screening." Biochemical Society Transactions 31, no. 3 (2003): 603–6. http://dx.doi.org/10.1042/bst0310603.

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Current similarity measures for virtual screening are based on the use of molecular fingerprints and the Tanimoto coefficient. This paper describes two ways in which one can increase the effectiveness of similarity-based virtual screening: using similarity coefficients other than the Tanimoto coefficient for the comparison of molecular fingerprints; and using a graph-theoretic similarity measure based on the largest substructure common to a pair of molecules.

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49

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 Infras

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50

Harrison, Charlotte. "Homology model allows effective virtual screening." Nature Reviews Drug Discovery 10, no. 11 (2011): 816. http://dx.doi.org/10.1038/nrd3597.

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