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Journal articles on the topic '3D Virtual Screening'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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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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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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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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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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11

Mirabello, Claudio, and Björn Wallner. "InterLig: improved ligand-based virtual screening using topologically independent structural alignments." Bioinformatics 36, no. 10 (2020): 3266–67. http://dx.doi.org/10.1093/bioinformatics/btaa089.

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Abstract Motivation In the past few years, drug discovery processes have been relying more and more on computational methods to sift out the most promising molecules before time and resources are spent to test them in experimental settings. Whenever the protein target of a given disease is not known, it becomes fundamental to have accurate methods for ligand-based virtual screening, which compares known active molecules against vast libraries of candidate compounds. Recently, 3D-based similarity methods have been developed that are capable of scaffold hopping and to superimpose matching molecu

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12

Zhou, Nannan, Yuan Xu, Xian Liu, et al. "Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors." International Journal of Molecular Sciences 16, no. 12 (2015): 13407–26. http://dx.doi.org/10.3390/ijms160613407.

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13

Kinnings, Sarah L., and Richard M. Jackson. "LigMatch: A Multiple Structure-Based Ligand Matching Method for 3D Virtual Screening." Journal of Chemical Information and Modeling 49, no. 9 (2009): 2056–66. http://dx.doi.org/10.1021/ci900204y.

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14

Bhatti, Zeeshan, Abdul Waheed Mahesar, Ghullam Asghar Bhutto, and Fida Hussain Chandio. "Enhancing Cognitive Theory of Multimedia Leaning through 3D Animation." Sukkur IBA Journal of Computing and Mathematical Sciences 1, no. 2 (2017): 25. http://dx.doi.org/10.30537/sjcms.v1i2.43.

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Cognitive theory of Multimedia learning has been a widely used principle in education. However, with current technological advancements and usage, the teaching and learning trend of children’s have also changed with more dependability towards technology. This research work explores and implement the use of 3D Animation as tool for multimedia learning based on cognitive theory. This new dimension in cognitive learning, will foster the latest multimedia tools and application driven through 3D Animation, Virtual Reality and Augmented Reality. The three principles, that facilitate cognitive theory

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15

Lee, June, Sung Cho, and Mi-hyun Kim. "Discovery of CNS-Like D3R-Selective Antagonists Using 3D Pharmacophore Guided Virtual Screening." Molecules 23, no. 10 (2018): 2452. http://dx.doi.org/10.3390/molecules23102452.

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The dopamine D3 receptor is an important CNS target for the treatment of a variety of neurological diseases. Selective dopamine D3 receptor antagonists modulate the improvement of psychostimulant addiction and relapse. In this study, five and six featured pharmacophore models of D3R antagonists were generated and evaluated with the post-hoc score combining two survival scores of active and inactive. Among the Top 10 models, APRRR215 and AHPRRR104 were chosen based on the coefficient of determination (APRRR215: R2training = 0.80; AHPRRR104: R2training = 0.82) and predictability (APRRR215: Q2tes

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16

Sharma, Mayank Kumar, Prashant R. Murumkar, Guanglin Kuang, Yun Tang, and Mange Ram Yadav. "Identifying the structural features and diversifying the chemical domain of peripherally acting CB1 receptor antagonists using molecular modeling techniques." RSC Advances 6, no. 2 (2016): 1466–83. http://dx.doi.org/10.1039/c5ra20612j.

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A four featured pharmacophore and predictive 3D-QSAR models were developed which were used for virtual screening of the Asinex database to get chemically diverse hits of peripherally active CB1 receptor antagonists.

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17

Naima, Jannatul, Ruhshan A. Abir, and Mohammad J. Hosen. "Homology Modeling and Virtual Screening of Proteins Related to PXE and PXE-like Diseases: Insights for Overlapping Metabolites." Current Pharmaceutical Biotechnology 21, no. 14 (2020): 1470–78. http://dx.doi.org/10.2174/1389201021666200519115032.

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Background: The molecular etiology of Pseudoxanthoma Elasticum (PXE), an autosomal recessive connective tissue disorder, has become increasingly complex as not only mutations in the ABCC6, but also in ENPP1 and GGCX, can cause resembling phenotypes. Methods: To get insights on the common pathway, the overlapping metabolites for these three proteins were predicted through 3D homology modeling and virtual screening. 3D homology models of ABCC6, ENPP1, and GGCX were generated by the MODELLER program, which were further validated using RAMPAGE and ERRAT servers. Substrate binding sites of ABCC6 we

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18

Langer, T., R. D. Hoffmann, F. Bachmair, and S. Begle. "Chemical function based pharmacophore models as suitable filters for virtual 3D-database screening." Journal of Molecular Structure: THEOCHEM 503, no. 1-2 (2000): 59–72. http://dx.doi.org/10.1016/s0166-1280(99)00363-2.

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19

Prokhorov, Eugeny I., Aleksandra V. Bekker, Alexander V. Perevoznikov, Mikhail I. Kumskov, and Igor V. Svitanko. "Combining 3D-QSAR and molecular docking for the virtual screening of PARP inhibitors." Mendeleev Communications 25, no. 3 (2015): 214–15. http://dx.doi.org/10.1016/j.mencom.2015.05.019.

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20

Hu, Guoping, Guanglin Kuang, Wen Xiao, Weihua Li, Guixia Liu, and Yun Tang. "Performance Evaluation of 2D Fingerprint and 3D Shape Similarity Methods in Virtual Screening." Journal of Chemical Information and Modeling 52, no. 5 (2012): 1103–13. http://dx.doi.org/10.1021/ci300030u.

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21

Ma, Ying, Hong-Lian Li, Xiu-Bo Chen, et al. "3D QSAR Pharmacophore Based Virtual Screening for Identification of Potential Inhibitors for CDC25B." Computational Biology and Chemistry 73 (April 2018): 1–12. http://dx.doi.org/10.1016/j.compbiolchem.2018.01.005.

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22

Vucicevic, Jelica, Katarina Nikolic, and John B. O. Mitchell. "Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches." Current Medicinal Chemistry 26, no. 21 (2019): 3874–89. http://dx.doi.org/10.2174/0929867324666170712115411.

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Background: Computer-Aided Drug Design has strongly accelerated the development of novel antineoplastic agents by helping in the hit identification, optimization, and evaluation. Results: Computational approaches such as cheminformatic search, virtual screening, pharmacophore modeling, molecular docking and dynamics have been developed and applied to explain the activity of bioactive molecules, design novel agents, increase the success rate of drug research, and decrease the total costs of drug discovery. Similarity, searches and virtual screening are used to identify molecules with an increas

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23

Sharma, Vidushi, Hirdesh Kumar, and Sharad Wakode. "Pharmacophore generation and atom based 3D-QSAR of quinoline derivatives as selective phosphodiesterase 4B inhibitors." RSC Advances 6, no. 79 (2016): 75805–19. http://dx.doi.org/10.1039/c6ra11210b.

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Reported PDE4B inhibitors were used to design QSAR based pharmacophore model. Using developed pharmacophore model, virtual screening was performed followed by cross-docking to identify novel PDE4B specific inhibitors.

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24

Polishchuk, Pavel, Alina Kutlushina, Dayana Bashirova, Olena Mokshyna, and Timur Madzhidov. "Virtual Screening Using Pharmacophore Models Retrieved from Molecular Dynamic Simulations." International Journal of Molecular Sciences 20, no. 23 (2019): 5834. http://dx.doi.org/10.3390/ijms20235834.

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Pharmacophore models are widely used for the identification of promising primary hits in compound large libraries. Recent studies have demonstrated that pharmacophores retrieved from protein-ligand molecular dynamic trajectories outperform pharmacophores retrieved from a single crystal complex structure. However, the number of retrieved pharmacophores can be enormous, thus, making it computationally inefficient to use all of them for virtual screening. In this study, we proposed selection of distinct representative pharmacophores by the removal of pharmacophores with identical three-dimensiona

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25

Kandakatla, Naresh, and Geetha Ramakrishnan. "Ligand Based Pharmacophore Modeling and Virtual Screening Studies to Design Novel HDAC2 Inhibitors." Advances in Bioinformatics 2014 (November 26, 2014): 1–11. http://dx.doi.org/10.1155/2014/812148.

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Histone deacetylases 2 (HDAC2), Class I histone deacetylase (HDAC) family, emerged as an important therapeutic target for the treatment of various cancers. A total of 48 inhibitors of two different chemotypes were used to generate pharmacophore model using 3D QSAR pharmacophore generation (HypoGen algorithm) module in Discovery Studio. The best HypoGen model consists of four pharmacophore features namely, one hydrogen bond acceptor (HBA), and one hydrogen donor (HBD), one hydrophobic (HYP) and one aromatic centres, (RA). This model was validated against 20 test set compounds and this model was

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26

Sugumar, Shobana. "VIRTUAL SCREENING, PHARMACOPHORE MODELING, AND QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP STUDIES ON HISTAMINE 4 RECEPTOR." Asian Journal of Pharmaceutical and Clinical Research 10, no. 12 (2017): 150. http://dx.doi.org/10.22159/ajpcr.2017.v10i12.19991.

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Objective: To find out novel inhibitors for histamine 4 receptor (H4R), the target for various allergic and inflammatory pathophysiological conditions.Methods: Homology modeling of H4R was performed using easy modeler and validated using structure analysis and verification server, and with the modeled structure, virtual screening, pharmacophore modeling, and quantitative structure activity relationship (QSAR) studies were performed using the Schrodinger 9.3 software.Results: Among all the synthetic and natural ligands, hesperidin, vitexin, and diosmin were found to have the highest dock score,

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27

Braga, Rodolpho C., and Carolina H. Andrade. "Assessing the Performance of 3D Pharmacophore Models in Virtual Screening: How Good are They?" Current Topics in Medicinal Chemistry 13, no. 9 (2013): 1127–38. http://dx.doi.org/10.2174/1568026611313090010.

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28

Chavan, Sayalee, Rajkumar Hirwani, M. Sarwar Alam, Nikhil Vidyasagar, Radhacharan Dash, and Veena Deshpande. "Designing of CHK1 Inhibitors by 3d-QSAR, Virtual Screening and Induced Fit Docking Studies." Current Science 109, no. 12 (2015): 2271. http://dx.doi.org/10.18520/cs/v109/i12/2271-2277.

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29

Setny, Piotr, and Joanna Trylska. "Search for Novel Aminoglycosides by Combining Fragment-Based Virtual Screening and 3D-QSAR Scoring." Journal of Chemical Information and Modeling 49, no. 2 (2009): 390–400. http://dx.doi.org/10.1021/ci800361a.

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30

Chavan, Sayalee, Rajkumar Hirwani, M. Sarwar Alam, Nikhil Vidyasagar, Radhacharan Dash, and Veena Deshpande. "Designing of CHK1 Inhibitors by 3d-QSAR, Virtual Screening and Induced Fit Docking Studies." Current Science 109, no. 12 (2015): 2271. http://dx.doi.org/10.18520/v109/i12/2271-2277.

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31

Sharma, Kalicharan, Apeksha Srivastava, Pooja Tiwari, et al. "3D QSAR Based Virtual Screening of Pyrido[1,2-a] Benzimidazoles as Potent Antimalarial Agents." Letters in Drug Design & Discovery 16, no. 3 (2019): 301–12. http://dx.doi.org/10.2174/1570180815666180502115147.

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Background: Development of novel antimalarial agents has been one of the sought areas in medicinal chemistry. In this study the same was done by virtual screening of in-house database on developed QSAR model. </P><P> Methods: A six point pharmacophore model was generated (AADHRR.56) from 41 compounds using PHASE module of Schrodinger software and used for pharmacophore based search. Docking studies of the obtained hits were performed using GLIDE. Most promising hit was synthesized & biologically evaluated for antimalarial activity. </P><P> Result: The best generated

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32

Simonin, Céline, Mahendra Awale, Michael Brand, et al. "Optimization of TRPV6 Calcium Channel Inhibitors Using a 3D Ligand-Based Virtual Screening Method." Angewandte Chemie 127, no. 49 (2015): 14961–65. http://dx.doi.org/10.1002/ange.201507320.

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33

Simonin, Céline, Mahendra Awale, Michael Brand, et al. "Optimization of TRPV6 Calcium Channel Inhibitors Using a 3D Ligand-Based Virtual Screening Method." Angewandte Chemie International Edition 54, no. 49 (2015): 14748–52. http://dx.doi.org/10.1002/anie.201507320.

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34

Ghayas, Sana, M. Ali Masood, Rashida Parveen, et al. "3D QSAR pharmacophore-based virtual screening for the identification of potential inhibitors of tyrosinase." Journal of Biomolecular Structure and Dynamics 38, no. 10 (2019): 2916–27. http://dx.doi.org/10.1080/07391102.2019.1647287.

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35

Švajger, Urban, Žiga Horvat, Damijan Knez, Primož Rožman, Samo Turk, and Stanislav Gobec. "New antagonists of toll-like receptor 7 discovered through 3D ligand-based virtual screening." Medicinal Chemistry Research 24, no. 1 (2014): 362–71. http://dx.doi.org/10.1007/s00044-014-1127-5.

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36

Gupta, Shikhar, and C. Gopi Mohan. "Dual Binding Site and Selective Acetylcholinesterase Inhibitors Derived from Integrated Pharmacophore Models and Sequential Virtual Screening." BioMed Research International 2014 (2014): 1–21. http://dx.doi.org/10.1155/2014/291214.

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In this study, we have employedin silicomethodology combining double pharmacophore based screening, molecular docking, and ADME/T filtering to identify dual binding site acetylcholinesterase inhibitors that can preferentially inhibit acetylcholinesterase and simultaneously inhibit the butyrylcholinesterase also but in the lesser extent than acetylcholinesterase. 3D-pharmacophore models of AChE and BuChE enzyme inhibitors have been developed from xanthostigmine derivatives through HypoGen and validated using test set, Fischer’s randomization technique. The best acetylcholinesterase and butyrylc

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37

Marinescu, Rodica, Diana Popescu, and Dan Laptoiu. "A Review on 3D-Printed Templates for Precontouring Fixation Plates in Orthopedic Surgery." Journal of Clinical Medicine 9, no. 9 (2020): 2908. http://dx.doi.org/10.3390/jcm9092908.

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This paper is a systematic review of the literature on 3D-printed anatomical replicas used as templates for precontouring the fixation plates in orthopedic surgery. Embase, PubMed, Cochrane, Scopus and Springer databases were consulted for information on design study, fracture anatomical location, number of patients, surgical technique, virtual modeling approach and 3D printing process. The initial search provided a total of 496 records. After removing the duplicates, the title and abstract screening, and applying exclusion criteria and citations searching, 30 papers were declared eligible and

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38

Parvez, Mohammad K., and Naidu Subbarao. "Molecular Analysis and Modeling of Hepatitis E Virus Helicase and Identification of Novel Inhibitors by Virtual Screening." BioMed Research International 2018 (August 30, 2018): 1–8. http://dx.doi.org/10.1155/2018/5753804.

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The hepatitis E virus- (HEV-) helicase as a novel drug-target was evaluated. While cell culture model was used for mutational characterization of helicase,in silicoprotein modeling and virtual screening were employed to identify helicase inhibitors. None of the saturation mutant replicons significantly affected RNA replication. Notably, mutants encompassing the Walker motifs replicated as wild-type, showing indispensability of nucleotides conservation in viability compared to known criticality of amino acids. A 3D modeling of HEV-helicase and screening of a compound dataset identified ten most

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39

Xie, Huiding, Kaixiong Qiu, and Xiaoguang Xie. "3D QSAR Studies, Pharmacophore Modeling and Virtual Screening on a Series of Steroidal Aromatase Inhibitors." International Journal of Molecular Sciences 15, no. 11 (2014): 20927–47. http://dx.doi.org/10.3390/ijms151120927.

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40

Fu, Ying, Yi-Na Sun, Ke-Han Yi, et al. "3D Pharmacophore-Based Virtual Screening and Docking Approaches toward the Discovery of Novel HPPD Inhibitors." Molecules 22, no. 6 (2017): 959. http://dx.doi.org/10.3390/molecules22060959.

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41

Giganti, David, Hélène Guillemain, Jean-Louis Spadoni, Michael Nilges, Jean-François Zagury, and Matthieu Montes. "Comparative Evaluation of 3D Virtual Ligand Screening Methods: Impact of the Molecular Alignment on Enrichment." Journal of Chemical Information and Modeling 50, no. 6 (2010): 992–1004. http://dx.doi.org/10.1021/ci900507g.

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42

Guo, Haiqiong, Yuxuan Wang, Qingxiu He, et al. "In silico rational design and virtual screening of antixoidant tripeptides based on 3D-QSAR modeling." Journal of Molecular Structure 1193 (October 2019): 223–30. http://dx.doi.org/10.1016/j.molstruc.2019.05.002.

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43

Bender, Andreas, Hamse Y. Mussa, Gurprem S. Gill, and Robert C. Glen. "Molecular Surface Point Environments for Virtual Screening and the Elucidation of Binding Patterns (MOLPRINT 3D)." Journal of Medicinal Chemistry 47, no. 26 (2004): 6569–83. http://dx.doi.org/10.1021/jm049611i.

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44

Sakkiah, Sugunadevi, Sundarapandian Thangapandian, Shalini John, Yong Jung Kwon, and Keun Woo Lee. "3D QSAR pharmacophore based virtual screening and molecular docking for identification of potential HSP90 inhibitors." European Journal of Medicinal Chemistry 45, no. 6 (2010): 2132–40. http://dx.doi.org/10.1016/j.ejmech.2010.01.016.

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45

He, Lijuan, Ru Dai, Xuan R. Zhang, et al. "Ligand-based 3D pharmacophore design, virtual screening and molecular docking for novel p38 MAPK inhibitors." Medicinal Chemistry Research 24, no. 2 (2014): 797–809. http://dx.doi.org/10.1007/s00044-014-1158-y.

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46

Gupta, Shikhar, and C. Gopi Mohan. "3D-pharmacophore model based virtual screening to identify dual-binding site and selective acetylcholinesterase inhibitors." Medicinal Chemistry Research 20, no. 9 (2010): 1422–30. http://dx.doi.org/10.1007/s00044-010-9373-7.

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47

Dube, Divya, Vinita Periwal, Mukesh Kumar, Sujata Sharma, Tej P. Singh, and Punit Kaur. "3D-QSAR based pharmacophore modeling and virtual screening for identification of novel pteridine reductase inhibitors." Journal of Molecular Modeling 18, no. 5 (2011): 1701–11. http://dx.doi.org/10.1007/s00894-011-1187-0.

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48

Polamreddy, Prasanthi, Vinita Vishwakarma, and Manoj Kumar Mahto. "COMBINATORIAL PHARMACOPHORE MODELING AND ATOM BASED 3D QSAR STUDIES OF BENZOTHIADIAZINES AS HCV-NS5B INHIBITORS." International Journal of Pharmacy and Pharmaceutical Sciences 10, no. 3 (2018): 43. http://dx.doi.org/10.22159/ijpps.2018v10i3.23734.

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Objective: The objective of the current study was to elucidate the 3D pharmacophoric features of benzothiadiazine derivatives that are crucial for inhibiting Hepatitis C virus (HCV) Non-structural protein 5B (NS5B) and quantifying the features by building an atom based 3D quantitative structure-activity relationship (3D QSAR) model.Methods: Generation of QSAR model was carried out using PHASE 3.3.Results: A five-point pharmacophore model with two hydrogen bond acceptors, one negative ionization potential and two aromatic rings (AANRR) was found to be common among a maximum number of benzothiad

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49

Markt, Patrick, Rasmus K. Petersen, Esben N. Flindt, et al. "Discovery of Novel PPAR Ligands by a Virtual Screening Approach Based on Pharmacophore Modeling, 3D Shape, and Electrostatic Similarity Screening." Journal of Medicinal Chemistry 51, no. 20 (2008): 6303–17. http://dx.doi.org/10.1021/jm800128k.

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50

Gao, Ya, Yanming Chen, Yafeng Tian, et al. "In silico study of 3-hydroxypyrimidine-2,4-diones as inhibitors of HIV RT-associated RNase H using molecular docking, molecular dynamics, 3D-QSAR, and pharmacophore models." New Journal of Chemistry 43, no. 43 (2019): 17004–17. http://dx.doi.org/10.1039/c9nj03353j.

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