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1

Hol, W. G. J. "Protein crystallography, computer graphics and drug design." Pure and Applied Chemistry 59, no. 3 (January 1, 1987): 431–36. http://dx.doi.org/10.1351/pac198759030431.

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2

Hol, Wim G. J. "Protein Crystallography and Computer Graphics—toward Rational Drug Design." Angewandte Chemie International Edition in English 25, no. 9 (September 1986): 767–78. http://dx.doi.org/10.1002/anie.198607673.

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3

Ma, Jing. "The Application of Pattern Recognition Technology in Quantitative Drug Design." Advanced Materials Research 926-930 (May 2014): 3414–17. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3414.

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Quantitative design focuses on drugs between biological activity and structure parameters of quantitative change rule, so as to apply these rules to guide the design and synthesis of new drugs to predict unknown compounds of biological activity, agent theory and inference mechanism of drugs. This paper briefly introduces the concept of quantitative drug design and computer graphics and its typical applications in pattern recognition, quantitative drug design, and introduces a quantitative drug design system based on pattern recognition, finally will point out their application prospects and some problems to be solved. Quantitative drug design is of great significance for the diagnosis of the disease.
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4

Hibert, M. "Graphics computer-aided receptor mapping: an efficient predictive tool for drug design." European Journal of Pharmacology 183, no. 2 (July 1990): 180. http://dx.doi.org/10.1016/0014-2999(90)93013-g.

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5

Hibert, M., M. W Gittos, D. N Middlemiss, and J. R Fozard. "Graphics computer-generated receptor mapping as a redictive tool for drug design." Journal of Molecular Graphics 4, no. 4 (December 1986): 227. http://dx.doi.org/10.1016/0263-7855(86)80055-8.

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6

Prabhu, S., and M. Arulperumjothi. "ON CERTAIN TOPOLOGICAL INDICES OF BENZENOID COMPOUNDS." JOURNAL OF ADVANCES IN CHEMISTRY 13, no. 8 (February 18, 2017): 6406–12. http://dx.doi.org/10.24297/jac.v13i8.5747.

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Drug discovery is mainly the result of chance discovery and massive screening of large corporate libraries of synthesized or naturally-occurring compounds. Computer aided drug design is an approach to rational drug design made possible by the recent advances in computational chemistry in various fields of chemistry, such as molecular graphics, molecular mechanics, quantum chemistry, molecular dynamics, library searching, prediction of physical, chemical, and biological properties. The structure of a chemical compound can be represented by a graph whose vertex and edge specify the atom and bonds respectively. Topological indices are designed basically by transforming a molecular graph into a number. A topological index is a numeric quantity of a molecule that is mathematically derived from the structural graph of a molecule. In this paper we compute certain topological indices of pyrene molecular graph. The topological indices are used in quantitative structure-property relationships (QSPR) and quantitative structure-activity relationships (QSAR) studies.Â
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7

Papakonstantinou, Eleni, Vasileios Megalooikonomou, and Dimitrios Vlachakis. "Dark Suite; a comprehensive toolbox for computer-aided drug design." EMBnet.journal 25 (March 13, 2020): e928. http://dx.doi.org/10.14806/ej.25.0.928.

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Dark Suite is a complete interactive software pipeline aiming to exploit the advantages of free software and modern programming. Besides two commands (installation and opening) on the command line, the handling and the program operation is done through a user’s friendly interface. This platform has a central graphical interface which allows the user to choose in what computational tool to work. Each computational tool has an interface. Dark Suite combines the functions of other programmes to create a pipeline for high-quality secondary effects through a friendly user interface. It is made to run on GNU/Linux distributions and its interface was built using JAVA to seamlessly integrate scientific tools written in Perl, Java, R and Python.
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8

Tapia, O., M. Paulino, and F. M. L. G. Stamato. "Computer assisted simulations and molecular graphics methods in molecular design. 1. Theory and applications to enzyme active-site directed drug design." Molecular Engineering 3, no. 4 (1994): 377–414. http://dx.doi.org/10.1007/bf01003761.

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9

Pei, Jianfeng, Jiaju Zhou, Guirong Xie, Hongming Chen, and Xianfeng He. "PARM: a practical utility for drug design." Journal of Molecular Graphics and Modelling 19, no. 5 (October 2001): 448–54. http://dx.doi.org/10.1016/s1093-3263(00)00104-2.

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10

Griffith, Renate, Tien T. T. Luu, James Garner, and Paul A. Keller. "Combining structure-based drug design and pharmacophores." Journal of Molecular Graphics and Modelling 23, no. 5 (April 2005): 439–46. http://dx.doi.org/10.1016/j.jmgm.2004.12.001.

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11

Kazerounian, Kazem. "From Mechanisms and Robotics to Protein Conformation and Drug Design." Journal of Mechanical Design 126, no. 1 (January 1, 2004): 40–45. http://dx.doi.org/10.1115/1.1644554.

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The systematic study of kinematics can be traced to the writings of the ancient Greeks, Egyptians, Romans and Persians as far back as 500 B.C. For many centuries kinematics (along with geometry) was regarded as one of the basic sciences that explained observed physical phenomena and was used to engineer machines. Though it may seem unlikely, kinematics (in particular, robot kinematics) can significantly contribute to our understanding of biological systems and their functions at the microscopic level and to the engineering of new diagnostic tools, treatments, and drugs for a variety of diseases. Given the vast body of knowledge in theoretical, applied, and analytical kinematics and robotics, the conspicuous absence of the kinematics community from current molecular science research relating to the prediction of protein folding, protein docking, protein engineering, and drug design seems puzzling. In this paper, we will discuss the potential contributions of kinematics to some current challenges in biotechnology.
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12

Chang, Robert, Jae Nam, and Wei Sun. "Computer-Aided Design, Modeling, and Freeform Fabrication of 3D Tissue Constructs for Drug Metabolism Studies." Computer-Aided Design and Applications 5, no. 1-4 (January 2008): 363–70. http://dx.doi.org/10.3722/cadaps.2008.363-370.

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13

Shahin, Rand, Iman Mansi, Lubna Swellmeen, Tahani Alwidyan, Nabil Al-Hashimi, Yaser Al-Qarar’h, and Omar Shaheen. "Ligand-based computer aided drug design reveals new tropomycin receptor kinase a (TrkA) inhibitors." Journal of Molecular Graphics and Modelling 80 (March 2018): 327–52. http://dx.doi.org/10.1016/j.jmgm.2018.01.004.

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14

Wolters, Hans J. "Geometric modeling applications in rational drug design: a survey." Computer Aided Geometric Design 23, no. 6 (August 2006): 482–94. http://dx.doi.org/10.1016/j.cagd.2006.03.003.

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15

Simon, Laurent. "Graphical process design tools for iontophoretic transdermal drug-delivery devices." Computer Methods and Programs in Biomedicine 107, no. 3 (September 2012): 447–55. http://dx.doi.org/10.1016/j.cmpb.2011.01.004.

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16

Kiralj, Rudolf, and Márcia M. C. Ferreira. "Molecular graphics approach to bacterial AcrB protein–β-lactam antibiotic molecular recognition in drug efflux mechanism." Journal of Molecular Graphics and Modelling 25, no. 1 (September 2006): 126–45. http://dx.doi.org/10.1016/j.jmgm.2005.10.010.

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17

Khalid, Zoya, and Osman Ugur Sezerman. "Computational drug repurposing to predict approved and novel drug-disease associations." Journal of Molecular Graphics and Modelling 85 (October 2018): 91–96. http://dx.doi.org/10.1016/j.jmgm.2018.08.005.

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18

Draber, Wilfried. "Can Quantitative Structure Activity Analyses and Molecular Graphics Assist in Designing New Inhibitors of Photosystem II?" Zeitschrift für Naturforschung C 42, no. 6 (June 1, 1987): 713–17. http://dx.doi.org/10.1515/znc-1987-0611.

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A calculation, based on various plausible assumptions, shows that under optimal conditions a rate of less than 10 g/ha of a potent photosynthesis inhibitor would be sufficient for good herbidical action. The possibility to achieve this by computer assisted drug design (CADD) is discussed. In order to provide some background, several methods of “classical” OSAR and the most frequently used parameters are discussed. Moreover, some aspects of molecular graphics are described. This powerful new CADD-method gives results that are qualitatively different from QSAR. It is not only intellectually demanding but also expensive with regard to hard- and software. In conclusion, one successful QSAR-based prediction of a herbicide is reviewed.
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19

Tsekleves, Emmanuel, Andy Darby, Collins Ahorlu, Dziedzom De Souza, Roger Pickup, and Daniel Boakye. "Combining design research with microbiology to tackle drug-resistant infections in different home environments in Ghana: Challenging the boundaries of design thinking." Design Journal 22, sup1 (April 1, 2019): 347–58. http://dx.doi.org/10.1080/14606925.2019.1595424.

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20

Brown, Robert D., Moists Hassan, and Marvin Waldman. "Combinatorial library design for diversity, cost efficiency, and drug-like character libraries." Journal of Molecular Graphics and Modelling 18, no. 4-5 (2000): 537. http://dx.doi.org/10.1016/s1093-3263(00)80096-0.

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21

da Silva, Aldineia P., Laise P. A. Chiari, Amanda R. Guimaraes, Kathia M. Honorio, and Albérico B. F. da Silva. "Drug design of new 5-HT6R antagonists aided by artificial neural networks." Journal of Molecular Graphics and Modelling 104 (May 2021): 107844. http://dx.doi.org/10.1016/j.jmgm.2021.107844.

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22

Ebrahimzadeh, Magid, Shlomo Barnoon, and Zilla Sinuani-Stern. "A simulation of a multi-item drug inventory system." SIMULATION 45, no. 3 (September 1985): 115–21. http://dx.doi.org/10.1177/003754978504500302.

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23

Hibert, Marcel F., Maurice W. Gittos, Derek N. Middlemiss, Anis K. Mir, and John R. Fozard. "Graphics computer-aided receptor mapping as a predictive tool for drug design: development of potent, selective, and stereospecific ligands for the 5-HT1A receptor." Journal of Medicinal Chemistry 31, no. 6 (June 1988): 1087–93. http://dx.doi.org/10.1021/jm00401a007.

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24

Vulpetti, Anna, Elena Casale, Fulvia Roletto, Raffaella Amici, Manuela Villa, and Paolo Pevarello. "Structure-based drug design to the discovery of new 2-aminothiazole CDK2 inhibitors." Journal of Molecular Graphics and Modelling 24, no. 5 (March 2006): 341–48. http://dx.doi.org/10.1016/j.jmgm.2005.09.012.

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25

Ramezani, Mohammad, and Jamal Shamsara. "Application of DPD in the design of polymeric nano-micelles as drug carriers." Journal of Molecular Graphics and Modelling 66 (May 2016): 1–8. http://dx.doi.org/10.1016/j.jmgm.2016.01.010.

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26

Amera, Gizachew Muluneh, Rameez Jabeer Khan, Amita Pathak, Rajat Kumar Jha, Monika Jain, Jayaraman Muthukumaran, and Amit Kumar Singh. "Structure based drug designing and discovery of promising lead molecules against UDP-N-acetylenolpyruvoylglucosamine reductase (MurB): A potential drug target in multi-drug resistant Acinetobacter baumannii." Journal of Molecular Graphics and Modelling 100 (November 2020): 107675. http://dx.doi.org/10.1016/j.jmgm.2020.107675.

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27

Schneider, Melanie, Jean-Luc Pons, and Gilles Labesse. "Exploring the conformational space of a receptor for drug design: An ERα case study." Journal of Molecular Graphics and Modelling 108 (November 2021): 107974. http://dx.doi.org/10.1016/j.jmgm.2021.107974.

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28

da Silva, Carlos Henrique Tomich de Paula, Vinicius Barreto da Silva, Jonathan Resende, Patrícia Franco Rodrigues, Fernanda Cristina Bononi, Carolina Gomes Benevenuto, and Carlton Anthony Taft. "Computer-aided drug design and ADMET predictions for identification and evaluation of novel potential farnesyltransferase inhibitors in cancer therapy." Journal of Molecular Graphics and Modelling 28, no. 6 (February 2010): 513–23. http://dx.doi.org/10.1016/j.jmgm.2009.11.011.

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29

Alnabulsi, Soraya, Enas A. Al-Hurani, Nizar A. Al-shar'i, and Tamam El-Elimat. "Amino-carboxamide benzothiazoles as potential LSD1 hit inhibitors. Part I: Computational fragment-based drug design." Journal of Molecular Graphics and Modelling 93 (December 2019): 107440. http://dx.doi.org/10.1016/j.jmgm.2019.107440.

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30

Pulla, Venkat Koushik, Mallika Alvala, Dinavahi Saketh Sriram, Srikant Viswanadha, Dharmarajan Sriram, and Perumal Yogeeswari. "Structure-based drug design of small molecule SIRT1 modulators to treat cancer and metabolic disorders." Journal of Molecular Graphics and Modelling 52 (July 2014): 46–56. http://dx.doi.org/10.1016/j.jmgm.2014.06.005.

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31

Nikfar, Zahra, and Zahra Shariatinia. "Phosphate functionalized (4,4)-armchair CNTs as novel drug delivery systems for alendronate and etidronate anti-osteoporosis drugs." Journal of Molecular Graphics and Modelling 76 (September 2017): 86–105. http://dx.doi.org/10.1016/j.jmgm.2017.06.021.

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32

Czaplinsky, Karl-H., Manfred Kansy, Joachim K. Seydel, and Rolf Haller. "Design of a New Substituted 2,4-Diamino-5-benzylpyrimidine as Inhibitor of Bacterial Dihydrofolate Reductase Assisted by Molecular Graphics." Quantitative Structure-Activity Relationships 6, no. 2 (1987): 70–72. http://dx.doi.org/10.1002/qsar.19870060206.

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33

Hu, Baichun, Xiaoming Zheng, Ying Wang, Jian Wang, and Fengjiao Zhang. "Computational Approaches for Elucidating Protein-Protein Interactions in Cation Channel Signaling." Current Drug Targets 21, no. 2 (January 22, 2020): 179–92. http://dx.doi.org/10.2174/1389450120666190906154412.

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Background: The lipid bilayer of the plasma membrane is impermeable to ions, yet changes in the flux of ions across the cell membrane are critical regulatory events in cells. Because of their regulatory roles in a range of physiological processes, such as electrical signaling in muscles and neurons, to name a few, these proteins are one of the most important drug targets. Objective: This review mainly focused on the computational approaches for elucidating proteinprotein interactions in cation channel signaling. Discussion: Due to continuously advanced facilities and technologies in computer sciences, the physical contacts of macromolecules of channel structures have been virtually visualized. Indeed, techniques like protein-protein docking, homology modeling, and molecular dynamics simulation are valuable tools for predicting the protein complex and refining channels with unreleased structures. Undoubtedly, these approaches will greatly expand the cation channel signaling research, thereby speeding up structure-based drug design and discovery. Conclusion: We introduced a series of valuable computational tools for elucidating protein-protein interactions in cation channel signaling, including molecular graphics, protein-protein docking, homology modeling, and molecular dynamics simulation.
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34

Ikram, Saima, Jamshaid Ahmad, and Serdar Durdagi. "Screening of FDA approved drugs for finding potential inhibitors against Granzyme B as a potent drug-repurposing target." Journal of Molecular Graphics and Modelling 95 (March 2020): 107462. http://dx.doi.org/10.1016/j.jmgm.2019.107462.

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35

Mushayabasa, Steady, and Claver P. Bhunu. "Mathematical analysis of hepatitis C model for intravenous drug misusers: Impact of antiviral therapy, abstinence and relapse." SIMULATION 90, no. 5 (April 16, 2014): 487–500. http://dx.doi.org/10.1177/0037549714528388.

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36

Vatanparast, Morteza, and Zahra Shariatinia. "Hexagonal boron nitride nanosheet as novel drug delivery system for anticancer drugs: Insights from DFT calculations and molecular dynamics simulations." Journal of Molecular Graphics and Modelling 89 (June 2019): 50–59. http://dx.doi.org/10.1016/j.jmgm.2019.02.012.

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37

Rajkhowa, Sanchaita, Anupam Nath Jha, and Ramesh Chandra Deka. "Anti-tubercular drug development: computational strategies to identify potential compounds." Journal of Molecular Graphics and Modelling 62 (November 2015): 56–68. http://dx.doi.org/10.1016/j.jmgm.2015.09.007.

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38

Ahmad, Jamshaid, Saima Ikram, Ahmer Bin Hafeez, and Serdar Durdagi. "Physics-driven identification of clinically approved and investigation drugs against human neutrophil serine protease 4 (NSP4): A virtual drug repurposing study." Journal of Molecular Graphics and Modelling 101 (December 2020): 107744. http://dx.doi.org/10.1016/j.jmgm.2020.107744.

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39

Fatematossadat, Pourseyed Aghaei, Mahnaz Mohammadi, and S. Ehsan Roozmeh. "Fe@(Au/Ag)n (n=1,12,54) core-shell nanoparticles as effective drug delivery vehicles for anti-cancer drugs: The computational study." Journal of Molecular Graphics and Modelling 90 (July 2019): 33–41. http://dx.doi.org/10.1016/j.jmgm.2019.03.020.

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40

Kist, Roger, Luis Fernando Saraiva Macedo Timmers, and Rafael Andrade Caceres. "Searching for potential mTOR inhibitors: Ligand-based drug design, docking and molecular dynamics studies of rapamycin binding site." Journal of Molecular Graphics and Modelling 80 (March 2018): 251–63. http://dx.doi.org/10.1016/j.jmgm.2017.12.015.

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41

Wills, Graham, and Leland Wilkinson. "AutoVis: Automatic Visualization." Information Visualization 9, no. 1 (December 18, 2008): 47–69. http://dx.doi.org/10.1057/ivs.2008.27.

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AutoVis is a data viewer that responds to content–text, relational tables, hierarchies, streams, images–and displays the information appropriately (that is, as an expert would). Its design rests on the grammar of graphics, scagnostics and a modeler based on the logic of statistical analysis. We distinguish an automatic visualization system (AVS) from an automated visualization system. The former automatically makes decisions about what is to be visualized. The latter is a programming system for automating the production of charts, graphs and visualizations. An AVS is designed to provide a first glance at data before modeling and analysis are done. AVS is designed to protect researchers from ignoring missing data, outliers, miscodes and other anomalies that can violate statistical assumptions or otherwise jeopardize the validity of models. The design of this system incorporates several unique features: (1) a spare interface–analysts simply drag a data source into an empty window, (2) a graphics generator that requires no user definitions to produce graphs, (3) a statistical analyzer that protects users from false conclusions, and (4) a pattern recognizer that responds to the aspects (density, shape, trend, and so on) that professional statisticians notice when investigating data sets.
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42

Jiang, Mingjian, Zhiqiang Wei, Shugang Zhang, Shuang Wang, Xiaofeng Wang, and Zhen Li. "FRSite: Protein drug binding site prediction based on faster R–CNN." Journal of Molecular Graphics and Modelling 93 (December 2019): 107454. http://dx.doi.org/10.1016/j.jmgm.2019.107454.

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43

Xiao, Chi, Kun Ma, Guofeng Cai, Xinyu Zhang, and Esmail Vessally. "Borophene as an electronic sensor for metronidazole drug: A computational study." Journal of Molecular Graphics and Modelling 96 (May 2020): 107539. http://dx.doi.org/10.1016/j.jmgm.2020.107539.

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44

Reutlinger, Michael, and Gisbert Schneider. "Nonlinear dimensionality reduction and mapping of compound libraries for drug discovery." Journal of Molecular Graphics and Modelling 34 (April 2012): 108–17. http://dx.doi.org/10.1016/j.jmgm.2011.12.006.

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45

Fujita, Tadao. "Using CAD to design receptor targetting of potent drugs." Computer-Aided Design 19, no. 2 (March 1987): 91–94. http://dx.doi.org/10.1016/s0010-4485(87)80051-9.

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46

Zaric, Gregory S., Margaret L. Brandeau, Ahmed M. Bayoumi, and Douglas K. Owens. "The Effects of Protease Inhibitors on the Spread of HIV and the Development of Drug-Resistant HIV Strains: A Simulation Study." SIMULATION 71, no. 4 (October 1998): 262–75. http://dx.doi.org/10.1177/003754979807100406.

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47

Brown, Guy J., Godfrey G. S. Collins, David G. Dewhurst, and Ian E. Hughes. "Computer Simulations in Teaching Neuromuscular Pharmacology—Time for a Change from Traditional Methods?" Alternatives to Laboratory Animals 16, no. 2 (December 1988): 163–74. http://dx.doi.org/10.1177/026119298801600207.

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Two interactive computer programs are described which illustrate different approaches to teaching the pharmacology of neuromuscular transmission. Both programs are based on a simulation of the in vivo sciatic nerve–tibialis anterior muscle preparation of the cat. Program 1 simulates the preparation and allows students to follow a schedule set by the teacher, or design experiments themselves using drugs (including unknowns) and procedures selected from an extensive menu. The sequence of events and the doses used are entirely in the hands of the user and students “learn by discovery”. Program 2 is an interactive, computer-assisted learning package based on a software simulation of experiments which can be performed on this preparation to illustrate the fundamental pharmacology. The program is menu-driven, contains extensive textual information, makes use of animated graphics, and is accompanied by educational support material and assessments. Details of each program are presented and their use as alternatives to animal experiments is discussed in relation to the primary teaching objectives of practical classes.
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48

Baday, Sefer. "Optimization of CHARMM force field parameters of a prostate cancer drug enzalutamide." Journal of Molecular Graphics and Modelling 96 (May 2020): 107541. http://dx.doi.org/10.1016/j.jmgm.2020.107541.

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49

Yap, C. W., Z. R. Li, and Y. Z. Chen. "Quantitative structure–pharmacokinetic relationships for drug clearance by using statistical learning methods." Journal of Molecular Graphics and Modelling 24, no. 5 (March 2006): 383–95. http://dx.doi.org/10.1016/j.jmgm.2005.10.004.

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50

Onsori, Saeid, and Elham Alipour. "A computational study on the cisplatin drug interaction with boron nitride nanocluster." Journal of Molecular Graphics and Modelling 79 (January 2018): 223–29. http://dx.doi.org/10.1016/j.jmgm.2017.12.007.

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