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Journal articles on the topic 'Molecular modeling analysis'

Author: Grafiati

Published: 18 May 2024

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1

Hanai, Toshihiko. "Molecular Modeling for Quantitative Analysis of Molecular Interaction†." Letters in Drug Design & Discovery 2, no. 3 (May 1, 2005): 232–38. http://dx.doi.org/10.2174/1570180053765192.

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2

Kumawat, Renu, Vineet Sahula, and Manoj S. Gaur. "Probabilistic modeling and analysis of molecular memory." ACM Journal on Emerging Technologies in Computing Systems 11, no. 1 (October 6, 2014): 1–16. http://dx.doi.org/10.1145/2629533.

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3

Gutiérrez, Alberto, Mert Atilhan, and Santiago Aparicio. "Molecular Modeling Analysis of CO2Absorption by Glymes." Journal of Physical Chemistry B 122, no. 6 (February 6, 2018): 1948–57. http://dx.doi.org/10.1021/acs.jpcb.7b10276.

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4

Boyle, A. "Polymer chain packing analysis using molecular modeling." Journal of Molecular Graphics 12, no. 3 (September 1994): 219–25. http://dx.doi.org/10.1016/0263-7855(94)80091-x.

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5

Chahibi, Youssef, Ian F. Akyildiz, and Ilangko Balasingham. "Propagation Modeling and Analysis of Molecular Motors in Molecular Communication." IEEE Transactions on NanoBioscience 15, no. 8 (December 2016): 917–27. http://dx.doi.org/10.1109/tnb.2016.2620439.

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6

Banks, H. T., N. S. Luke, and J. R. Samuels. "Viscoelasticity in polymers: Phenomenological to molecular mathematical modeling." Numerical Methods for Partial Differential Equations 23, no. 4 (2007): 817–31. http://dx.doi.org/10.1002/num.20250.

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7

Korendyasev, S. P., A. V. Firsova, D. M. Mordasov, and M. M. Mordasov. "Modeling and Fractal Analysis of Molecular Film Structures." Vestnik Tambovskogo gosudarstvennogo tehnicheskogo universiteta 23, no. 3 (2017): 527–34. http://dx.doi.org/10.17277/vestnik.2017.03.pp.527-534.

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8

Obiso, Jr., Richard J., David R. Bevan, and Tracy D. Wilkins. "Molecular Modeling and Analysis of Fragilysin, theBacteroides fragilisToxin." Clinical Infectious Diseases 25, s2 (September 1997): S153—S155. http://dx.doi.org/10.1086/516240.

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9

Ferreira-Júnior, José Ribamar, Lucas Bleicher, and Mario H. Barros. "Her2p molecular modeling, mutant analysis and intramitochondrial localization." Fungal Genetics and Biology 60 (November 2013): 133–39. http://dx.doi.org/10.1016/j.fgb.2013.06.006.

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10

Boonyapranai, Kongsak, Hsien-Yu Tsai, Miles Chih-Ming Chen, Supawadee Sriyam, Supachok Sinchaikul, Suree Phutrakul, and Shui-Tien Chen. "Glycoproteomic analysis and molecular modeling of haptoglobin multimers." ELECTROPHORESIS 32, no. 12 (June 2011): 1422–32. http://dx.doi.org/10.1002/elps.201000464.

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11

Coats, Eugene A., and James J. Knittel. "Correlation Analysis and Molecular Modeling of Cholecystokinin Inhibitors." Quantitative Structure-Activity Relationships 9, no. 2 (1990): 94–101. http://dx.doi.org/10.1002/qsar.19900090204.

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12

Fernández, Elmer Andrés, Carlos Alberto Perazzo, Rodolfo Valtuille, Peter Willshaw, and Mónica Balzarini. "Molecular Kinetics Modeling in Hemodialysis: On-Line Molecular Monitoring and Spectral Analysis." ASAIO Journal 53, no. 5 (September 2007): 582–86. http://dx.doi.org/10.1097/mat.0b013e318145bb31.

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13

Balasundaram, Arthi. "Molecular modeling and docking analysis of aspirin with pde7b." Bioinformation 16, no. 2 (February 29, 2020): 183–88. http://dx.doi.org/10.6026/97320630016183.

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14

Taylor, Robin, Graham W. Mullier, and Graham J. Sexton. "Automation of conformational analysis and other molecular modeling calculations." Journal of Molecular Graphics 10, no. 3 (September 1992): 152–60. http://dx.doi.org/10.1016/0263-7855(92)80049-j.

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15

Baranov, V. I., L. A. Gribov, and V. E. Dridger. "Computer modeling of standardless molecular spectral analysis of mixtures." Journal of Analytical Chemistry 67, no. 2 (February 2012): 114–21. http://dx.doi.org/10.1134/s1061934812020049.

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16

Hajihosseini, Morteza, Payam Amini, Dan Voicu, Irina Dinu, and Saumyadipta Pyne. "Geostatistical Modeling and Heterogeneity Analysis of Tumor Molecular Landscape." Cancers 14, no. 21 (October 25, 2022): 5235. http://dx.doi.org/10.3390/cancers14215235.

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Intratumor heterogeneity (ITH) is associated with therapeutic resistance and poor prognosis in cancer patients, and attributed to genetic, epigenetic, and microenvironmental factors. We developed a new computational platform, GATHER, for geostatistical modeling of single cell RNA-seq data to synthesize high-resolution and continuous gene expression landscapes of a given tumor sample. Such landscapes allow GATHER to map the enriched regions of pathways of interest in the tumor space and identify genes that have spatial differential expressions at locations representing specific phenotypic contexts using measures based on optimal transport. GATHER provides new applications of spatial entropy measures for quantification and objective characterization of ITH. It includes new tools for insightful visualization of spatial transcriptomic phenomena. We illustrate the capabilities of GATHER using real data from breast cancer tumor to study hallmarks of cancer in the phenotypic contexts defined by cancer associated fibroblasts.

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17

Nicolle, E., A. Boumendjel, S. Macalou, E. Genoux, A. Ahmed-Belkacem, P. A. Carrupt, and A. Di Pietro. "QSAR analysis and molecular modeling of ABCG2-specific inhibitors." Advanced Drug Delivery Reviews 61, no. 1 (January 2009): 34–46. http://dx.doi.org/10.1016/j.addr.2008.10.004.

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18

Weltman, Joel K., and George B. Loriot. "Molecular modeling of penicilloate anions: an RHF-SCF analysis." Journal of Molecular Modeling 9, no. 4 (August 1, 2003): 225–29. http://dx.doi.org/10.1007/s00894-003-0131-3.

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19

Morales Medina, Giovanni, and Ramiro Martínez Rey. "MOLECULAR AND MULTISCALE MODELING: REVIEW ON THE THEORIES AND APPLICATIONS IN CHEMICAL ENGINEERING." CT&F - Ciencia, Tecnología y Futuro 3, no. 5 (December 31, 2009): 205–23. http://dx.doi.org/10.29047/01225383.458.

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We call molecular modeling to the application of suitable laws in the analysis of phenomena occurred at scales less than those accounted for by the macroscopic world. Such different scales (including micro-, meso- and macroscales), can be linked and integrated in order to improve understanding and predictions of complex physical chemistry phenomena, thus originating a global or multiscale analysis. A considerable amount of chemical engineering phenomena are complex due to the interrelation among these different realms of length and time. Multiscale modeling rises as an alternative for an outstanding mathematical and conceptual representation of such phenomena. This adequate representation may help to design and optimize chemical and petrochemical processes from a microscopic point of view. Herein we present a brief introduction to both molecular and multiscale modeling methods. We also comment and examine opportunities for applying the different levels of modeling to the analysis of industrial problems. The fundamental mathematical machinery of the molecular modelling theories is presented in order to motivate the study of these new engineering tools. Finally, we show a classification of different strategies for applying multilevel analysis, illustrating various examples of each methodology.

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20

López-Ruiz, Ricardo. "Mathematical Biology: Modeling, Analysis, and Simulations." Mathematics 10, no. 20 (October 20, 2022): 3892. http://dx.doi.org/10.3390/math10203892.

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Mathematical biology has been an area of wide interest during the recent decades, as the modeling of complicated biological processes has enabled the creation of analytical and computational approaches to many different bio-inspired problems originating from different branches such as population dynamics, molecular dynamics in cells, neuronal and heart diseases, the cardiovascular system, genetics, etc [...]

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21

Majumder, Riddhi, Sujata Roy, and Ashoke Ranjan Thakur. "Analysis of Delta–Notch interaction by molecular modeling and molecular dynamic simulation studies." Journal of Biomolecular Structure and Dynamics 30, no. 1 (May 2012): 13–29. http://dx.doi.org/10.1080/07391102.2012.674184.

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22

Rakauskas, R. J., J. Šulskus, and S. Vošterienė. "PC Cluster Possibilities in Mathematical Modeling in Quantum Mechanical Molecular Computations." Nonlinear Analysis: Modelling and Control 7, no. 2 (December 5, 2002): 113–21. http://dx.doi.org/10.15388/na.2002.7.2.15197.

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We present the PC cluster built in the Department of Applied Sciences of Lithuanian Military Academy. The structure of the cluster is described and the performance is evaluated by solving of linear algebra testing tasks and nonlinear quantum chemistry molecular electronic structure computations.

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23

Belaidi, Salah, and Dalal Harkati. "Conformational Analysis in 18-Membered Macrolactones Based on Molecular Modeling." ISRN Organic Chemistry 2011 (April 19, 2011): 1–5. http://dx.doi.org/10.5402/2011/594242.

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Conformational analysis of 18-ring membered macrolactones has been carried out using molecular mechanics calculations and molecular dynamics. A high conformational flexibility of macrolactones was obtained, and an important stereoselectivity was observed for the complexed macrolides. For 18d macrolactone, which was presented by a most favored conformer with 20.1% without complex, it was populated with 50.1% in presence of Fe(CO)3.

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24

REGO, José, Jorddy CRUZ, Marcondes COSTA, Fabrine ALVES, Isaque MEDEIROS, Gleice PEREIRA, Maria SANTOS, Pabllo SANTOS, Alessandra LOPES, and Davi BRASIL. "ANALYSIS OF PURINIC ALKALOIDS BY XRD AND MOLECULAR MODELING METHODS." BOLETIM DO MUSEU DE GEOCIÊNCIAS DA AMAZÔNIA 8 (2021), no. 1 (May 6, 2021): 1–8. http://dx.doi.org/10.31419/issn.2594-942x.v82021i1a1jarr.

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Theophylline, theobromine and caffeine, are purine-based alkaloids in which the main differentiation in the molecular structure is the presence of methyls, one, two and three, respectively in these substances. This study presents an analysis by XRD and molecular modeling methods of the alkaloid’s caffeine and theobromine. The crystalline structure of caffeine was characterized as a monoclinic system, and the diffractogram of the caffeine crystals showed peaks with regions of greater intensity at 2θ = 11.7616 ° (d = 7.51 Å; I% = 80.13) and 2θ = 11.9416 ° (d = 7.40 Å; I% = 98.14). In the diffractogram of the theobromine crystal sample, peaks of greater intensity occurred in the regions 2θ = 13.4616 ° (d = 6.57 Å; I% = 98.92) and 2θ = 27.0816 ° (d = 3, 28 Å; I% = 67.23). Results obtained by XRD for caffeine and theobromine were compatible with standard cards of the X’Pert High Score Plus® program. The presence of an extra methyl in the structure of the caffeine purine base, suggests, a shift in the values of the angle 2 θ for the main peaks of theobromine, as well as an increase in intensity, mainly in 27.016, theobromine also presents a peak in the region 10.6 which does not occur in caffeine. Statistical results reveal that the linear models for data of peaks of specific angles in 2θ of the samples, presented good linear correlation (R2> 98%) and satisfactory results after the procedure of cross validation. caffeine and theobromine also showed important differences in interactions with adenosine A2AR, particularly in hydrophobic and hydrogen interactions.

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25

Yudina, M. N. "Software system for molecular networks of cells analysis and modeling." Omsk Scientific Bulletin, no. 162 (2018): 265–70. http://dx.doi.org/10.25206/1813-8225-2018-162-265-270.

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26

Temml, Veronika, and Zsofia Kutil. "Structure-based molecular modeling in SAR analysis and lead optimization." Computational and Structural Biotechnology Journal 19 (2021): 1431–44. http://dx.doi.org/10.1016/j.csbj.2021.02.018.

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27

Bicen, A. Ozan, and Ian F. Akyildiz. "Interference Modeling and Capacity Analysis for Microfluidic Molecular Communication Channels." IEEE Transactions on Nanotechnology 14, no. 3 (May 2015): 570–79. http://dx.doi.org/10.1109/tnano.2015.2418175.

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28

Kuscu, Murat, and Ozgur B. Akan. "Modeling and Analysis of SiNW FET-Based Molecular Communication Receiver." IEEE Transactions on Communications 64, no. 9 (September 2016): 3708–21. http://dx.doi.org/10.1109/tcomm.2016.2589935.

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29

Zhang, Jianhua, Zhigang Shang, Xiaohui Zhang, and Yuntao Zhang. "Modeling and analysis of Schistosoma Argonaute protein molecular spatial conformation." Asian Pacific Journal of Tropical Biomedicine 1, no. 4 (August 2011): 275–78. http://dx.doi.org/10.1016/s2221-1691(11)60042-7.

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30

Król, Dawid Jan, Artur Wymysłowski, and Kamil Nouri Allaf. "Adhesion work analysis through molecular modeling and wetting angle measurement." Microelectronics Reliability 55, no. 5 (April 2015): 758–64. http://dx.doi.org/10.1016/j.microrel.2015.02.006.

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31

Mikros, E. "Conformational analysis of asperlin by NMR spectroscopy and molecular modeling." Carbohydrate Research 294, no. 1-4 (November 20, 1996): 1–13. http://dx.doi.org/10.1016/s0008-6215(96)00201-7.

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32

Mikros, Emmanuel, Photis Dais, and Françoise Sauriol. "Conformational analysis of asperlin by NMR spectroscopy and molecular modeling." Carbohydrate Research 294 (November 1996): 1–13. http://dx.doi.org/10.1016/s0008-6215(96)90609-6.

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33

Zein, Haggag S., Jaime A. Teixeira da Silva, and Kazutaka Miyatake. "Structure–function analysis and molecular modeling of DNase catalytic antibodies." Immunology Letters 129, no. 1 (March 2010): 13–22. http://dx.doi.org/10.1016/j.imlet.2010.01.004.

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34

Zheng, Qiang, Rakefet Rosenfeld, and Donald J. Kyle. "Theoretical analysis of the multicopy sampling method in molecular modeling." Journal of Chemical Physics 99, no. 11 (December 1993): 8892–96. http://dx.doi.org/10.1063/1.465557.

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35

Raza, Muhammad Imran, Hajra Sadia, Sajid Mansoor, Attya Bhatti, Muhammad Ayaz Anwar, Peter John, Qurat Ul Ain Rana, and Ishtiaq Qadri. "Molecular modeling and mutational analysis of macrophage colony stimulating factor." Current Opinion in Biotechnology 22 (September 2011): S60. http://dx.doi.org/10.1016/j.copbio.2011.05.166.

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36

Yang, Chia-Wei, and Nien-Ti Tsou. "Microstructural analysis and molecular dynamics modeling of shape memory alloys." Computational Materials Science 131 (April 2017): 293–300. http://dx.doi.org/10.1016/j.commatsci.2017.02.011.

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37

Zhao, Qianqian, Weixiang Zhang, Runmiao Wang, Yitao Wang, and Defang Ouyang. "Research Advances in Molecular Modeling in Cyclodextrins." Current Pharmaceutical Design 23, no. 3 (February 20, 2017): 522–31. http://dx.doi.org/10.2174/1381612822666161208142617.

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Background: Cyclodextrins (CDs), as one type of the novel pharmaceutical excipients, have been widely used in drug delivery and pharmaceutical industry. Over the past decades, a large amount of molecular modeling studies in CDs were reported for profound understanding of structural, dynamic and energetic features of CDs systems. Thus, this review is focused on qualitative and quantitative analysis of research outputs on molecular modeling in CDs. Methods: The original data were collected from Web of Science and analyzed by scientific knowledge mapping tools, including Citespace, Science of Science, VOSviewer, GPSvisualizer and Gephi software. Scientific knowledge mapping, as an emerging approach for literature analysis, was employed to identify the knowledge structure and capture the development of the science in a visual way. Results: The results of analysis included research outputs landscape, collaboration patterns, knowledge structure and research frontiers shift with time. China had the largest contributions to the publication number in this area, while USA dominated the high quality research outputs. International collaboration between USA and Europe was much stronger than that within Asia. J American Chemical Society, as one of the most important journals, played a pivotal role in linking different research fields. Furthermore, seven important thematic clusters were identified by the research cluster analysis with visualization tools and demonstrated from three different perspectives including: (1) the mostly-used CD molecules: β-Cyclodextrin, (2) preferred modeling tools: docking calculation and molecular dynamic, (3) hot research fields: structural properties, solubility, chiral recognition and solidstate inclusion complexes. Moreover, research frontier shift in the past three decades was traced by detecting keywords bursts with high citation. Conclusion: The current review provided us a macro-perspective and intellectual landscape to molecular modeling in CDs.

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38

Soni, Sangeeta, Chetna Tyagi, Abhinav Grover, and Shyamal K. Goswami. "Molecular modeling and molecular dynamics simulations based structural analysis of the SG2NA protein variants." BMC Research Notes 7, no. 1 (2014): 446. http://dx.doi.org/10.1186/1756-0500-7-446.

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39

Qin, Jin, Beilei Lei, Lili Xi, Huanxiang Liu, and Xiaojun Yao. "Molecular modeling studies of Rho kinase inhibitors using molecular docking and 3D-QSAR analysis." European Journal of Medicinal Chemistry 45, no. 7 (July 2010): 2768–76. http://dx.doi.org/10.1016/j.ejmech.2010.02.059.

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40

Zou, Jian, Wentao Liang, and Sulin Zhang. "Coarse-grained molecular dynamics modeling of DNA-carbon nanotube complexes." International Journal for Numerical Methods in Engineering 83, no. 8-9 (August 19, 2010): 968–85. http://dx.doi.org/10.1002/nme.2819.

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41

Maris, Kurniawati, Amak Y. E. Prasetyo, Subandi ., and Suharti . "Analysis of Molecular Modeling and Molecular Docking of Beta-glucanase from Metagenomic Expression Library as Candida Antibiofilm Candidate." INTERNATIONAL JOURNAL OF DRUG DELIVERY TECHNOLOGY 12, no. 03 (June 30, 2022): 929–35. http://dx.doi.org/10.25258/ijddt.12.3.01.

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MOvergrowth of Candida tends to produce high levels of secondary metabolites affecting the immersion of infectious and degenerative diseases. Biofilm’s existence as a virulence factor of Candida makes it challenging to overcome causing multidrugresistant issues. Studies on the effectiveness of Candida antibiofilm drug candidates should be supported by data related to model structure and molecular interaction within the eradication process of biofilm through homology modeling and in-silico docking. This study aims to determine molecular interactions between 1,3-β-glucanase Achatina fulica in which the substrate is, through homology modeling and docking studies within the biofilm matrix eradication process.

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42

PAL, Ria. "Molecular Modeling on Structure-Function Analysis of Human Progesterone Receptor Modulators." Scientia Pharmaceutica 79, no. 3 (2011): 461–77. http://dx.doi.org/10.3797/scipharm.1105-03.

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43

Seki, Y., and K. Soda. "Structural Analysis of Acid-unfolded Myoglobin by a Molecular Modeling Method." Seibutsu Butsuri 41, supplement (2001): S174. http://dx.doi.org/10.2142/biophys.41.s174_4.

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44

dos Santos, Cleydson, Cleison Lobato, Francinaldo Braga, Josivan Costa, Hugo Favacho, Jose Carvalho, Williams Macedo, Davi Brasil, Carlos da Silva, and Lorane da Silva Hage-Melim. "Rational Design of Antimalarial Drugs Using Molecular Modeling and Statistical Analysis." Current Pharmaceutical Design 21, no. 28 (September 22, 2015): 4112–27. http://dx.doi.org/10.2174/1381612821666150528121423.

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45

Chakraborty, Raja, Sayak Ganguli, Hirak Jyoti Chakraborty, and Abhijit Datta. "STRUCTURAL ANALYSIS AND MOLECULAR MODELING OF HUMAN DOPAMINE RECEPTOR 5 (DRD5)." International Journal of Bioinformatics Research 2, no. 2 (December 30, 2010): 96–102. http://dx.doi.org/10.9735/0975-3087.2.2.96-102.

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46

Pandey, Bharati, Pradeep Sharma, Chetna Tyagi, Sukriti Goyal, Abhinav Grover, and Indu Sharma. "Structural modeling and molecular simulation analysis of HvAP2/EREBP from barley." Journal of Biomolecular Structure and Dynamics 34, no. 6 (October 19, 2015): 1159–75. http://dx.doi.org/10.1080/07391102.2015.1073630.

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47

Mathieu, Axel P., Pierre Lavigne, and Jean-Guy LeHoux. "MOLECULAR MODELING AND STRUCTURE-BASED THERMODYNAMIC ANALYSIS OF THE StAR PROTEIN." Endocrine Research 28, no. 4 (January 2002): 419–23. http://dx.doi.org/10.1081/erc-120016817.

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48

Udrescu, Lucreția, Laura Sbârcea, Adriana Fuliaș, Ionuț Ledeți, Gabriela Vlase, Paul Barvinschi, and Ludovic Kurunczi. "Physicochemical Analysis and Molecular Modeling of the Fosinoprilβ-Cyclodextrin Inclusion Complex." Journal of Spectroscopy 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/748468.

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This research investigates the interaction between fosinopril sodium (FOS) and beta-cyclodextrin (β-CD) in aqueous solution and in solid state, in order to prove the formation of an inclusion complex between the two components. The stoichiometry of the inclusion complex was found as 1 : 1 by employing continuous variation method in UV. The formation constant was calculated as 278.93 M−1using Benesi-Hildebrand equation. The kneaded product (KP) and the physical mixture (PM) were further experimentally examined, using FTIR, powder X-ray diffractometry, and thermal analysis. The results confirm that the physicochemical properties of the FOS/β-CD KP are different from FOS and that the kneading method leads to formation of solid state inclusion complex between FOS andβ-CD. Structural studies of the FOS/β-CD were carried out using molecular modeling techniques, in order to explain the complexation mechanism and the host-guest geometry.

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49

Masoud, Mamdouh S., Amr M. Beltagi, and Hany A. Moutawa. "Synthesis, spectral, molecular modeling, thermal analysis studies of orange (II) complexes." Journal of Molecular Structure 1175 (January 2019): 335–45. http://dx.doi.org/10.1016/j.molstruc.2018.07.094.

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50

Leal-Pinto, E., B. E. Cohen, and R. G. Abramson. "Functional Analysis and Molecular Modeling of a Cloned Urate Transporter/Channel." Journal of Membrane Biology 169, no. 1 (May 1, 1999): 13–27. http://dx.doi.org/10.1007/pl00005897.

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