Molecular modeling as a stage of searching for new substances with potential therapeutic significance
 
More details
Hide details
1
Zakład Statystyki, Wydział Nauk Farmaceutycznych w Sosnowcu, Śląski Uniwersytet Medyczny w Katowicach
 
 
Corresponding author
Elżbieta Chełmecka   

Zakład Statystyki, Wydział Nauk Farmaceutycznych w Sosnowcu, Śląski Uniwersytet Medyczny w Katowicach, ul. Ostrogórska 30, 41-200 Sosnowiec
 
 
Ann. Acad. Med. Siles. 2020;74:91-98
 
KEYWORDS
TOPICS
ABSTRACT
The paper is a review of the literature on molecular modeling, with particular emphasis on the use of modern in silico methods in the early stages of designing new medical substances. Its purpose is to discuss the significance and justification of using computer software in the process of creating new drugs. Therefore, the stages through which a compound must pass so that it can be considered as a good drug candidate were presented, and the subsequent stages in the process of searching for substances using molecular modeling methods were discussed. It has been demonstrated that molecular modeling can be a useful tool in the process of designing medicinal substances, as well as an important factor reducing the costs and shortening the time spent researching a new drug. Due to the considerable effectiveness of computer methods, work should be carried out in their further development.
 
REFERENCES (24)
1.
Wałęsa R., Broda M.A. Rola modelowania molekularnego w procesie poszukiwania nowych substancji chemicznych o potencjalnym znaczeniu terapeutycznym. Lek w Polsce 2014; 24(16): 46–49.
 
2.
Nadendla R.R. Molecular modeling: a powerful tool for drug design and molecular docking. Resonance 2004; 9(5): 51–60.
 
3.
Kore P.P., Mutha M.M., Antre R.V., Oswal R.J., Kshirsagar S.S. Computer-aided drug design: an innovative tool for modeling. OJMC 2012; 2(4): 139–148, doi: 10.4236/ojmc.2012.24017.
 
4.
Ooms F. Molecular modeling and computer aided drug design. Examples of their applications in medicinal chemistry. Curr. Med. Chem. 2000; 7(2): 141–158.
 
5.
Messaoudi A., Belguith H., Ben Hamida J. Homology modeling and virtual screening approaches to identify potent inhibitors of VEB-1 β-lactamase. Theor. Biol. Med. Model. 2013; 10: 22–32, doi: 10.1186/1742-4682-10-22.
 
6.
DiMasi J.A., Grabowski H.G., Hansen R.W. Innovation in the pharmaceutical industry: New estimates of R&D costs. J. Health Econ. 2016; 47: 20–33, doi: 10.1016/j.jhealeco.2016.01.012.
 
7.
Bodera P. Tworzenie nowych leków: miejsca docelowe i receptory. Czas. Aptek. 2009; 1(181): 13–20.
 
8.
Moses H. 3rd, Matheson D.H., Cairns-Smith S., George B.P., Palisch C., Dorsey E.R. The anatomy of medical research: US and international comparisons. JAMA 2015; 313(2): 174–189, doi: 10.1001/jama.2014.15939.
 
9.
Mahan V.L. Clinical Trial Phases. IJCM 2014; 5(21): 1374–1383, doi: 10.4236/ijcm.2014.521175.
 
10.
Bielenica A., Kossakowski J. Zastosowanie metod obliczeniowych do wyznaczania budowy modeli farmakoforowych receptorów 5-HT1A, 5-HT2A oraz 5HT7. Biul. Wydz. Farm. WUM 2010; 1: 1–12.
 
11.
Cortés A., Moreno E., Rodríguez-Ruiz M., Canela E.I., Casadó V. Targeting the dopamine D3 receptor: an overview of drug design strategies. Expert Opin. Drug Discov. 2016; 11(7): 641–664, doi: 10.1080/17460441.2016.1185413.
 
12.
Hansch L., Leo A., Hoekman D. Exploring QSAR: Hydrophobic, Electronic, and Steric Constants. American Chemical Society, Washington, DC, 1995.
 
13.
de Ruyck J., Brysbaert G., Blossey R., Lensink M.F. Molecular docking as a popular tool in drug design, an in silico travel. Adv. Appl. Bioinform. Chem. 2016; 9: 1–11, doi: 10.2147/AABC.S105289.
 
14.
Phenix, online, https://www.phenix-online.org [Dostęp: 27.03.2020].
 
15.
Dali. Protein Structure Comparison Server, online, http://ekhidna2.biocenter.hels... [Dostęp: 27.03.2020].
 
16.
OmicX, online, https://omictools.com/ligsitec... [Dostęp: 27.03.2020].
 
17.
GeneXplain, online, http://genexplain.com/pass [Dostęp: 27.03.2020].
 
18.
Ferreira L.G., Dos Santos R.N., Oliva G., Andricopulo A.D. Molecular docking and structure-based drug design strategies. Molecules 2015; 20(7): 13384–13421, doi: 10.3390/molecules200713384.
 
19.
Gruca A. Bioinformatyczne bazy danych. Wydawnictwo PJWSTK. Warszawa 2010, s. 1–7.
 
20.
Eweas A.F., Maghrabi I.A., Namarneh A.I. Advances in molecular modeling and docking as a tool for modern drug discovery. Der Pharma Chemica 2014; 6(6): 211–228.
 
21.
Jagieła D., Łuczak S. Modelowanie w chemii. Laborant 2011; 3: 27–30.
 
22.
Huang P.S., Boyken S.E., Baker D. The coming of age of de novo protein design. Nature 2016; 537(7620): 320–327, doi: 10.1038/nature19946.
 
23.
Rodrigues T., Hauser N., Reker D., Reutlinger M., Wunderlin T., Hamon J., Koch G., Schneider G. Multidimensional de novo design reveals 5-HT2B receptor-selective ligands. Angew. Chem. Int. Ed. Engl. 2015; 54(5): 1551–1555, doi: 10.1002/anie.201410201.
 
24.
Lipinski C.A. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov. Today Technol. 2004; 1(4): 337–341, doi: 10.1016/j.ddtec.2004.11.007.
 
eISSN:1734-025X
Journals System - logo
Scroll to top