Quantitative structure-activity relationship to predict the anti-malarial activity in a set of new imidazolopiperazines based on artificial neural networks.
Antimalarial
Artificial neural networks
Imidazolopiperazine
QSAR
Journal
Malaria journal
ISSN: 1475-2875
Titre abrégé: Malar J
Pays: England
ID NLM: 101139802
Informations de publication
Date de publication:
14 Sep 2019
14 Sep 2019
Historique:
received:
30
05
2019
accepted:
27
08
2019
entrez:
16
9
2019
pubmed:
16
9
2019
medline:
27
12
2019
Statut:
epublish
Résumé
After years of efforts on the control of malaria, it remains as a most deadly infectious disease. A major problem for the available anti-malarial drugs is the occurrence of drug resistance in Plasmodium. Developing of new compounds or modification of existing anti-malarial drugs is an effective approach to face this challenge. Quantitative structure activity relationship (QSAR) modelling plays an important role in design and modification of anti-malarial compounds by estimation of the activity of the compounds. In this research, the QSAR study was done on anti-malarial activity of 33 imidazolopiperazine compounds based on artificial neural networks (ANN). The structural descriptors of imidazolopiperazine molecules was used as the independents variables and their activity against 3D7 and W2 strains was used as the dependent variables. During modelling process, 70% of compound was used as the training and two 15% of imidazolopiperazines were used as the validation and external test sets. In this work, stepwise multiple linear regression was applied as the valuable selection and ANN with Levenberg-Marquardt algorithm was utilized as an efficient non-linear approach to correlate between structural information of molecules and their anti-malarial activity. The sufficiency of the suggested method to estimate the anti-malarial activity of imidazolopiperazine compounds at two 3D7 and W2 strains was demonstrated using statistical parameters, such as correlation coefficient (R QSAR can be an efficient way to virtual screening the molecules to design more efficient compounds with activity against malaria (3D7 and W2 strains). Imidazolopiperazines can be good candidates and change in the structure and functional groups can be done intelligently using QSAR approach to rich more efficient compounds with decreasing trial-error runs during synthesis.
Sections du résumé
BACKGROUND
BACKGROUND
After years of efforts on the control of malaria, it remains as a most deadly infectious disease. A major problem for the available anti-malarial drugs is the occurrence of drug resistance in Plasmodium. Developing of new compounds or modification of existing anti-malarial drugs is an effective approach to face this challenge. Quantitative structure activity relationship (QSAR) modelling plays an important role in design and modification of anti-malarial compounds by estimation of the activity of the compounds.
METHODS
METHODS
In this research, the QSAR study was done on anti-malarial activity of 33 imidazolopiperazine compounds based on artificial neural networks (ANN). The structural descriptors of imidazolopiperazine molecules was used as the independents variables and their activity against 3D7 and W2 strains was used as the dependent variables. During modelling process, 70% of compound was used as the training and two 15% of imidazolopiperazines were used as the validation and external test sets. In this work, stepwise multiple linear regression was applied as the valuable selection and ANN with Levenberg-Marquardt algorithm was utilized as an efficient non-linear approach to correlate between structural information of molecules and their anti-malarial activity.
RESULTS
RESULTS
The sufficiency of the suggested method to estimate the anti-malarial activity of imidazolopiperazine compounds at two 3D7 and W2 strains was demonstrated using statistical parameters, such as correlation coefficient (R
CONCLUSION
CONCLUSIONS
QSAR can be an efficient way to virtual screening the molecules to design more efficient compounds with activity against malaria (3D7 and W2 strains). Imidazolopiperazines can be good candidates and change in the structure and functional groups can be done intelligently using QSAR approach to rich more efficient compounds with decreasing trial-error runs during synthesis.
Identifiants
pubmed: 31521174
doi: 10.1186/s12936-019-2941-5
pii: 10.1186/s12936-019-2941-5
pmc: PMC6744662
doi:
Substances chimiques
Antimalarials
0
Imidazoles
0
Piperazines
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
310Subventions
Organisme : Shiraz University of Medical Sciences
ID : 98-01-42-20527
Références
J Chem Inf Comput Sci. 2001 Sep-Oct;41(5):1218-27
pubmed: 11604021
Bioorg Med Chem. 2001 Dec;9(12):3287-93
pubmed: 11711304
Bioorg Med Chem. 2002 Sep;10(9):2883-91
pubmed: 12110308
J Chem Inf Comput Sci. 2004 Jan-Feb;44(1):1-12
pubmed: 14741005
J Chem Inf Model. 2005 Jul-Aug;45(4):839-49
pubmed: 16045276
Altern Lab Anim. 2005 Apr;33(2):155-73
pubmed: 16180989
Bioorg Med Chem. 2006 Apr 1;14(7):2333-57
pubmed: 16426851
Br J Pharmacol. 2007 Sep;152(1):21-37
pubmed: 17549046
J Mol Model. 2008 Jan;14(1):39-48
pubmed: 17968600
J Med Chem. 2011 Jul 28;54(14):5116-30
pubmed: 21644570
J Med Chem. 2012 May 10;55(9):4244-73
pubmed: 22524250
Molecules. 2012 Apr 25;17(5):4791-810
pubmed: 22534664
Prog Med Chem. 2013;52:97-151
pubmed: 23384667
Bioorg Med Chem Lett. 2013 May 15;23(10):2829-43
pubmed: 23587422
Nat Rev Microbiol. 2013 Dec;11(12):849-62
pubmed: 24217412
Antimicrob Agents Chemother. 2014 Nov;58(11):6437-43
pubmed: 25136017
Comb Chem High Throughput Screen. 2015;18(2):91-128
pubmed: 25543681
Int J Appl Sci Technol. 2014 Oct;4(5):9-19
pubmed: 25664257
Mol Inform. 2014 Apr;33(4):311-4
pubmed: 27485777
Eur J Med Chem. 2017 Jan 5;125:1300-1320
pubmed: 27886547
Ann Acad Med Singapore. 2018 Apr;47(4):135-137
pubmed: 29777242