[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Journal Information::
Articles archive::
Publication Ethics::
Peer Review Process::
Indexing Databases::
For Authors::
For Reviewers::
Subscription::
Contact us::
Site Facilities::
::
Google Scholar Metrics

Citation Indices from GS

AllSince 2020
Citations71733701
h-index2920
i10-index20479

..
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
Registered in

AWT IMAGE

AWT IMAGE

..
:: Volume 33, Issue 2 (5-2025) ::
Journal of Ilam University of Medical Sciences 2025, 33(2): 26-44 Back to browse issues page
Quantitative structure-activity relationship study of some angiotensin-converting enzyme inhibitor drugs in the treatment of hypertension based on Monte Carlo optimization method
Shahram Lotfi *1 , Shahin Ahmadi2 , Ali Azimi3
1- Dept of Chemistry, Payame Noor University (PNU), Tehran, Iran , Sh.lotfi@pnu.ac.ir
2- Dept of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
3- Dept of Chemistry, Science and Research Branch, Islamic Azad University, Tehran, Iran
Abstract:   (38 Views)
Introduction:  Hypertension is a severe cardiovascular disease affecting human health, and its control and prevention are crucial for global health. Angiotensin-converting-enzyme inhibitors are primarily used in treating this condition. The aim of this work was to develop quantitative structure-activity relationship (QSAR) models to predict the activity of some chemical compounds as angiotensin I-converting enzyme inhibitors using CORAL software.
Materials & Methods: In this study, quantitative structure-activity relationship to predict the inhibitory activity of the data set containing 255 angiotensin-converting enzyme inhibitor compounds based on the algorithm Monte Carlo was studied. The input file of CORAL software contains the structures of compounds with SMILES symbols along with their inhibitory activity, which were randomly divided into four sets, including active training, passive training, calibration and validation sets. The whole data set is randomly divided into three splits, and an individual model was created for each division.
Results: A hybrid optimal descriptor computed from SMILES and molecular hydrogen-suppressed graphs is employed to construct QSAR models. Four target functions, i.e., TF0 (WIIC = WCII = 0), TF1 (WIIC = 0.3 and WCII = 0), TF2 (WIIC = 0 and WCII = 0.3), and TF3 (WIIC = WCII = 0.3), are employed to build 12 QSAR models.
Conclusion: The study analyzed experimental data on chemical compounds as inhibitors of angiotensin I-converting enzyme. The QSAR model of split 2 calculated by TF3 was found to be the best model, and it identified structural attributes responsible for the increase and decrease of inhibitory activity.
Keywords: QSAR models, Angiotensin-converting enzyme inhibitors, Hypertension disease, CORAL software
Full-Text [PDF 1157 kb]   (6 Downloads)    
Type of Study: Research | Subject: biostatistics
Received: 2024/12/23 | Accepted: 2025/03/10 | Published: 2025/05/26
References
1. Jao CL, Huang SL, Hsu KU. Angiotensin I-converting enzyme inhibitory peptides: Inhibition mode, bioavailability, and antihypertensive effects. BioMedicine. 2012; 2: 130-6. doi: 10.1016/j.biomed.2012.06.005.
2. Amaya, J.A.G., et al., In-silico design of new enalapril analogs (ACE inhibitors) using QSAR and molecular docking models. Inform Med Unlocked. 2020; 19: 100336. doi:10.1016/j.imu.2020.100336.
3. Cushman, D.W., et al., Design of potent competitive inhibitors of angiotensin-converting enzyme. Carboxyalkanoyl and mercaptoalkanoyl amino acids. Biochemistry. 1977; 16: 5484-91. doi:10.1021/bi00644a014.
4. Lin K, Zhang L, Han X, Meng Z, Zhang J, Wu Y, Cheng D. Novel angiotensin I-converting enzyme inhibitory peptides from protease hydrolysates of Qula casein: Quantitative structure-activity relationship modeling and molecular docking study. J Agric Food Chem. 2017; 32: 266-77. doi:10.1016/j.jff.2017.03.008.
5. Yılmaz, İ., Angiotensin-converting enzyme inhibitors induce cough. Turk Thorac J. 2019; 20: 36-42. doi: 10.5152/TurkThoracJ.2018.18014.
6. Vena GA, Cassano N, Coco V, De Simone C. Eczematous reactions due to angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers. Immunopharmacol Immunotoxicol. 2013; 35: 447-50. doi: 10.3109/08923973.2013.797992.
7. Murray BA, FitzGerald RJ. Angiotensin converting enzyme inhibitory peptides derived from food proteins: biochemistry, bioactivity and production. Curr Pharm Des. 2007; 13: 773-91. doi: 10.2174/138161207780363068.
8. Wu Q, Cai QF, Tao ZP, et al. Purification and characterization of a novel angiotensin I-converting enzyme inhibitory peptide derived from abalone (Haliotis discus hannai Ino) gonads. Eur Food Res Technol. 2015; 240: 137-45. doi: 10.1016/j.peptides.2011.11.006.
9. Lorenz S, et al. Toward application and implementation of in silico tools and workflows within benign by design approaches. ACS Sustain Chem Eng. 2021; 9: 12461-75. doi:10.1021/acssuschemeng.1c03070.
10. Zhao P, Peng Y, Xu X, Wang Z, Wu Z, Li W, Tang Y, Liu G. In silico prediction of mitochondrial toxicity of chemicals using machine learning methods. J Appl Toxicol. 2021; 41: 1518-26. doi: 10.1002/jat.4141.
11. Azimi A, Ahmadi Sh, Kumar A, Qomi M, Almasirad A, et al. SMILES-Based QSAR and Molecular Docking Study of Oseltamivir Derivatives as Influenza Inhibitors. PACs. 2022;43: 1-21. doi:10.1080/10406638.2022.2067194.
12. Ahmadi S, Ghanbari H, Lotfi S, Azimi N. Predictive QSAR modeling for the antioxidant activity of natural compounds derivatives based on Monte Carlo method. Mol Divers. 2021; 25: 87-97. doi:10.1007/s11030-019-10026-9.
13. Ahmadi S, Khani R, Moghaddas M. Prediction of anti-cancer activity of 1, 8-naphthyridin derivatives by using of genetic algorithm-stepwise multiple linear regression.Med Sci. 2018;28:181-94. doi:10.29252/iau.28.3.181.
14. Lotfi S, Ahmadi S, Zohrabi P. QSAR modeling of toxicities of ionic liquids toward Staphylococcus aureus using SMILES and graph invariants. Struct Chem. 2020; 31: 2257-70. doi: 10.1007/s11224-020-01568-y
15. Ahmadi S, Moradi Z, Kumar A, Almasirad A. SMILES-based QSAR and molecular docking study of xanthone derivatives as α-glucosidase inhibitors. J Recept Signal Transduct Res. 2021; 42:361-72. 1-12. doi:10.1080/10799893.2021.1957932.
16. rabhakar YS, Gupta SP. Structure-Activity relationship study on angiotensin-converting enzyme inhibitors--investigation of hydrophobic interaction in inhibition mechanism. Indian J Biochem Biophys. 1985, 22: 318-20.
17. Javidfar M, Ahmadi S. QSAR modelling of larvicidal phytocompounds against Aedes aegypti using index of ideality of correlation. SAR QSAR Environ Res. 2020; 31: 717-39. doi:10.1080/1062936X.2020.1806922.
18. Deokar H, Deokar M, Wang W, Zhang R, Buolamwini JK. QSAR studies of new pyrido [3, 4-b] indole derivatives as inhibitors of colon and pancreatic cancer cell proliferation. Med Chem Res. 2018; 27: 2466-81. doi: 10.1007/s00044-018-2250-5.
19. Lotfi S, Ahmadi S, Kumar P. Correction: Ecotoxicological prediction of organic chemicals toward Pseudokirchneriella subcapitata by Monte Carlo approach. RSC Adv. 2022; 12: 34567. doi:10.1039/D2RA03936B.
20. Toropov AA, Toropova AP, Roncaglioni A, Benfenati E. The system of self-consistent models for pesticide toxicity to Daphnia magna. Toxicol Mech Methods. 2023;33: 578-83. doi:10.1080/15376516.2023.2197487.
21. Toropova AP, Toropov AA, Roncaglioni A, Benfenati E. The index of ideality of correlation improves the predictive potential of models of the antioxidant activity of tripeptides from frog skin (Litoria rubella). Comput Biol Med. 2021; 133: 104370. doi:10.1016/j.compbiomed.2021.104370.
22. Lotfi S, Ahmadi Sh, Kumar P. A hybrid descriptor based QSPR model to predict the thermal decomposition temperature of imidazolium ionic liquids using Monte Carlo approach. J Mol Liq. 2021; 338: 116465. doi:10.1016/j.molliq.2021.116465.
23. Kumar P, Kumar A. In silico enhancement of azo dye adsorption affinity for cellulose fibre through mechanistic interpretation under guidance of QSPR models using Monte Carlo method with index of ideality correlation. SAR QSAR Environ Res. 2020; 31: 697-715. doi:10.1080/1062936X.2020.1806105.
24. Toropov AA, Toropova AP, Achary PGR, Raškova M, Raška I. The searching for agents for Alzheimer’s disease treatment via the system of self-consistent models. Toxicology Toxicol Mech Methods. 2022; 32: 549-557. doi: 10.1080/15376516.2022.2053918.
25. Shah S, Chaple D, Masand VH, Zaki MEA, Al-Hussain SA, Shah A,et al. In silico study to recognize novel angiotensin-converting-enzyme-I inhibitors by 2D-QSAR and constraint-based molecular simulations. J Biomol Struct Dyn. 2023; 43:2211-30. doi: 10.1080/07391102.2023.220326.
26. Duhan M, Sindhu J, Kumar P, Devi M, Singh R, Kumar R, et al. Quantitative structure activity relationship studies of novel hydrazone derivatives as α-amylase inhibitors with index of ideality of correlation. J Biomol Struct Dyn. 2020; 40: 4933-53. doi: 10.1080/07391102.2020.1863861.
27. Achary PGR, Toropova AP, Toropov AA. Combinations of graph invariants and attributes of simplified molecular input-line entry system (SMILES) to build up models for sweetness. Food Res Int. 2019; 122: 40-46. doi:10.1016/j.foodres.2019.03.067.
28. Roy PP, Roy K. QSAR studies of CYP2D6 inhibitor aryloxypropanolamines using 2D and 3D descriptors. Chem Biol Drug Des. 2009; 73: 442-55. doi: 10.1111/j.1747-0285.2009.00791.x.
29. Lotfi S, Ahmadi S, Kumar P. The Monte Carlo approach to model and predict the melting point of imidazolium ionic liquids using hybrid optimal descriptors. RSC Adv. 2021; 11: 33849-57. doi:10.1039/D1RA06861J.
30. Ahmadi S, Ketabi S, Qomi M. CO 2 uptake prediction of metal–organic frameworks using quasi-SMILES and Monte Carlo optimization. New J Chem. 2022; 46: 8827-37. doi:10.1039/D2NJ00596D.
31. Kumar P, Kumar A. CORAL: QSAR models of CB1 cannabinoid receptor inhibitors based on local and global SMILES attributes with the index of ideality of correlation and the correlation contradiction index. Chemometr Intell Lab Sys. 2020; 200: 103982. doi:10.1016/j.chemolab.2020.103982.
32. Toropova AP, Toropov AA, Veselinović AM, Veselinović JB, Benfenati E, Leszczynska D, et al. Nano-QSAR: Model of mutagenicity of fullerene as a mathematical function of different conditions. Ecotoxicol Environ Saf. 2016; 124: 32-36. doi:10.1016/j.ecoenv.2015.09.038.
33. Tabti K, et al. HQSAR, CoMFA, CoMSIA docking studies and simulation MD on quinazolines/quinolines derivatives for DENV virus inhibitory activity. Chem Afr. 2022; 5: 1937-58. doi:10.1007/s42250-022-00484-4.
34. Chirico N, Gramatica P. Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection. J Chem Inf Model. 2012; 52: 2044-58. doi:10.1021/ci300084j.
35. Rücker C, Rücker G, Meringer M. Y-randomization–a useful tool in QSAR validation, or folklore. J Chem Inf Model. 2007; 47: 2345-57. doi:10.1021/ci700157b.
36. Yordanova D, W. Schultz T, Kuseva C, Tankova K, Ivanova H. Automated and standardized workflows in the OECD QSAR Toolbox. ComputToxicol. 2019; 10: 89-104. doi:10.1016/j.comtox.2019.01.006.
37. Gatidou G, Vazaiou N, Thomaidis NS, Stasinakis AS. Biodegradability assessment of food additives using OECD 301F respirometric test. Chemosphere. 2020; 241: 125071. doi:10.1016/j.chemosphere.2019.125071.
38. Shah S, Chaple D, Masand VH, Zaki MEA, Al-Hussain SA, Shah A. In silico study to recognize novel angiotensin-converting-enzyme-I inhibitors by 2D-QSAR and constraint-based molecular simulations. J Biomol Struct Dyn. 2024; 42: 2211-30. doi: 10.1080/07391102.2023.220326.
39. Roy K, Chakraborty P, Mitra I, Ojha PK, Kar S, Das RN. Some case studies on application of “rm2” metrics for judging quality of quantitative structure–activity relationship predictions: emphasis on scaling of response data. J Comput Chem. 2013; 34: 1071-82. doi.org/10.1002/jcc.23231.
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA

Ethics code: 1


XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Lotfi S, Ahmadi S, Azimi A. Quantitative structure-activity relationship study of some angiotensin-converting enzyme inhibitor drugs in the treatment of hypertension based on Monte Carlo optimization method. J. Ilam Uni. Med. Sci. 2025; 33 (2) :26-44
URL: http://sjimu.medilam.ac.ir/article-1-8498-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 33, Issue 2 (5-2025) Back to browse issues page
مجله دانشگاه علوم پزشکی ایلام Journal of Ilam University of Medical Sciences
Persian site map - English site map - Created in 0.16 seconds with 41 queries by YEKTAWEB 4701