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Current Computer-Aided Drug Design

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ISSN (Print): 1573-4099
ISSN (Online): 1875-6697

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Research Article

Relevance of Machine Learning to Predict the Inhibitory Activity of Small Thiazole Chemicals on Estrogen Receptor

Author(s): Venkatesan Jayaprakash, Thangavelu Saravanan, Karuppaiyan Ravindran, Thangavelu Prabha*, Jubie Selvaraj, Sudeepan Jayapalan, M.V.N.L. Chaitanya and Thangavel Sivakumar

Volume 19, Issue 1, 2023

Published on: 15 December, 2022

Page: [37 - 50] Pages: 14

DOI: 10.2174/1573409919666221121141646

Price: $65

Abstract

Background: Drug discovery requires the use of hybrid technologies for the discovery of new chemical substances. One of those interesting strategies is QSAR via applying an artificial intelligence system that effectively predicts how chemical alterations can impact biological activity via in-silico.

Aim: Our present study aimed to work on a trending machine learning approach with a new opensource data analysis python script for the discovery of anticancer lead via building the QSAR model by using 53 compounds of thiazole derivatives.

Methods: A python script has been executed with 53 small thiazole chemicals using Google collaboratory interface. A total of 82 CDK molecular descriptors were downloaded from “chemdes” web server and used for our study. After training the model, we checked the model performance via cross-validation of the external test set.

Results: The generated QSAR model afforded the ordinary least squares (OLS) regression as R2 = 0.542, F=8.773, and adjusted R2 (Q2) =0.481, std. error = 0.061, reg.coef_ developed were of, - 0.00064 (PC1), -0.07753 (PC2), -0.09078 (PC3), -0.08986 (PC4), 0.05044 (PC5), and reg.intercept_ of 4.79279 developed through stats models, formula module. The performance of test set prediction was done by multiple linear regression, support vector machine, and partial least square regression classifiers of sklearn module, which generated the model score of 0.5424, 0.6422 and 0.6422 respectively.

Conclusion: Hence, we conclude that the R2values (i.e. the model score) obtained using this script via three diverse algorithms were correlated well and there is not much difference between them and may be useful in the design of a similar group of thiazole derivatives as anticancer agents.

Keywords: QSAR, python, supervised machine learning, thiazole derivatives, MCF-7, breast cancer, cytotoxicity.

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[1]
Bray, F.; Ferlay, J.; Soerjomataram, I.; Siegel, R.L.; Torre, L.A.; Jemal, A. Global Cancer Statistics 2018 GLOBOCAN estimates of incidence and mortalityworldwide for 36 cancers in 185 countries. Cancer J Clin., 2018, 68, 394-424.
[2]
Cherkasov, A.; Muratov, E.N.; Fourches, D.; Varnek, A.; Baskin, I.I.; Cronin, M.; Dearden, J.; Gramatica, P.; Martin, Y.C.; Todeschini, R.; Consonni, V.; Kuz’min, V.E.; Cramer, R.; Benigni, R.; Yang, C.; Rathman, J.; Terfloth, L.; Gasteiger, J.; Richard, A.; Tropsha, A. QSAR modeling: where have you been? Where are you going to? J. Med. Chem., 2014, 57(12), 4977-5010.
[http://dx.doi.org/10.1021/jm4004285] [PMID: 24351051]
[3]
Thangavelu, P.; Thangavel, S. Design, synthesis, and docking of sulfadiazine schiff base scaffold for their potential claim as INHA enoyl-(acyl-carrier-protein) reductase inhibitors. Asian J. Pharm. Clin. Res., 2018, 11(10), 233-237.
[http://dx.doi.org/10.22159/ajpcr.2018.v11i10.27179]
[4]
Mitchell, M.O. Discovering protein−ligand chalcogen bonding in the protein data bank using endocyclic sulfur-containing heterocycles as ligand search subsets. J. Mol. Model., 2017, 23(10), 287.
[http://dx.doi.org/10.1007/s00894-017-3452-3] [PMID: 28942498]
[5]
Prabha, T.; Selvinthanuja, C.; Hemalatha, S.; Sengottuvelu, S.; Senthil, J. Machine learning algorithm used to build a QSAR model for pyrazoline scaffold as anti-tubercular agent. J Med Pharm Allied Sci, 2021, 10(6), 4024-4030.
[http://dx.doi.org/10.22270/jmpas.V10I6.2562]
[6]
Varnek, A.; Baskin, I. Machine learning methods for property prediction in chemoinformatics: Quo Vadis? J. Chem. Inf. Model., 2012, 52(6), 1413-1437.
[http://dx.doi.org/10.1021/ci200409x] [PMID: 22582859]
[7]
de Siqueiraa, L.R.P.; de Moraes Gomes, P.A.T.; de Lima Ferreira, L.P. Multi-target compounds acting in cancer progression: Focus on thiosemicarbazone, thiazole and thiazolidinone analogues. Eur. J. Med. Chem., 2019, 170, 237-260.
[8]
Al-Said, M.S.; Bashandy, M.S.; Al-qasoumi, S.I.; Ghorab, M.M. Anti-breast cancer activity of some novel 1,2-dihydropyridine, thiophene and thiazole derivatives. Eur. J. Med. Chem., 2011, 46(1), 137-141.
[http://dx.doi.org/10.1016/j.ejmech.2010.10.024] [PMID: 21093116]
[9]
Braga, S.F.P.; Fonseca, N.C.; Ramos, J.P.; Souza-Fagundes, E.M.; Oliveira, R.B. Synthesis and cytotoxicity evaluation of thiosemicarbazones and their thiazole derivatives. Braz. J. Pharm. Sci., 2016, 52(2), 299-308.
[http://dx.doi.org/10.1590/S1984-82502016000200008]
[10]
Wang, G.; Liu, W.; Fan, M.; He, M.; Li, Y.; Peng, Z. Design, synthesis and biological evaluation of novel thiazole-naphthalene derivatives as potential anticancer agents and tubulin polymerisation inhibitors. J. Enzyme Inhib. Med. Chem., 2021, 36(1), 1694-1702.
[http://dx.doi.org/10.1080/14756366.2022.2081164] [PMID: 34309466]
[11]
Gümüş, M.; Yakan, M.; Koca, İ. Recent advances of thiazole hybrids in biological applications. Future Med. Chem., 2019, 11(15), 1979-1998.
[http://dx.doi.org/10.4155/fmc-2018-0196] [PMID: 31517529]
[12]
Chhabria, M.T.; Patel, S.; Modi, P.; Brahmkshatriya, P.S. Thiazole: A review on chemistry, synthesis and therapeutic importance of its derivatives. Curr. Top. Med. Chem., 2016, 16(26), 2841-2862.
[http://dx.doi.org/10.2174/1568026616666160506130731] [PMID: 27150376]
[13]
Ayati, A.; Emami, S.; Moghimi, S.; Foroumadi, A. Thiazole in the targeted anticancer drug discovery. Future Med. Chem., 2019, 11(15), 1929-1952.
[http://dx.doi.org/10.4155/fmc-2018-0416] [PMID: 31313595]
[14]
Jain, S.; Pattnaik, S.; Pathak, K.; Kumar, S.; Pathak, D.; Jain, S.; Vaidya, A. Anticancer potential of thiazole derivatives: A retrospective review. Mini Rev. Med. Chem., 2018, 18(8), 640-655.
[http://dx.doi.org/10.2174/1389557517666171123211321] [PMID: 29173166]
[15]
Mishra, R.; Sharma, P.K.; Verma, P.K.; Tomer, I.; Mathur, G.; Dhakad, P.K. Biological potential of thiazole derivatives of synthetic origin. J. Heterocycl. Chem., 2017, 54(4), 2103-2116.
[http://dx.doi.org/10.1002/jhet.2827]
[16]
Sharma, P.C.; Bansal, K.K.; Sharma, A.; Sharma, D.; Deep, A. Thiazole-containing compounds as therapeutic targets for cancer therapy. Eur. J. Med. Chem., 2020, 188, 112016.
[http://dx.doi.org/10.1016/j.ejmech.2019.112016] [PMID: 31926469]
[17]
Alqahtani, A.M.; Bayazeed, A.A. Synthesis and antiproliferative activity studies of new functionalized pyridine linked thiazole derivatives. Arab. J. Chem., 2021, 14(1), 102914.
[http://dx.doi.org/10.1016/j.arabjc.2020.11.020]
[18]
Fabian, P.; Gaël, V.; Alexandre, G.; Vincent, M.; Bertrand, T.; Olivier, G.; Mathieu, B.; Peter, P.; Ron, W.; Vincent, D.; Jake, V.; Alexandre, P.; David, C.; Matthieu, B.; Matthieu, P.; Édouard, D. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res., 2011, 12, 2825-2830.
[19]
Kim, S.; Cho, K.H. PyQSAR: A fast QSAR modeling platform using machine learning and jupyter notebook. Bull. Korean Chem. Soc., 2019, 40, 39-44.
[20]
Kubinyi, H. Evolutionary variable selection in regression and PLS analyses. J. Chemometr., 1996, 10(2), 119-133.
[http://dx.doi.org/10.1002/(SICI)1099-128X(199603)10:2<119::AID-CEM409>3.0.CO;2-4]
[21]
Owen, J.R.; Nabney, I.T.; Medina-Franco, J.L.; López-Vallejo, F.; Fabian, L.V. Visualization of molecular fingerprints. J. Chem. Inf. Model., 2011, 51(7), 1552-1563.
[http://dx.doi.org/10.1021/ci1004042] [PMID: 21696145]
[22]
Gao, H.; Williams, C.; Labute, P.; Bajorath, J. Binary quantitative structure-activity relationship (QSAR) analysis of estrogen receptor ligands. J. Chem. Inf. Comput. Sci., 1999, 39(1), 164-168.
[http://dx.doi.org/10.1021/ci980140g] [PMID: 10094611]
[23]
Noble, W.S. What is a support vector machine? Nat. Biotechnol., 2006, 24(12), 1565-1567.
[http://dx.doi.org/10.1038/nbt1206-1565] [PMID: 17160063]
[24]
Nekoei, M.; Mohammadhosseini, M.; Pourbasheer, E. QSAR study of VEGFR-2 inhibitors by using genetic algorithm-multiple linear regressions (GA-MLR) and genetic algorithm-support vector machine (GA-SVM): A comparative approach. Med. Chem. Res., 2015, 24(7), 3037-3046.
[http://dx.doi.org/10.1007/s00044-015-1354-4]
[25]
Handbook of Chemoinformatics: from Data to Knowledge; Gasteiger, J., Ed.; Wiley-VCH, 2008.
[26]
Eriksson, L.; Jaworska, J.; Worth, A.P.; Cronin, M.T.D.; McDowell, R.M.; Gramatica, P. Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environ. Health Perspect., 2003, 111(10), 1361-1375.
[http://dx.doi.org/10.1289/ehp.5758] [PMID: 12896860]
[27]
Zhu, H.; Tropsha, A.; Fourches, D.; Varnek, A.; Papa, E.; Gramatica, P.; Öberg, T.; Dao, P.; Cherkasov, A.; Tetko, I.V. Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis. J. Chem. Inf. Model., 2008, 48(4), 766-784.
[http://dx.doi.org/10.1021/ci700443v] [PMID: 18311912]
[28]
Tetko, I.V.; Sushko, I.; Pandey, A.K.; Zhu, H.; Tropsha, A.; Papa, E.; Öberg, T.; Todeschini, R.; Fourches, D.; Varnek, A. Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: Focusing on applicability domain and overfitting by variable selection. J. Chem. Inf. Model., 2008, 48(9), 1733-1746.
[http://dx.doi.org/10.1021/ci800151m] [PMID: 18729318]
[29]
Zhao, L.; Wang, W.; Sedykh, A.; Zhu, H. Experimental errors in QSAR modeling sets: What we can do and what we cannot do. ACS Omega, 2017, 2(6), 2805-2812.
[http://dx.doi.org/10.1021/acsomega.7b00274] [PMID: 28691113]

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