Quantitative structure-activity relationship model for classifying the diverse series of antifungal agents using ratio weighted penalized logistic regression

References

• M. Arisoy, O. Temiz-Arpaci, I. Yildiz, F. Kaynak-Onurdag, E. Aki, I. Yalcin, and U. Abbasoglu, Synthesis, antimicrobial activity and QSAR studies of 2, 5-disubstituted benzoxazoles, SAR QSAR Environ. Res. 19 (2008), pp. 589–612. doi:10.1080/10629360802348738.
   PubMed Web of Science ®Google Scholar
• V.S. Georgiev, Membrane transporters and antifungal drug resistance, Curr. Drug Targets 1 (2000), pp. 261–284. doi:10.2174/1389450003349209.
   PubMedGoogle Scholar
• J.-J. Xing, Y.-F. Liu, Y.-Q. Li, H. Gong, and Y.-P. Zhou, QSAR classification model for diverse series of antimicrobial agents using classification tree configured by modified particle swarm optimization, Chemom. Intell. Lab. Syst. 137 (2014), pp. 82–90. doi:10.1016/j.chemolab.2014.06.011.
   Web of Science ®Google Scholar
• R.K. Singla and G.V. Bhat, QSAR model for predicting the fungicidal action of 1,2,4-triazole derivatives against Candida albicans, J. Enzyme Inhib. Med. Chem. 25 (2010), pp. 696–701. doi:10.3109/14756360903524296.
   PubMed Web of Science ®Google Scholar
• R. Medina Marrero, Y. Marrero-Ponce, S.J. Barigye, Y. Echeverria Diaz, R. Acevedo-Barrios, G.M. Casanola-Martin, M. Garcia Bernal, F. Torrens, and F. Pérez-Giménez, QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents, SAR QSAR Environ. Res. 26 (2015), pp. 943–958. doi:10.1080/1062936X.2015.1104517.
   PubMed Web of Science ®Google Scholar
• R. Di Santo, A. Tafi, R. Costi, M. Botta, M. Artico, F. Corelli, M. Forte, F. Caporuscio, L. Angiolella, and A.T. Palamara, Antifungal agents. 11. N-substituted derivatives of 1-[(aryl)(4-aryl-1 H-pyrrol-3-yl) methyl]-1 H-imidazole: Synthesis, anti-candida activity, and QSAR studies, J. Med. Chem. 48 (2005), pp. 5140–5153. doi:10.1021/jm048997u.
   PubMed Web of Science ®Google Scholar
• N.H. Georgopapadakou, Antifungals: Mechanism of action and resistance, established and novel drugs, Curr. Opin. Microbiol. 1 (1998), pp. 547–557. doi:10.1016/S1369-5274(98)80087-8.
   PubMed Web of Science ®Google Scholar
• K. Takrouri, G. Oren, I. Polacheck, E. Sionov, E. Shalom, J. Katzhendler, and M. Srebnik, Synthesis and antifungal activity of a novel series of alkyldimethylamine cyanoboranes and their derivatives, J. Med. Chem. 49 (2006), pp. 4879–4885. doi:10.1021/jm060476e.
   PubMed Web of Science ®Google Scholar
• T.J. Walsh, C. Gonzalez, E. Roilides, B.U. Mueller, N. Ali, L.L. Lewis, T.O. Whitcomb, D.J. Marshall, and P.A. Pizzo, Fungemia in children infected with the human immunodeficiency virus: New epidemiologic patterns, emerging pathogens, and improved outcome with antifungal therapy, Clin. Infect. Dis. 20 (1995), pp. 900–906. doi:10.1093/clinids/20.4.900.
   PubMed Web of Science ®Google Scholar
• P.R. Duchowicz, M.G. Vitale, E.A. Castro, M. Fernández, and J. Caballero, QSAR analysis for heterocyclic antifungals, Bioorg. Med. Chem. 15 (2007), pp. 2680–2689. doi:10.1016/j.bmc.2007.01.039.
   PubMed Web of Science ®Google Scholar
• N.-H. Nam, S. Sardari, M. Selecky, and K. Parang, Carboxylic acid and phosphate ester derivatives of fluconazole: Synthesis and antifungal activities, Bioorg. Med. Chem. 12 (2004), pp. 6255–6269. doi:10.1016/j.bmc.2004.08.049.
   PubMed Web of Science ®Google Scholar
• V.M. Gokhale and V.M. Kulkarni, Understanding the antifungal activity of terbinafine analogues using quantitative structure–activity relationship (QSAR) models, Bioorg. Med. Chem. 8 (2000), pp. 2487–2499. doi:10.1016/S0968-0896(00)00178-4.
   PubMed Web of Science ®Google Scholar
• B. Narasimhan, V. Judge, R. Narang, R. Ohlan, and S. Ohlan, Quantitative structure–activity relationship studies for prediction of antimicrobial activity of synthesized 2, 4-hexadienoic acid derivatives, Bioorg. Med. Chem. Lett. 17 (2007), pp. 5836–5845. doi:10.1016/j.bmcl.2007.08.037.
   PubMed Web of Science ®Google Scholar
• M.D. Altıntop, Z.A. Kaplancıklı, G.A. Çiftçi, and R. Demirel, Synthesis and biological evaluation of thiazoline derivatives as new antimicrobial and anticancer agents, Eur. J. Med. Chem. 74 (2014), pp. 264–277. doi:10.1016/j.ejmech.2013.12.060.
   PubMed Web of Science ®Google Scholar
• B. Narasimhan, D. Belsare, D. Pharande, V. Mourya, and A. Dhake, Esters, amides and substituted derivatives of cinnamic acid: Synthesis, antimicrobial activity and QSAR investigations, Eur. J. Med. Chem. 39 (2004), pp. 827–834. doi:10.1016/j.ejmech.2004.06.013.
   PubMed Web of Science ®Google Scholar
• D. Sharma, B. Narasimhan, P. Kumar, and A. Jalbout, Synthesis and QSAR evaluation of 2-(substituted phenyl)-1H-benzimidazoles and [2-(substituted phenyl)-benzimidazol-1-yl]-pyridin-3-yl-methanones, Eur. J. Med. Chem. 44 (2009), pp. 1119–1127. doi:10.1016/j.ejmech.2008.06.009.
   PubMed Web of Science ®Google Scholar
• A.R. Katritzky, S.H. Slavov, D.A. Dobchev, and M. Karelson, QSAR modeling of the antifungal activity against Candida albicans for a diverse set of organic compounds, Bioorg. Med. Chem. 16 (2008), pp. 7055–7069. doi:10.1016/j.bmc.2008.05.014.
   PubMed Web of Science ®Google Scholar
• S.-M. Tan, J. Jiao, X.-L. Zhu, Y.-P. Zhou, -D.-D. Song, H. Gong, and R.Q. Yu, QSAR studies of a diverse series of antimicrobial agents against Candida albicans by classification and regression trees, Chemom. Intell. Lab. Syst. 103 (2010), pp. 184–190. doi:10.1016/j.chemolab.2010.07.005.
   Web of Science ®Google Scholar
• A.M. Al-Fakih, Z.Y. Algamal, M.H. Lee, M. Aziz, and H.T.M. Ali, QSAR classification model for diverse series of antifungal agents based on improved binary differential search algorithm, SAR QSAR Environ. Res. 30 (2019), pp. 131–143. doi:10.1080/1062936X.2019.1568298.
   PubMed Web of Science ®Google Scholar
• R. Sabet and A. Fassihi, QSAR study of antimicrobial 3-hydroxypyridine-4-one and 3-hydroxypyran-4-one derivatives using different chemometric tools, Int. J. Mol. Sci. 9 (2008), pp. 2407–2423. doi:10.3390/ijms9122407.
   PubMed Web of Science ®Google Scholar
• A. Buciński, A. Socha, M. Wnuk, T. Bączek, A. Nowaczyk, J. Krysiński, K. Goryński, and M. Koba, Artificial neural networks in prediction of antifungal activity of a series of pyridine derivatives against Candida albicans, J. Microbiol. Methods 76 (2009), pp. 25–29. doi:10.1016/j.mimet.2008.09.003.
   PubMed Web of Science ®Google Scholar
• A. Worachartcheewan, C. Nantasenamat, C. Isarankura-Na-Ayudhya, and V. Prachayasittikul, Predicting antimicrobial activities of benzimidazole derivatives, Med. Chem. Res. 22 (2013), pp. 5418–5430. doi:10.1007/s00044-013-0539-y.
   Web of Science ®Google Scholar
• K. Lal, C.P. Kaushik, and A. Kumar, Antimicrobial evaluation, QSAR and docking studies of amide-linked 1, 4-disubstituted 1, 2, 3-bistriazoles, Med. Chem. Res. 24 (2015), pp. 3258–3271. doi:10.1007/s00044-015-1378-9.
   Web of Science ®Google Scholar
• C.-W. Cho, J.-S. Park, S. Stolte, and Y.-S. Yun, Modelling for antimicrobial activities of ionic liquids towards Escherichia coli, Staphylococcus aureus and Candida albicans using linear free energy relationship descriptors, J. Hazard. Mater. 311 (2016), pp. 168–175. doi:10.1016/j.jhazmat.2016.03.006.
   PubMed Web of Science ®Google Scholar
• R. Montes, A. Perez, C. Medeiros, M. Araújo, E. Lima, M. Scotti, and D. Pergentino de Sousa, Synthesis, antifungal evaluation and in silico study of n-(4-halobenzyl) amides, Molecules 21 (2016), pp. 1716. doi:10.3390/molecules21121716.
   Web of Science ®Google Scholar
• F. Yan, W. He, Q. Jia, S. Xia, and Q. Wang, QSAR models for describing the toxicological effects of ILs against Candida albicans based on norm indexes, Chemosphere 201 (2018), pp. 417–424. doi:10.1016/j.chemosphere.2018.02.147.
   PubMed Web of Science ®Google Scholar
• Z.Y. Algamal and M.H. Lee, Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classification, Expert Syst. Appl. 42 (2015), pp. 9326–9332. doi:10.1016/j.eswa.2015.08.016.
   Web of Science ®Google Scholar
• Y. Asar and A. Genç, New shrinkage parameters for the Liu-type logistic estimators, Commun. Stat. Comput. 45 (2016), pp. 1094–1103. doi:10.1080/03610918.2014.995815.
   Web of Science ®Google Scholar
• Z.Y. Algamal and M.H. Lee, High dimensional logistic regression model using adjusted elastic net penalty, Pakistan J. Stat. Oper. Res. 11 (2015), pp. 667–676. doi:10.18187/pjsor.v11i4.990.
   Web of Science ®Google Scholar
• Z.Y. Algamal and M.H. Lee, Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification, Comput. Biol. Med. 67 (2015), pp. 136–145. doi:10.1016/j.compbiomed.2015.10.008.
   PubMed Web of Science ®Google Scholar
• C. Bielza, V. Robles, and P. Larrañaga, Regularized logistic regression without a penalty term: An application to cancer classification with microarray data, Expert Syst. Appl. 38 (2011), pp. 5110–5118. doi:10.1016/j.eswa.2010.09.140.
   Web of Science ®Google Scholar
• Z.Y. Algamal, M.H. Lee, A.M. Al-Fakih, and M. Aziz, High-dimensional QSAR prediction of anticancer potency of imidazo[4,5-b]pyridine derivatives using adjusted adaptive LASSO, J. Chemom. 29 (2015), pp. 547–556. doi:10.1002/cem.2741.
   Web of Science ®Google Scholar
• A.M. Al-Fakih, M. Aziz, H.H. Abdallah, Z.Y. Algamal, M.H. Lee, and H. Maarof, High dimensional QSAR study of mild steel corrosion inhibition in acidic medium by furan derivatives, Int. J. Electrochem. Sci. 10 (2015), pp. 3568–3583.
   Web of Science ®Google Scholar
• P. Filzmoser, M. Gschwandtner, and V. Todorov, Review of sparse methods in regression and classification with application to chemometrics, J. Chemom. 26 (2012), pp. 42–51. doi:10.1002/cem.1418.
   Web of Science ®Google Scholar
• R. Alhamzawi, K. Yu, and D.F. Benoit, Bayesian adaptive Lasso quantile regression, Stat. Model. 12 (2012), pp. 279–297. doi:10.1177/1471082X1101200304.
   Web of Science ®Google Scholar
• M.A. Rasmussen and R. Bro, A tutorial on the Lasso approach to sparse modeling, Chemom. Intell. Lab. Syst. 119 (2012), pp. 21–31. doi:10.1016/j.chemolab.2012.10.003.
   Web of Science ®Google Scholar
• R. Tibshirani, Regression shrinkage and selection via the Lasso, J. R. Stat. Soc. SeR. B 58 (1996), pp. 267–288.
   Google Scholar
• J. Fan and R. Li, Variable selection via nonconcave penalized likelihood and its oracle properties, J. Am. Stat. Assoc. 96 (2001), pp. 1348–1360. doi:10.1198/016214501753382273.
   Web of Science ®Google Scholar
• H. Zou and T. Hastie, Regularization and variable selection via the elastic net, J. R. Stat. Soc. Ser. B Stat. Meth. 67 (2005), pp. 301–320. doi:10.1111/j.1467-9868.2005.00503.x.
   Google Scholar
• H. Zou, The adaptive Lasso and its oracle properties, J. Am. Stat. Assoc. 101 (2006), pp. 1418–1429. doi:10.1198/016214506000000735.
   Web of Science ®Google Scholar
• A.C. Müller and S. Guido, Introduction to Machine Learning with Python: A Guide for Data Scientists, O’Reilly Media, Inc., Boston 2016.
   Google Scholar
• G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning, Vol. 112, Springer, New York 2013.
   Google Scholar
• S. Wang, B. Nan, S. Rosset,and, and J. Zhu, Random Lasso, Ann. Appl. Stat. 5 (2011), pp. 468–485. doi:10.1214/10-AOAS377.
   PubMed Web of Science ®Google Scholar
• Z.Y. Algamal, Classification of gene expression autism data based on adaptive penalized logistic regression, Electron. J. Appl. Stat. Anal. 10 (2017), pp. 561–571.
   Google Scholar
• P. Bühlmann and S. Van De Geer, Statistics for High-Dimensional Data: Methods, Theory and Applications, Springer, Berlin, 2011.
   Google Scholar
• J. Friedman, T. Hastie, and R. Tibshirani, Regularization paths for generalized linear models via coordinate descent, J. Stat. Soft. 33 (2010), pp. 1–22. doi:10.18637/jss.v033.i01.
   PubMed Web of Science ®Google Scholar
• Z.Y. Algamal, M.H. Lee, A.M. Al-Fakih, and M. Aziz, High-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives based on the sparse logistic regression model with a bridge penalty, J. Chemom. 31 (2017), pp. 1–8. doi:10.1002/cem.2889.
   Web of Science ®Google Scholar
• S. Dudoit, J. Fridlyand, T.P. Speed, S. Dudoit, J. Fridlyand, and T.P. Speed, Comparison of discrimination methods for the classification of tumors using gene expression data, Am. Stat. Assoc. 97 (2002), pp. 77–87. doi:10.1198/016214502753479248.
   Web of Science ®Google Scholar
• H. Peng, Y. Fu, J. Liu, X. Fang, and C. Jiang, Optimal gene subset selection using the modified SFFS algorithm for tumor classification, Neural Comput. Appl. 23 (2013), pp. 1531–1538. doi:10.1007/s00521-012-1148-2.
   Web of Science ®Google Scholar