Login Register Cart 0
Generic placeholder image

Current Analytical Chemistry

Editor-in-Chief

ISSN (Print): 1573-4110
ISSN (Online): 1875-6727

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches

Author(s): Nafiseh Vahedi, Majid Mohammadhosseini* and Mehdi Nekoei

Volume 16, Issue 8, 2020

Page: [1088 - 1105] Pages: 18

DOI: 10.2174/1573411016999200518083359

Price: $65

Abstract

Background: The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily present in eukaryotes.

Methods: In the present report, some efficient linear and non-linear methods including multiple linear regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully used to develop and establish quantitative structure-activity relationship (QSAR) models capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set and selection of the training and test sets. A genetic algorithm (GA) variable selection method was employed to select the optimal subset of descriptors that have the most significant contributions to the overall inhibitory activity from the large pool of calculated descriptors.

Results: The accuracy and predictability of the proposed models were further confirmed using crossvalidation, validation through an external test set and Y-randomization (chance correlations) approaches. Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed models. The results revealed that non-linear modeling approaches, including SVM and ANN could provide much more prediction capabilities.

Conclusion: Among the constructed models and in terms of root mean square error of predictions (RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for the training set, the predictive power of the GA-SVM approach was better. However, compared with MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.

Keywords: Artificial Neural Networks (ANN), genetic algorithm, Multiple Linear Regressions (MLR), Poly(ADP-ribose) polymerases (PARPs) inhibitors, QSAR, Support Vector Machine (SVM).

« Previous Next »
Graphical Abstract

[1]
Muiras, M.L. Mammalian longevity under the protection of PARP-1's multi-facets. Ageing Res. Rev., 2003, 2(2), 129-148.
[http://dx.doi.org/10.1016/S1568-1637(02)00062-4] [PMID: 12605957]
[2]
Eskander, R.N.; Tewari, K.S. PARP inhibition and synthetic lethality in ovarian cancer. Expert Rev. Clin. Pharmacol., 2014, 7(5), 613-622.
[http://dx.doi.org/10.1586/17512433.2014.930662] [PMID: 24984781]
[3]
Cincinelli, R.; Musso, L.; Merlini, L.; Giannini, G.; Vesci, L.; Milazzo, F.M.; Carenini, N.; Perego, P.; Penco, S.; Artali, R.; Zunino, F.; Pisano, C.; Dallavalle, S. 7-Azaindole-1-carboxamides as a new class of PARP-1 inhibitors. Bioorg. Med. Chem., 2014, 22(3), 1089-1103.
[http://dx.doi.org/10.1016/j.bmc.2013.12.031] [PMID: 24398383]
[4]
Langelier, M.F.; Riccio, A.A.; Pascal, J.M. PARP-2 and PARP-3 are selectively activated by 5′ phosphorylated DNA breaks through an allosteric regulatory mechanism shared with PARP-1. Nucleic Acids Res., 2014, 42(12), 7762-7775.
[http://dx.doi.org/10.1093/nar/gku474] [PMID: 24928857]
[5]
Lupo, B.; Trusolino, L. Inhibition of poly(ADP-ribosyl)ation in cancer: old and new paradigms revisited. Biochim. Biophys. Acta, 2014, 1846(1), 201-215.
[http://dx.doi.org/10.1016/j.bbcan.2014.07.004] [PMID: 25026313]
[6]
Maxwell, C.A.; McCarthy, J.; Turley, E. Cell-surface and mitotic-spindle RHAMM: moonlighting or dual oncogenic functions? J. Cell Sci., 2008, 121(Pt 7), 925-932.
[http://dx.doi.org/10.1242/jcs.022038] [PMID: 18354082]
[7]
Costantino, G.; Macchiarulo, A.; Camaioni, E.; Pellicciari, R. Modeling of poly(ADP-ribose)polymerase (PARP) inhibitors. Docking of ligands and quantitative structure-activity relationship analysis. J. Med. Chem., 2001, 44(23), 3786-3794.
[http://dx.doi.org/10.1021/jm010116l] [PMID: 11689065]
[8]
Giannini, G.; Battistuzzi, G.; Vesci, L.; Milazzo, F.M.; De Paolis, F.; Barbarino, M.; Guglielmi, M.B.; Carollo, V.; Gallo, G.; Artali, R.; Dallavalle, S. Novel PARP-1 inhibitors based on a 2-propanoyl-3H-quinazolin-4-one scaffold. Bioorg. Med. Chem. Lett., 2014, 24(2), 462-466.
[http://dx.doi.org/10.1016/j.bmcl.2013.12.048] [PMID: 24388690]
[9]
Rewatkar, P.V.; Kokil, G.R.; Raut, M.K. QSAR studies of phthalazinones: novel inhibitors of poly (ADP-ribose) polymerase. Med. Chem. Res., 2011, 20, 877-886.
[http://dx.doi.org/10.1007/s00044-010-9414-2]
[10]
Zeng, H.; Zhang, H.; Jang, F.; Zhao, L.; Zhang, J. Molecular modeling studies on benzimidazole carboxamide derivatives as PARP-1 inhibitors using 3D-QSAR and docking. Chem. Biol. Drug Des., 2011, 78(3), 333-352.
[http://dx.doi.org/10.1111/j.1747-0285.2011.01139.x] [PMID: 21585709]
[11]
Hottiger, M.O.; Hassa, P.O.; Lüscher, B.; Schüler, H.; Koch-Nolte, F. Toward a unified nomenclature for mammalian ADP-ribosyltransferases. Trends Biochem. Sci., 2010, 35(4), 208-219.
[http://dx.doi.org/10.1016/j.tibs.2009.12.003] [PMID: 20106667]
[12]
Glendenning, J.; Tutt, A. PARP inhibitors--current status and the walk towards early breast cancer. Breast, 2011, 20(Suppl. 3), S12-S19.
[http://dx.doi.org/10.1016/S0960-9776(11)70288-0] [PMID: 22015278]
[13]
Liu, J.F.; Konstantinopoulos, P.A.; Matulonis, U.A. PARP inhibitors in ovarian cancer: current status and future promise. Gynecol. Oncol., 2014, 133(2), 362-369.
[http://dx.doi.org/10.1016/j.ygyno.2014.02.039] [PMID: 24607283]
[14]
Underhill, C.; Toulmonde, M.; Bonnefoi, H. A review of PARP inhibitors: from bench to bedside. Ann. Oncol., 2011, 22(2), 268-279.
[http://dx.doi.org/10.1093/annonc/mdq322] [PMID: 20643861]
[15]
Kosvyra, A.; Maramis, C.; Chouvarda, I. Developing an integrated genomic profile for cancer patients with the use of NGS data. Emerg. Sci. J., 2019, 3, 157-167.
[http://dx.doi.org/10.28991/esj-2019-01178]
[16]
Yavari, K. Anti-angiogenesis therapy of cancer cells using 153sm-bevasesomab. Emerg. Sci. J., 2018, 2, 130-139.
[http://dx.doi.org/10.28991/esj-2018-01136]
[17]
Zare, H. Effects of Salvia officinalis extract on the breast cancer cell line. Sci. Med. J., 2019, 1, 25-29.
[18]
Fatima, S.; Bathini, R.; Sivan, S.K.; Manga, V. Molecular docking and 3D-QSAR studies on inhibitors of DNA damage signaling enzyme human PARP-1. J. Recept. Signal Transduct. Res., 2012, 32(4), 214-224.
[http://dx.doi.org/10.3109/10799893.2012.693087] [PMID: 22713102]
[19]
Fatima, S.; Jatavath, M.B.; Bathini, R.; Sivan, S.K.; Manga, V. Multiple receptor conformation docking, dock pose clustering and 3D QSAR studies on human poly(ADP-ribose) polymerase-1 (PARP-1) inhibitors. J. Recept. Signal Transduct. Res., 2014, 34(5), 417-430.
[http://dx.doi.org/10.3109/10799893.2014.917323] [PMID: 25046176]
[20]
Zhu, G-D.; Gong, J.; Gandhi, V.B.; Liu, X.; Shi, Y.; Johnson, E.F.; Donawho, C.K.; Ellis, P.A.; Bouska, J.J.; Osterling, D.J.; Olson, A.M.; Park, C.; Luo, Y.; Shoemaker, A.; Giranda, V.L.; Penning, T.D. Discovery and SAR of orally efficacious tetrahydropyrido-pyridazinone PARP inhibitors for the treatment of cancer. Bioorg. Med. Chem., 2012, 20(15), 4635-4645.
[http://dx.doi.org/10.1016/j.bmc.2012.06.021] [PMID: 22766219]
[21]
Rescigno, A.; Casañola-Martin, G.M.; Sanjust, E.; Zucca, P.; Marrero-Ponce, Y. Vanilloid derivatives as tyrosinase inhibitors driven by virtual screening-based QSAR models. Drug Test. Anal., 2011, 3(3), 176-181.
[http://dx.doi.org/10.1002/dta.187] [PMID: 21125547]
[22]
Noorizadeh, H.; Farmany, A. Determination of partitioning of drug molecules using immobilized liposome chromatography and chemometrics methods. Drug Test. Anal., 2012, 4(2), 151-157.
[http://dx.doi.org/10.1002/dta.262] [PMID: 21438160]
[23]
Nekoei, M.; Salimi, M.; Dolatabadi, M.; Mohammadhosseini, M. A quantitative structure-activity relationship study of tetrabutylphos-phonium bromide analogs as muscarinic acetylcholine receptors agonists. J. Serb. Chem. Soc., 2011, 76, 1117-1127.
[http://dx.doi.org/10.2298/JSC101122102S]
[24]
Ece, A.; Pejin, B. A computational insight into acetylcholinesterase inhibitory activity of a new lichen depsidone. J. Enzyme Inhib. Med. Chem., 2015, 30(4), 528-532.
[http://dx.doi.org/10.3109/14756366.2014.949256] [PMID: 25198888]
[25]
Pejin, B.; Tommonaro, G.; Iodice, C.; Tesevic, V.; Vajs, V.; De Rosa, S. A new depsidone of Lobaria pulmonaria with acetylcholinesterase inhibition activity. J. Enzyme Inhib. Med. Chem., 2013, 28(4), 876-878.
[http://dx.doi.org/10.3109/14756366.2012.677839] [PMID: 22512723]
[26]
Chakravarti, S.; Saiakhov, R. A new approach based on QSAR based expert system and a quantitative read across methodology to achieve better in silico genotoxicity assessment of drugs, impurities and metabolites. Toxicol. Lett., 2013, 221, S78-S79.
[http://dx.doi.org/10.1016/j.toxlet.2013.05.077]
[27]
Devillers, J. Linear versus nonlinear QSAR modeling of the toxicity of phenol derivatives to Tetrahymena pyriformis. SAR QSAR Environ. Res., 2004, 15(4), 237-249.
[http://dx.doi.org/10.1080/10629360410001724905] [PMID: 15370415]
[28]
Pontiki, E.; Hadjipavlou-Litina, D.; Geromichalos, G.; Papageorgiou, A. Anticancer activity and quantitative-structure activity relationship (QSAR) studies of a series of antioxidant/anti-inflammatory aryl-acetic and hydroxamic acids. Chem. Biol. Drug Des., 2009, 74(3), 266-275.
[http://dx.doi.org/10.1111/j.1747-0285.2009.00864.x] [PMID: 19703028]
[29]
Rasulev, B.F.; Abdullaev, N.D.; Syrov, V.N.; Leszczynski, J. A quantitative structure-activity relationship (QSAR) study of the antioxidant activity of flavonoids. QSAR Comb. Sci., 2005, 24, 1056-1065.
[http://dx.doi.org/10.1002/qsar.200430013]
[30]
Worachartcheewan, A.; Prachayasittikul, S.; Pingaew, R.; Nantasenamat, C.; Tantimongcolwat, T.; Ruchirawat, S.; Prachayasittikul, V. Antioxidant, cytotoxicity, and QSAR study of 1-adamantylthio derivatives of 3-picoline and phenylpyridines. Med. Chem. Res., 2012, 21, 3514-3522.
[http://dx.doi.org/10.1007/s00044-011-9903-y]
[31]
Giraud, F.; Loge, C.; Le Borgne, M.; Pagniez, F.; Na, Y.M.; Le Pape, P. A 3D-QSAR CoMSIA study on 3-azolylmethylindoles as anti-leishmanial agents. SAR QSAR Environ. Res., 2006, 17(3), 299-309.
[http://dx.doi.org/10.1080/10659360600787494] [PMID: 16815769]
[32]
Jensen, G.E.; Nikolov, N.G.; Wedebye, E.B.; Ringsted, T.; Niemela, J.R. QSAR models for anti-androgenic effect--a preliminary study. SAR QSAR Environ. Res., 2011, 22(1-2), 35-49.
[http://dx.doi.org/10.1080/1062936X.2010.528981] [PMID: 21391140]
[33]
Pandey, S.K.; Naware, N.B.; Trivedi, P.; Saxena, A.K. Molecular modeling and 3D-QSAR studies in 2-aziridinyl-and 2,3-bis(aziridinyl)-1,4-naphthoquinonyl sulfonate and acylate derivatives as potential antimalarial agents. SAR QSAR Environ. Res., 2001, 12(6), 547-564.
[http://dx.doi.org/10.1080/10629360108039834] [PMID: 11813805]
[34]
Athri, P.; Wenzler, T.; Ruiz, P.; Brun, R.; Boykin, D.W.; Tidwell, R.; Wilson, W.D. 3D QSAR on a library of heterocyclic diamidine derivatives with antiparasitic activity. Bioorg. Med. Chem., 2006, 14(9), 3144-3152.
[http://dx.doi.org/10.1016/j.bmc.2005.12.029] [PMID: 16442293]
[35]
González-Díaz, H.; Olazábal, E.; Santana, L.; Uriarte, E.; González-Díaz, Y.; Castañedo, N. QSAR study of anticoccidial activity for diverse chemical compounds: prediction and experimental assay of trans-2-(2-nitrovinyl)furan. Bioorg. Med. Chem., 2007, 15(2), 962-968.
[http://dx.doi.org/10.1016/j.bmc.2006.10.032] [PMID: 17081758]
[36]
González-Díaz, H.; Prado-Prado, F.J.; Santana, L.; Uriarte, E. Unify QSAR approach to antimicrobials. Part 1: predicting antifungal activity against different species. Bioorg. Med. Chem., 2006, 14(17), 5973-5980.
[http://dx.doi.org/10.1016/j.bmc.2006.05.018] [PMID: 16759868]
[37]
Dolatabadi, M.; Nekoei, M.; Banaei, A. Prediction of antibacterial activity of pleuromutilin derivatives by genetic algorithm–multiple linear regression GA-MLR. Monatsh. Chem., 2010, 141, 577-588.
[http://dx.doi.org/10.1007/s00706-010-0299-z]
[38]
Gopalakrishnan, B.; Khandelwal, A.; Rajjak, S.A.; Selvakumar, N.; Das, J.; Trehan, S.; Iqbal, J.; Kumar, M.S. Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of tricyclic oxazolidinones as antibacterial agents. Bioorg. Med. Chem., 2003, 11(12), 2569-2574.
[http://dx.doi.org/10.1016/S0968-0896(03)00157-3] [PMID: 12757724]
[39]
Virsdoia, V.; Shaikh, M.S.; Manvar, A.; Desai, B.; Parecha, A.; Loriya, R.; Dholariya, K.; Patel, G.; Vora, V.; Upadhyay, K.; Denish, K.; Shah, A.; Coutinho, E.C. Screening for in vitro antimycobacterial activity and three-dimensional quantitative structure-activity relationship (3D-QSAR) study of 4-(arylamino)coumarin derivatives. Chem. Biol. Drug Des., 2010, 76(5), 412-424.
[http://dx.doi.org/10.1111/j.1747-0285.2010.00997.x] [PMID: 20925693]
[40]
Van Damme, S.; Bultinck, P. 3D QSAR based on conceptual DFT molecular fields: Antituberculotic activity. J. Mol. Struct. THEOCHEM, 2010, 943, 83-89.
[http://dx.doi.org/10.1016/j.theochem.2009.10.031]
[41]
Riahi, S.; Pourbasheer, E.; Dinarvand, R.; Ganjali, M.R.; Norouzi, P. Exploring QSARs for antiviral activity of 4-alkylamino-6-(2-hydroxyethyl)-2-methylthiopyrimidines by support vector machine. Chem. Biol. Drug Des., 2008, 72(3), 205-216.
[http://dx.doi.org/10.1111/j.1747-0285.2008.00695.x] [PMID: 18715229]
[42]
Garkani-Nejad, Z.; Karlovits, M.; Demuth, W.; Stimpfl, T.; Vycudilik, W.; Jalali-Heravi, M.; Varmuza, K. Prediction of gas chromatographic retention indices of a diverse set of toxicologically relevant compounds. J. Chromatogr. A, 2004, 1028(2), 287-295.
[http://dx.doi.org/10.1016/j.chroma.2003.12.003] [PMID: 14989482]
[43]
Gharagheizi, F. A simple equation for prediction of net heat of combustion of pure chemicals. Chemom. Intell. Lab. Syst., 2008, 91, 177-180.
[http://dx.doi.org/10.1016/j.chemolab.2007.11.003]
[44]
Mohammadhosseini, M.; Zamani, H.A.; Akhlaghi, H.; Nekoei, M. Hydrodistilled volatile oil constituents of the aerial parts of Prangos serpentinica (Rech.f., Aell. Esfand.) Hernnstadt and Heyn from Iran and quantitative structure-retention relationship simulation. J. Essent. Oil-Bear. Plants, 2011, 14, 559-573.
[http://dx.doi.org/10.1080/0972060X.2011.10643973]
[45]
Dashtbozorgi, Z.; Golmohammadi, H. Prediction of air to liver partition coefficient for volatile organic compounds using QSAR approaches. Eur. J. Med. Chem., 2010, 45(6), 2182-2190.
[http://dx.doi.org/10.1016/j.ejmech.2010.01.056] [PMID: 20153567]
[46]
Ghasemi, J.B.; Salahinejad, M.; Rofouei, M.K. Alignment independent 3D-QSAR modeling of fullerene (C-60) solubility in different organic solvents. Fuller. Nanotub. Car. N., 2013, 21, 367-380.
[http://dx.doi.org/10.1080/1536383X.2011.629751]
[47]
Asadpour-Zeynali, K.; Jalili-Jahani, N. Modeling GC-ECD retention times of pentafluorobenzyl derivatives of phenol by using artificial neural networks. J. Sep. Sci., 2008, 31(21), 3788-3795.
[http://dx.doi.org/10.1002/jssc.200800418] [PMID: 18956382]
[48]
Jalali-Heravi, M.; Asadollahi-Baboli, M. QSAR analysis of platelet-derived growth inhibitors using GA-ANN and shuffling crossvalidation. QSAR Comb. Sci., 2008, 27, 750-757.
[http://dx.doi.org/10.1002/qsar.200710138]
[49]
Deeb, O.; Drabh, M. Exploring QSARs of some analgesic compounds by PC-ANN. Chem. Biol. Drug Des., 2010, 76(3), 255-262.
[http://dx.doi.org/10.1111/j.1747-0285.2010.01004.x] [PMID: 20626409]
[50]
Đorđević, N.O.; Todorović, N.; Novaković, I.T.; Pezo, L.L.; Pejin, B.; Maraš, V.; Tešević, V.V.; Pajović, S.B. Antioxidant activity of selected polyphenolics in yeast cells: The case study of Montenegrin Merlot wine. Molecules, 2018, 23(8), E1971
[http://dx.doi.org/10.3390/molecules23081971] [PMID: 30087228]
[51]
Deeb, O. Correlation ranking and stepwise regression procedures in principal components artificial neural networks modeling with application to predict toxic activity and human serum albumin binding affinity. Chemom. Intell. Lab. Syst., 2010, 104, 181-194.
[http://dx.doi.org/10.1016/j.chemolab.2010.08.007]
[52]
Jalali-Heravi, M.; Asadollahi-Baboli, M.; Shahbazikhah, P. QSAR study of heparanase inhibitors activity using artificial neural networks and Levenberg-Marquardt algorithm. Eur. J. Med. Chem., 2008, 43(3), 548-556.
[http://dx.doi.org/10.1016/j.ejmech.2007.04.014] [PMID: 17602800]
[53]
Jalali-Heravi, M.; Garkani-Nejad, Z. Prediction of relative response factors for flame ionization and photoionization detection using self-training artificial neural networks. J. Chromatogr. A, 2002, 950(1-2), 183-194.
[http://dx.doi.org/10.1016/S0021-9673(02)00054-7] [PMID: 11990992]
[54]
Niani, C.; Wencong, L.; Jie, Y.; Gozheng, L. Support Vector Machine in Chemistry; ; World Scientific Publishing Co. Pet. Ltd.: Shanghai, . , 2004.
[55]
Doucet, J.P.; Barbault, F.; Xia, H.R.; Panaye, A.; Fan, B. Nonlinear SVM approaches to QSPR/QSAR studies and drug design. Curr. Comput. Aided Drug Des., 2007, 3, 263-289.
[http://dx.doi.org/10.2174/157340907782799372]
[56]
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, 3037-3046.
[http://dx.doi.org/10.1007/s00044-015-1354-4]
[57]
Riahi, S.; Pourbasheer, E.; Ganjali, M.R.; Norouzi, P. Investigation of different linear and nonlinear chemometric methods for modeling of retention index of essential oil components: concerns to support vector machine. J. Hazard. Mater., 2009, 166(2-3), 853-859.
[http://dx.doi.org/10.1016/j.jhazmat.2008.11.097] [PMID: 19144466]
[58]
Gunn, S.R. Support Vector Machines for Classification and Regression; University of Southampton: Southampton, 1997.
[59]
Mohammadhosseini, M. Novel PSO-MLR algorithm to predict the chromatographic retention behaviors of natural compounds. Anal. Chem. Lett., 2013, 3, 226-248.
[http://dx.doi.org/10.1080/22297928.2013.861164]
[60]
Mohammadhosseini, M. Prediction of the GC-MS retention indices for a diverse set of terpenes as constituent components of Camu-camu (Myrciaria dubia (HBK) McVaugh) volatile oil, using particle swarm optimization-multiple linear regression (PSO-MLR). J. Chem. Health Risks, 2014, 4, 75-95.
[http://dx.doi.org/10.22034/JCHR.2018.544059]
[61]
Nekoei, M.; Mohammadhosseini, M. Simultaneous spectro-photometric determination of iron and cobalt in micellar medium by using a principal component artificial neural network and multivariate calibration. J. Chin. Chem. Soc. (Taipei), 2007, 53, 383-390.
[http://dx.doi.org/10.1002/jccs.200700055]
[62]
Fernández, M.; Caballero, J. QSAR modeling of matrix metalloproteinase inhibition by N-hydroxy-alpha-phenylsulfonyl-acetamide derivatives. Bioorg. Med. Chem., 2007, 15(18), 6298-6310.
[http://dx.doi.org/10.1016/j.bmc.2007.06.014] [PMID: 17590339]
[63]
Vatani, A.; Mehrpooya, M.; Gharagheizi, F. Prediction of standard enthalpy of formation by a QSPR model. Int. J. Mol. Sci., 2007, 8, 407-432.
[http://dx.doi.org/10.3390/i8050407]
[64]
Goudarzi, N.; Goodarzi, M.; Mohammadhosseini, M.; Nekooei, M. QSPR models for prediction of half-wave potentials of some chlorinated organic compounds using SR-PLS and GA-PLS methods. Mol. Phys., 2009, 107, 1739-1744.
[http://dx.doi.org/10.1080/00268970903042266]
[65]
Nekoei, M.; Mohammadhosseini, M.; Alavi-Gharahbagh, A. Quantitative structure-electrochemistry relationship (QSER) study for prediction of half-wave potentials of organic compounds. Anal. Bioanal. Electrochem., 2009, 1, 159-168.
[66]
Rahimi, M.; Karimi, E.; Nekoei, M.; Mohammadhosseini, M. Hydro-distilled volatile oil constituents from the aerial parts of Satureja mutica and QSRR simulation by multiple linear regression. J. Essent. Oil-Bear. Plants, 2016, 19, 307-320.
[http://dx.doi.org/10.1080/0972060X.2015.1137237]
[67]
Pasha Zanousi, M.B.; Nekoei, M.; Mohammadhosseini, M. Composition of the essential oils and volatile fractions of Artemisia absinthium by three different extraction methods: Hydrodistillation, solvent-free microwave extraction and headspace solid-phase microextraction combined with a novel QSRR evaluation. J. Essent. Oil-Bear. Plants, 2016, 19, 1561-1581.
[http://dx.doi.org/10.1080/0972060X.2014.1001139]
[68]
Mohammadhosseini, M.; Deeb, O.; Alavi-Gharabagh, A.; Nekoei, M. Exploring novel QSRRs for simulation of gas chromatographic retention indices of diverse sets of terpenoids in Pistacia lentiscus L. essential oil using stepwise and genetic algorithm multiple linear regressions. Anal. Chem. Lett., 2012, 2, 80-102.
[http://dx.doi.org/10.1080/222979282000.10648255]
[69]
Pourbasheer, E.; Riahi, S.; Ganjali, M.R.; Norouzi, P. Quantitative structure-activity relationship (QSAR) study of interleukin-1 receptor associated kinase 4 (IRAK-4) inhibitor activity by the genetic algorithm and multiple linear regression (GA-MLR) method. J. Enzyme Inhib. Med. Chem., 2010, 25(6), 844-853.
[http://dx.doi.org/10.3109/14756361003757893] [PMID: 20429783]
[70]
Mohammadhosseini, M. Chemical profile and antibacterial activity in hydrodistilled oil from aerial parts of Prangos ferulacea (L.) Lindl. and prediction of gas chromatographic retention indices by using genetic algorithm multiple linear regressions. Asian J. Chem., 2012, 24, 3814-3820.
[71]
Hosseini, J.; Nekoei, M.; Mohammadhosseini, M.; Goudarzi, N. Quantitative structure-activity relationship study of arylsulfonyl-piperazine inhibitors of 11β-HSD1 by genetic algorithm-multiple linear regression. J. Appl. Res. Chem., 2011, 5, 5-17.
[72]
Jalali, A.; Nekoei, M.; Mohammadhosseini, M. Novel QSPR study on the melting points of a broad set of drug-like compounds using the genetic algorithm feature selection approach combined with multiple linear regression and support vector machine. J. Chem. Health Risks, 2016, 6, 49-67.
[http://dx.doi.org/10.22034/JCHR.2016.544128]
[73]
Nekoei, M.; Salimi, M.; Dolatabadi, M.; Mohammadhosseini, M. Prediction of antileukemia activity of berbamine derivatives by genetic algorithm-multiple linear regression. Monatsh. Chem., 2011, 142, 943-948.
[http://dx.doi.org/10.1007/s00706-011-0510-x]
[74]
Vapnik, N.V. Statistical Learning Theory; John Wiley & Sons: New York, 1998.
[75]
Al-Thanoon, N.A.; Qasim, O.S.; Algamal, Z.Y. A new hybrid firefly algorithm and particle swarm optimization for tuning parameter estimation in penalized support vector machine with application in chemometrics. Chemom. Intell. Lab. Syst., 2019, 184, 142-152.
[http://dx.doi.org/10.1016/j.chemolab.2018.12.003]
[76]
Li, W.; Yan, X.; Pan, J.; Liu, S.; Xue, D.; Qu, H. Rapid analysis of the Tanreqing injection by near-infrared spectroscopy combined with least squares support vector machine and Gaussian process modeling techniques. Spectrochim. Acta A Mol. Biomol. Spectrosc., 2019, 218, 271-280.
[http://dx.doi.org/10.1016/j.saa.2019.03.110] [PMID: 31004970]
[77]
Guo, H.; Wang, W. Granular support vector machine: a review. Artif. Intell. Rev., 2019, 51, 19-32.
[http://dx.doi.org/10.1007/s10462-017-9555-5]
[78]
Maltarollo, V.G.; Kronenberger, T.; Espinoza, G.Z.; Oliveira, P.R.; Honorio, K.M. Advances with support vector machines for novel drug discovery. Expert Opin. Drug Discov., 2019, 14(1), 23-33.
[http://dx.doi.org/10.1080/17460441.2019.1549033] [PMID: 30488731]
[79]
Nalepa, J.; Kawulok, M. Selecting training sets for support vector machines: a review. Artif. Intell. Rev., 2019, 52, 857-900.
[http://dx.doi.org/10.1007/s10462-017-9611-1]
[80]
Tavara, S. Parallel computing of support vector machines: A survey. ACM Comput. Surv., 2019, 51.
[http://dx.doi.org/10.1145/3280989]
[81]
Haglin, J.M.; Jimenez, G.; Eltorai, A.E.M. Artificial neural networks in medicine. Health Technol., 2019, 9.
[http://dx.doi.org/10.1007/s12553-018-0244-4]
[82]
Polulyakh, S.N.; Gorbovanov, A.I. Using artificial neural network in nuclear spin echo experiments. Tech. Phys. Lett., 2019, 45, 598-600.
[http://dx.doi.org/10.1134/S1063785019060270]
[83]
Rodríguez-Sánchez, A.E.; Ledesma-Orozco, E.; Ledesma, S.; Vidal-Lesso, A. Application of artificial neural networks to map the mechanical response of a thermoplastic elastomer. Mater. Res. Express, 2019, 6.
[http://dx.doi.org/10.1088/2053-1591/ab13ec]
[84]
Xu, Y.; Li, X.; Yao, H.; Lin, K. Neural networks in drug discovery: current insights from medicinal chemists. Future Med. Chem., 2019, 11(14), 1669-1672.
[http://dx.doi.org/10.4155/fmc-2019-0118] [PMID: 31287735]
[85]
Nekoei, M.; Mohammadhosseini, M.; Rahimi, M.; Alavi-Gharahbagh, A. Linear and non-linear quantitative structure-activity relationship for prediction of drug activity of some amino acid derivatives. J. Appl. Res. Chem., 2013, 6, 53-61.
[86]
Noorizadeh, H.; Sobhan-Ardakani, S.; Raoofi, F.; Noorizadeh, M.; Mortazavi, S.S.; Ahmadi, T.; Pournajafi, K. Application of artificial neural network to predict the retention time of drug metabolites in two-dimensional liquid chromatography. Drug Test. Anal., 2013, 5(5), 315-319.
[http://dx.doi.org/10.1002/dta.325] [PMID: 22012704]
[87]
Aires-de-Sousa, J.; Hemmer, M.C.; Gasteiger, J. Prediction of 1H NMR chemical shifts using neural networks. Anal. Chem., 2002, 74(1), 80-90.
[http://dx.doi.org/10.1021/ac010737m] [PMID: 11795822]
[88]
Todeschini, R.; Consonni, V. Handbook of Molecular Descriptors; Wiley-VCH: Weinheim, Germany, 2000.
[http://dx.doi.org/10.1002/9783527613106]
[89]
Agrawal, V.K.; Khadikar, P.V. QSAR prediction of toxicity of nitrobenzenes. Bioorg. Med. Chem., 2001, 9(11), 3035-3040.
[http://dx.doi.org/10.1016/S0968-0896(01)00211-5] [PMID: 11597486]
[90]
Ren, Y.; Qin, J.; Liu, H.; Yao, X.; Liu, M. QSPR study on the melting points of a diverse set of potential ionic liquids by projection pursuit regression. QSAR Comb. Sci., 2009, 28, 1237-1244.
[http://dx.doi.org/10.1002/qsar.200710073]
[91]
Baumann, K. Chance correlation in variable subset regression: Influence of the objective function, the selection mechanism, and ensemble averaging. QSAR Comb. Sci., 2005, 24, 1033-1046.
[http://dx.doi.org/10.1002/qsar.200530134]
[92]
Nekoei, M.; Goudarzi, N.; Nekoei, S.; Mohammadhosseini, M. QSAR Study of arylsulfonylpiperazine inhibitors of 11β-HSD1 by GA-MLR, GA-PLS and GA-ANN. Anal. Chem. Lett., 2014, 4, 14-28.
[http://dx.doi.org/10.1080/22297928.2013.856167]
[93]
Riahi, S.; Pourbasheer, E.; Dinarvand, R.; Ganjali, M.R.; Norouzi, P. QSAR study of 2-(1-propylpiperidin-4-yl)-1H-benzimidazole-4-carboxamide as PARP inhibitors for treatment of cancer. Chem. Biol. Drug Des., 2008, 72(6), 575-584.
[http://dx.doi.org/10.1111/j.1747-0285.2008.00739.x] [PMID: 19090924]
[94]
Srivastava, A.K.; Chaurasia, S.; Nath, A. Archana Quantitative structure activity relationship studies on a novel series of phthalazinone as potent poly(ADP-ribose) polymerase inhibitors. Proceedings of the National Academy of Sciences India Section a-Physical Sciences, 2008, pp. 37-44.
[95]
Prokhorov, E.I.; Bekker, A.V.; Perevoznikov, A.V.; Kumskov, M.I.; Svitanko, I.V. Combining 3D-QSAR and molecular docking for the virtual screening of PARP inhibitors. Mendeleev Commun., 2015, 25, 214-215.
[http://dx.doi.org/10.1016/j.mencom.2015.05.019]

Rights & Permissions Print Cite
Article Metrics
22
© 2024 Bentham Science Publishers | Privacy Policy