Generic placeholder image

Letters in Drug Design & Discovery


ISSN (Print): 1570-1808
ISSN (Online): 1875-628X

Research Article

Comparative QSAR Modeling for Predicting Anticancer Potency of Imidazo[4,5-b]Pyridine Derivatives Using GA-MLR and BP-ANN Techniques

Author(s): Mahdi Jafari, Tahereh Momeni Isfahani*, Fatemeh Shafiei, Masumeh Abdoli Senejani and Mohammad Alimoradi

Volume 20, Issue 12, 2023

Published on: 13 January, 2023

Page: [2034 - 2044] Pages: 11

DOI: 10.2174/1570180820666221207121031

Price: $65


Background: Prediction of toxicity of imidazo[4,5-b]pyridine derivatives is carried out using GA-MLR and BPANN methods.

Objective: A quantitative structure-property relationship (QSPR) was determined based on methods, including genetic algorithm-multiple linear regression (GA-MLR) and backpropagation artificial neural network (BP-ANN). These methods were employed for modeling and predicting the anticancer potency of imidazo[4,5-b]pyridine derivatives.

Materials and Methods: A dataset of imidazo[4,5-b]pyridine derivatives was randomly divided into two groups, training and test sets consisting of 75% and 25% of data points, respectively. The optimized conformation of compounds was obtained using the DFT-B3LYP method and 6-31G* basis sets level with Gaussian 09 software. A large number of molecular descriptors were calculated using Dragon software. The QSAR models were optimized using multiple linear regressions (MLR).

Results: The most relevant molecular descriptors were obtained using the genetic algorithm (GA) and backward stepwise regression. The predictive powers of the GA-MLR models were studied using leaveone- out (LOO) cross-validation and an external test set.

Conclusion: The obtained results of statistical parameters showed the BP-ANN model to have better performance compared to the GA-MLR model.

To assess the predictive ability of QSAR models, many statistical terms, such as correlation coefficient (R2), leave-one-out cross-validation (LOOCV), root mean squared error (RMSE), and external and internal validation were used. The results of validation methods demonstrate the QSAR model to be robust and with high predictivity.

Keywords: Imidazo[4, 5-b]pyridine derivatives, backpropagation artificial neural network (BP-ANN), anticancer potency, genetic algorithm, multiple linear regressions, QSAR.

Graphical Abstract
Martinez-Mayorga, K.; Madariaga-Mazon, A.; Medina-Franco, J.L.; Maggiora, G. The impact of chemoinformatics on drug discovery in the pharmaceutical industry. Expert Opin. Drug Discov., 2020, 15(3), 293-306.
[] [PMID: 31965870]
Khan, E. Pyridine derivatives as biologically active precursors; organics and selected coordination complexes. ChemistrySelect, 2021, 6(13), 3041-3064.
Shimizu, S.; Watanabe, N.; Kataoka, T.; Shoji, T.; Abe, N.; Morishita, S.; Ichimura, H. Pyridine and pyridine derivatives. In: Ullmann’s Encyclopedia of Industrial Chemistry; Wiley-VCH: Weinheim, 2000.
Bakhite, E.A.; Abd-Ella, A.A.; El-Sayed, M.E.A.; Abdel-Raheem, S.A.A. Pyridine derivatives as insecticides. Part 1: synthesis and toxicity of some pyridine derivatives against cowpea aphid, Aphis craccivora Koch (Homoptera: Aphididae). J. Agric. Food Chem., 2014, 62(41), 9982-9986.
[] [PMID: 25226271]
Chan, F.; Sun, C.; Perumal, M.; Nguyen, Q.D.; Bavetsias, V.; McDonald, E.; Martins, V.; Wilsher, N.E.; Raynaud, F.I.; Valenti, M.; Eccles, S.; te Poele, R.; Workman, P.; Aboagye, E.O.; Linardopoulos, S. Mechanism of action of the Aurora kinase inhibitor CCT129202 and in vivo quantification of biological activity. Mol. Cancer Ther., 2007, 6(12), 3147-3157.
[] [PMID: 18089709]
Chiang, C.C.; Lin, Y.H.; Lin, S.F.; Lai, C.L.; Liu, C.; Wei, W.Y.; Yang, S.; Wang, R.W.; Teng, L.W.; Chuang, S.H.; Chang, J.M.; Yuan, T.T.; Lee, Y.S.; Chen, P.; Chi, W.K.; Yang, J.Y.; Huang, H.J.; Liao, C.B.; Huang, J.J. Discovery of pyrrole-indoline-2-ones as Aurora kinase inhibitors with a different inhibition profile. J. Med. Chem., 2010, 53(16), 5929-5941.
[] [PMID: 20681538]
Carpinelli, P.; Ceruti, R.; Giorgini, M.L.; Cappella, P.; Gianellini, L.; Croci, V.; Degrassi, A.; Texido, G.; Rocchetti, M.; Vianello, P.; Rusconi, L.; Storici, P.; Zugnoni, P.; Arrigoni, C.; Soncini, C.; Alli, C.; Patton, V.; Marsiglio, A.; Ballinari, D.; Pesenti, E.; Fancelli, D.; Moll, J. PHA-739358, a potent inhibitor of Aurora kinases with a selective target inhibition profile relevant to cancer. Mol. Cancer Ther., 2007, 6(12), 3158-3168.
[] [PMID: 18089710]
Mahmood, A.; Wang, J.L. Machine learning for high performance organic solar cells: current scenario and future prospects. Energy Environ. Sci., 2021, 14(1), 90-105.
Mahmood, A.; Irfan, A.; Wang, J.L. Machine learning for organic photovoltaic polymers: A minireview. Chin. J. Polym. Sci., 2022, 40(8), 870-876.
Nassar, E. Synthesis,(in vitro) antitumor and antimicrobial activity of some pyrazoline, pyridine, and pyrimidine derivatives linked to indole moiety. J. Am. Sci., 2010, 6(8), 463-471.
Altundas, A.; Ayvaz, S.; Logoglu, E. Synthesis and evaluation of a series of aminocyanopyridines as antimicrobial agents. Med. Chem. Res., 2011, 20(1), 1-8.
Altaf, A.A.; Shahzad, A.; Gul, Z.; Rasool, N.; Badshah, A.; Lal, B.; Khan, E. A review on the medicinal importance of pyridine derivatives. J. Drug Des. Med. Chem, 2015, 1(1), 1-11.
Ahmadinejad, N.; Shafiei, F.; Isfahani, T.M. Quantitative Structure- Property Relationship (QSPR) investigation of camptothecin drugs derivatives. Comb. Chem. High Throughput Screen., 2018, 21(7), 533-542.
[] [PMID: 30264675]
Verma, J.; Khedkar, V.; Coutinho, E. 3D-QSAR in drug design - a review. Curr. Top. Med. Chem., 2010, 10(1), 95-115.
[] [PMID: 19929826]
Wang, F.; Yang, W.; Li, Z.; Zhou, B. Studies on molecular mechanism between SHP2 and pyridine derivatives by 3D-QSAR, molecular docking and MD simulations. J. Saudi Chem. Soc., 2021, 25(11), 101346.
Arba, M.; Azali, H.; Ombe, S.; Armid, A.; Usman, I. 3D-QSAR, molecular docking and dynamics simulation of difluorophenol pyridine derivatives as RSK2 inhibitor. J. Appl. Pharm. Sci., 2019, 9(6), 1-9.
Shirvani, P.; Fassihi, A. Molecular modelling study on pyrrolo[2,3- b]pyridine derivatives as c-Met kinase inhibitors: A combined approach using molecular docking, 3D-QSAR modelling and molecular dynamics simulation. Mol. Simul., 2020, 46(16), 1265-1280.
Momohji̇moh Ovaku, İ.; Stephehe Eyi̇je, A.; Gi̇deon Adamu, S.; Adamu, U. QSAR and molecular docking studies of novel thiophene, pyrimidine, coumarin, pyrazole and pyridine derivatives as potential anti-breast cancer agent. Turkish Comp. Theo. Chem., 2020, 4(1), 12-23.
Algamal, Z.Y.; Lee, M.H.; Al-Fakih, A.M.; Aziz, M. High-dimensional QSAR prediction of anticancer potency of imidazo[4,5-b]pyridine derivatives using adjusted adaptive LASSO. J. Chemometr., 2015, 29(10), 547-556.
Hajibabaei, M.; Shafiei, F.; Abdoli-Senejani, M. Quantitative modeling for prediction of thermodynamic properties of some pyridine derivatives using molecular descriptors and genetic algorithm‐multiple linear regressions. J. Chin. Chem. Soc., 2020, 67(4), 514-538.
Sharma, M.C. Comparative pharmacophore modeling and QSAR studies for structural requirements of some substituted 2-aminopyridine derivatives as inhibitors of nitric oxide synthases. Interdiscip. Sci., 2015, 7(2), 100-112.
[] [PMID: 26202943]
Abdelrahman, M.A.; Salama, I.; Gomaa, M.S.; Elaasser, M.M.; Abdel-Aziz, M.M.; Soliman, D.H. Design, synthesis and 2D QSAR study of novel pyridine and quinolone hydrazone derivatives as potential antimicrobial and antitubercular agents. Eur. J. Med. Chem., 2017, 138, 698-714.
[] [PMID: 28715707]
Tong, J.; Zhao, X.; Zhong, L. QSAR studies of imidazo[4,5-b]pyridine derivatives as anticancer drugs using RASMS method. Med. Chem. Res., 2014, 23(11), 4883-4892.
Howard, S.; Berdini, V.; Boulstridge, J.A.; Carr, M.G.; Cross, D.M.; Curry, J.; Devine, L.A.; Early, T.R.; Fazal, L.; Gill, A.L.; Heathcote, M.; Maman, S.; Matthews, J.E.; McMenamin, R.L.; Navarro, E.F.; O’Brien, M.A.; O’Reilly, M.; Rees, D.C.; Reule, M.; Tisi, D.; Williams, G.; Vinković, M.; Wyatt, P.G. Fragment-based discovery of the pyrazol-4-yl urea (AT9283), a multitargeted kinase inhibitor with potent aurora kinase activity. J. Med. Chem., 2009, 52(2), 379-388.
[] [PMID: 19143567]
Tuzun, B.; Yavuz, S.C.; Sabanci, N.; Saripinar, E. 4D-QSAR study of some pyrazole pyridine carboxylic acid derivatives by electron conformational-genetic algorithm method. Curr. Comput.-. Aided Drug Des., 2018, 14(4), 370-384.
[] [PMID: 29756584]
Mohseni Bababdani, B.; Mousavi, M. Gravitational search algorithm: A new feature selection method for QSAR study of anticancer potency of imidazo[4,5-b]pyridine derivatives. Chemom. Intell. Lab. Syst., 2013, 122, 1-11.
Liu, X.H.; Xu, X.Y.; Tan, C.X.; Weng, J.Q.; Xin, J.H.; Chen, J. Synthesis, crystal structure, herbicidal activities and 3D-QSAR study of some novel 1,2,4-triazolo[4,3- a]pyridine derivatives. Pest Manag. Sci., 2015, 71(2), 292-301.
[] [PMID: 24753294]
Zhang, J.; Hao, Q.Q.; Liu, X.; Jing, Z.; Jia, W.Q.; Wang, S.Q.; Xu, W.R.; Cheng, X.C.; Wang, R.L. Molecular docking, 3D-QSAR and structural optimization on imidazo-pyridine derivatives dually targeting AT1 and PPARγ. Oncotarget, 2017, 8(15), 25612-25627.
[] [PMID: 28445965]
Bavetsias, V.; Sun, C.; Bouloc, N.; Reynisson, J.; Workman, P.; Linardopoulos, S.; McDonald, E. Hit generation and exploration: Imidazo[4,5-b]pyridine derivatives as inhibitors of Aurora kinases. Bioorg. Med. Chem. Lett., 2007, 17(23), 6567-6571.
[] [PMID: 17933533]
Bavetsias, V.; Large, J.M.; Sun, C.; Bouloc, N.; Kosmopoulou, M.; Matteucci, M.; Wilsher, N.E.; Martins, V.; Reynisson, J.; Atrash, B.; Faisal, A.; Urban, F.; Valenti, M.; de Haven Brandon, A.; Box, G.; Raynaud, F.I.; Workman, P.; Eccles, S.A.; Bayliss, R.; Blagg, J.; Linardopoulos, S.; McDonald, E. Imidazo[4,5-b]pyridine derivatives as inhibitors of Aurora kinases: Lead optimization studies toward the identification of an orally bioavailable preclinical development candidate. J. Med. Chem., 2010, 53(14), 5213-5228.
[] [PMID: 20565112]
Mauri, A.; Consonni, V.; Pavan, M.; Todeschini, R. Dragon software: An easy approach to molecular descriptor calculations. Match, 2006, 56(2), 237-248.
Simon, D. Evolutionary optimization algorithms; John Wiley & Sons: Hoboken, 2013.
Mahmood, A.; Wang, J.L. A time and resource efficient machine learning assisted design of non-fullerene small molecule acceptors for P3HT-based organic solar cells and green solvent selection. J. Mater. Chem. A Mater. Energy Sustain., 2021, 9(28), 15684-15695.
Sardari, S.; Sardari, D. Applications of artificial neural network in AIDS research and therapy. Curr. Pharm. Des., 2002, 8(8), 659-670.
[] [PMID: 11945163]
Srikanth, S.; Mehar, A. Development of MLR, ANN and ANFIS Models for estimation of PCUs at different levels of service. J. Soft Comput. Civ. Eng., 2018, 2(1), 18-35.
Thapliyal, A.; Khar, R.K.; Chandra, A. Artificial neural network modelling of green synthesised silver nanoparticles in bentonite/starch bio-nanocomposite. Curr. Nanosci., 2018, 14(3), 239-251.
Shi, L.M.; Fang, H.; Tong, W.; Wu, J.; Perkins, R.; Blair, R.M.; Branham, W.S.; Dial, S.L.; Moland, C.L.; Sheehan, D.M. QSAR models using a large diverse set of estrogens. J. Chem. Inf. Comput. Sci., 2001, 41(1), 186-195.
[] [PMID: 11206373]
Schüürmann, G.; Ebert, R.U.; Chen, J.; Wang, B.; Kühne, R. External validation and prediction employing the predictive squared correlation coefficient test set activity mean vs training set activity mean. J. Chem. Inf. Model., 2008, 48(11), 2140-2145.
[] [PMID: 18954136]
Consonni, V.; Ballabio, D.; Todeschini, R. Evaluation of model predictive ability by external validation techniques. J. Chemometr., 2010, 24(3-4), 194-201.
Consonni, V.; Ballabio, D.; Todeschini, R. Comments on the definition of the Q2 parameter for QSAR validation. J. Chem. Inf. Model., 2009, 49(7), 1669-1678.
[] [PMID: 19527034]
Chirico, N.; Gramatica, P. Real external predictivity of QSAR models: How to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient. J. Chem. Inf. Model., 2011, 51(9), 2320-2335.
[] [PMID: 21800825]
Lin, L.I.K. Assay validation using the concordance correlation coefficient. Biometrics, 1992, 48(2), 599-604.
Hemmer, M.C.; Steinhauer, V.; Gasteiger, J. Deriving the 3D structure of organic molecules from their infrared spectra. Vib. Spectrosc., 1999, 19(1), 151-164.
Consonni, R.T.V. Handbook of Molecular Descriptors; Wiley: VCH Weinheim, Germany, 2000.
González, M.P.; Gándara, Z.; Fall, Y.; Gómez, G. Radial Distribution Function descriptors for predicting affinity for vitamin D receptor. Eur. J. Med. Chem., 2008, 43(7), 1360-1365.
[] [PMID: 18068275]
Xu, J.; Zhang, H.; Wang, L.; Liang, G.; Wang, L.; Shen, X.; Xu, W. QSPR study of absorption maxima of organic dyes for dye-sensitized solar cells based on 3D descriptors. Spectrochim. Acta A Mol. Biomol. Spectrosc., 2010, 76(2), 239-247.
[] [PMID: 20381412]
Olasupo, S.B.; Uzairu, A.; Shallangwa, G.; Uba, S. QSAR analysis and molecular docking simulation of norepinephrine transporter (NET) inhibitors as anti-psychotic therapeutic agents. Heliyon, 2019, 5(10), e02640.
[] [PMID: 31692668]
Ghasemi, G.; Mohamadzade, R. A 2D/3D-QSAR study on biological activities of 1, 2-ethylendiamine derivatives as anti-tuberculosis drugs. J. Chil. Chem. Soc., 2018, 63(4), 4173-4177.
Gasteiger, J.; Schuur, J.; Selzer, P.; Steinhauer, L.; Steinhauer, V. Finding the 3D structure of a molecule in its IR spectrum. Fresenius J. Anal. Chem., 1997, 359(1), 50-55.
González, M.P.; Suárez, P.L.; Fall, Y.; Gómez, G. Quantitative structure–activity relationship studies of vitamin D receptor affinity for analogues of 1α,25-dihydroxyvitamin D3. 1: WHIM descriptors. Bioorg. Med. Chem. Lett., 2005, 15(23), 5165-5169.
[] [PMID: 16202592]
Gramatica, P. Principles of QSAR models validation: Internal and external. QSAR Comb. Sci., 2007, 26(5), 694-701.
Todeschini, R.; Bettiol, C.; Giurin, G.; Gramatica, P.; Miana, P.; Argese, E. Modeling and prediction by using WHIM descriptors in QSAR studies: Submitochondrial particles (SMP) as toxicity blosensors of chlorophenols. Chemosphere, 1996, 33(1), 71-79.
Todeschini, R.; Vighi, M.; Finizio, A.; Gramatica, P. 3D-modelling and prediction by WHIM descriptors. Part 8. Toxicity and physico-chemical properties of environmental priority chemicals by 2D-TI and 3D-WHIM descriptors. SAR QSAR Environ. Res., 1997, 7(1-4), 173-193.
[] [PMID: 9501508]
Todeschini, R.; Gramatica, P.; Provenzani, R.; Marengo, E. Weighted holistic invariant molecular descriptors. Part 2. Theory development and applications on modeling physicochemical properties of polyaromatic hydrocarbons. Chemom. Intell. Lab. Syst., 1995, 27(2), 221-229.
Patel, H.; Cronin, M.T.D. A novel index for the description of molecular linearity. J. Chem. Inf. Comput. Sci., 2001, 41(5), 1228-1236.
[] [PMID: 11604022]
Nikolova, N.; Jaworska, J. Approaches to measure chemical similarity–a review. QSAR Comb. Sci., 2003, 22(9-10), 1006-1026.

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