In silico evaluation and docking studies of pyrazole analogs as potential autophagy modulators against pancreatic cancer cell line MIA PaCa-2 European Journal of Chemistry 11 (3) (2020) 187-193 European Journal of Chemistry ISSN 2153-2249 (Print) / ISSN 2153-2257 (Online) – Copyright © 2020 The Authors – Atlanta Publishing House LLC – Printed in the USA. This work is published and licensed by Atlanta Publishing House LLC – CC BY NC – Some Rights Reserved. http://dx.doi.org/10.5155/eurjchem.11.3.187-193.1976 European Journal of Chemistry View Journal Online View Article Online In silico evaluation and docking studies of pyrazole analogs as potential autophagy modulators against pancreatic cancer cell line MIA PaCa-2 Hiba Hashim Mahgoub Mohamed 1,2,*, Amna Bint Wahab Elrashid Mohammed Hussien 3 and Ahmed Elsadig Mohammed Saeed 1 1 Department of Chemistry, College of Science, Sudan University of Science and Technology, 2288, Khartoum, Sudan hibahashim.m@hotmail.com (H.H.M.M.); aemsaeed@gmail.com (A.E.M.S.) 2 Department of Chemistry, Ibn Sina University, 10995, Khartoum, Sudan 3 College of Animal Production Science and Technology, Sudan University of Science and Technology, 2288, Khartoum, Sudan amnaelrasheed@gmail.com (A.B.W.E.M.H.) * Corresponding author at: Department of Chemistry, College of Science, Sudan University of Science and Technology, 2288, Khartoum, Sudan. e-mail: hibahashim.m@hotmail.com (H.H.M. Mohamed). 10.5155/eurjchem.11.3.187-193.1976 Received: 28 February 2020 Received in revised form: 30 May 2020 Accepted: 17 June 2020 Published online: 30 September 2020 Printed: 30 September 2020 A quantitative structure activity relationship (QSAR) model for a series of N-(1-benzyl-3,5- dimethyl-1H-pyrazole-4-yl) benzamide derivatives having autophagy inhibitory activities as potent anticancer agents was developed by the multiple linear regressions (MLR) method. In this study, previous compounds were used in the model development were divided into a set of fifteen compounds as training set and set of four compounds as test set. A model with high prediction ability and high correlation coefficients was obtained. This model showed r = 0.968, r2 = 0.937 and Q2 = 0.880, the QSAR model was also employed to predict the experimental compounds in an external test set, and to predict the activity of a new designed set of 3,5-dimethyl-4-substituted-pyrazole derivatives (1-15), result showed that compound 3 has the most promising inhibition activity (EC50 = 0.869 μM) against human pancreatic ductal adenocarcinoma cell MIA PaCa-2 compared to the reference chloroquine with (EC50 = 14 μM). Thus, the model showed good correlative and predictive ability. Docking studies was performed for designed compounds, docking analysis showed the best compound 1 with high docking affinity of -24.8616 kcal/mol. QSAR Cancer Autophagy MIA PaCa-2 Molecular modeling Autophagy inhibitions Cite this: Eur. J. Chem. 2020, 11(3), 187-193 Journal website: www.eurjchem.com 1. Introduction Autophagy is a conserved intracellular degradation process that delivers substrates including bulk cytoplasm, organelles, aggregate-prone proteins, and infectious agents to lysosomes [1,2], it is induced under various conditions of cellular stress, which prevents cell damage and promotes survival in the event of energy or nutrient shortage. There are three types of autophagy: macroautophagy [3], microautophagy [4] and Chaperone-mediated autophagy (CMA) [5]. The molecular mechanism of autophagy involves several conserved ATG (autophagy-related) proteins, various stimuli lead to the formation of the phagophore, the elongation of the phagophore results in the formation of the characteristic double-membrane autophagosome. The autophagosome fuses with the lysosome and release its inner compartment into lysosomal lumen, after fusion, a series of acid hydrolases are involved in degradation of the sequestered cytoplasmic cargo. The small molecules resulting from the degradation, particularly amino acids are transported back to the cytosol for protein synthesis and maintenance of cellular functions under starvation conditions [6-9]. In cancer, the role of autophagy is highly complex and dependent on cancer type and stage [10], autophagy has been shown to act as a tumor suppressing to constrains tumor initiation in normal tissue, some tumor in last stage of progression rely on autophagy for tumor promotion and maintenance [11-13]. Therefore, targeting autophagy and discovering autophagy inhibitions and its modulation has considerable potential as anticancer agents used in therapeutic approach, especially in pancreatic ductal adenocarcinoma (PDAC), which it represents a viable approach to fight pancreatic cancer. Quantitative structure activity relationships is a mathematical equation relating chemical structure with its physical, chemical and biological effect [14], QSAR model is useful for understanding the factors controlling activity and for designing new compounds for therapeutic areas [15-17], it requires a compound set that has been tested against an identified molecular target, cell tissue, or even microorganism, ABSTRACT RESEARCH ARTICLE KEYWORDS http://dx.doi.org/10.5155/eurjchem.11.3.187-193.1976 http://www.eurjchem.com/ http://dx.doi.org/10.5155/eurjchem.11.3.187-193.1976 mailto:hibahashim.m@hotmail.com mailto:aemsaeed@gmail.com mailto:amnaelrasheed@gmail.com mailto:hibahashim.m@hotmail.com http://www.eurjchem.com/ https://crossmark.crossref.org/dialog/?doi=10.5155/eurjchem.11.3.187-193.1976&domain=pdf&date_stamp=2020-09-30 188 Mohamed et al. / European Journal of Chemistry 11 (3) (2020) 187-193 2020 – European Journal of Chemistry – CC BY NC – DOI: 10.5155/eurjchem.11.3.187-193.1976 Table 1. Biological activities and structures of N-(1-benzyl-3,5-dimethyl-1H-pyrazole-4-yl) benzamide compounds obtained from literature [22]. N N CH3 NH CH3 O OR1 R2 R3 N N CH3 NH CH3 OO R1 R2 R3 O N H H3C N N CH3 NH CH3 O O O N H H3C 1-8 9 10-19 Compound R1 R2 R3 EC50 pEC50 1 H H H 10.00 5.000 2 H C(O)NH2 H 9.20 5.036 3 H H C(O)NH2 42.00 4.377 4 H C(O)NHMe H 6.20 5.208 5 H C(O)N(Me)2 H 6.40 5.194 6 H C(O)NHEt H 11.00 4.959 7 H C(O)NHiPr H 8.90 5.051 8 H C(O)NHPh H 6.40 5.194 9 - - 14.00 4.854 10 H Me H 6.20 5.208 11 OMe H H 8.50 5.071 12 H OMe H 6.20 5.208 13 H H OMe 8.00 5.097 14 Cl H H 6.30 5.201 15 H Cl H 5.90 5.229 16 H H Cl 4.20 5.377 17 H CF3 H 2.30 5.638 18 H H CF3 0.80 6.097 19 Me H CF3 0.62 6.208 Chloroquine - - - 14.00 4.854 under the same experimental conditions and possesses the minimum variance in the observed responses [18]. Once a suitable dataset has been selected, the main step of modeling requires molecular/physicochemical properties, followed by variable selection, model generation from different algorithms and validation process using internal and external dataset [19- 21]. The current work aimed to obtain a QSAR model of N-(1- benzyl-3,5-dimethyl-1H-pyrazole-4-yl) benzamide derivatives in order to predict biological activity against human pancreatic ductal adenocarcinoma cell MIA PaCa-2, validate the predictive ability of the developed model through validation methods, calculate the statistical parameter to prove quality of model, and use the obtained model to predict the biological activity against human pancreatic ductal adenocarcinoma cell MIA PaCa-2 on a set of designed compounds (1-15). And conducting docking studies for all designed compounds (1-15) and selected protein 6s6a. 2. Experimental 2.1. QSAR studies 2.1.1. Data set A data set comprised of nineteen N-(1-benzyl-3,5-dimethyl- 1H-pyrazole-4-yl) benzamide derivatives was used in the present study. All compounds and associated data were obtained from literature [22]. The biological activity data were reported as (EC50) values half maximal effective concentration in MIA PaCa-2a pancreatic cancer line. The (EC50) values were converted into (pEC50) using the formula: pEC50 = -log EC50, values along with the N-(1-benzyl-3,5-dimethyl-1H-pyrazole-4- yl) benzamide derivatives structures can be found on Table 1. Chemical structures of the compounds were done using the ACD/ChemSketch v14.01 software (ACD, Copyright 1994-2013 Advanced Chemistry Development, Inc.), molecular modeling was performed using the Molecular Operating Environment software package (MOE, v2009.10; Chemical Computing Group Inc.). The QSAR model was derived from nineteen molecules which were randomly divided into training set of fifteen molecules and test set of four molecules was used to validate QSAR model. 2.1.2. Molecular descriptors generation Different molecular descriptors (physicochemical proper- ties) [23] were calculated for each molecule after the low energy conformer of structures were generated, these descript- tors included electronic, spatial, and structural descriptor were calculated using MOE and ACD lab programs. In order to select the best subset of descriptors, and avoid difficulties in forming QSAR models, hence the predictivity and the generalization of the model fail under these conditions, highly correlated descriptors were excluded using correlation matrix, the nine descriptors used to generate QSAR model denoted as molecular weight (MW), molar volume (MV), molar refractivity (Mr), sum of atomic polarizabilities (S-aPol), total polar surface area (T- pSA), density (D), index of refraction (InR), surface tension (ST), and Log octanol/water partition coefficient (log P(o/w)) reported in Table 2. Mohamed et al. / European Journal of Chemistry 11 (3) (2020) 187-193 189 2020 – European Journal of Chemistry – CC BY NC – DOI: 10.5155/eurjchem.11.3.187-193.1976 Table 2. Values of molecular descriptors calculated for training set. Compound MW MV Mr S-aPol T-pSA D InR ST Log P(o/w) 1 411.5050 359.0000 12.4145 67.3338 55.9685 0.9795 1.6070 43.5000 4.8820 3 454.5300 369.0000 13.2528 71.6626 143.1298 1.0129 1.6310 48.5000 3.8230 4 468.5570 392.1000 13.7479 74.7562 135.8367 1.0049 1.6160 45.3000 4.2180 5 482.5840 413.3000 14.1937 77.8498 122.8609 0.9937 1.6050 43.7000 4.4150 6 482.5840 408.1000 14.2149 77.8498 110.3188 0.9958 1.6100 44.8000 4.5590 7 496.6110 423.3000 14.6494 80.9434 94.9803 0.9829 1.6060 43.5000 5.0210 9 474.6050 392.1000 13.8371 78.7570 137.5979 0.9870 1.6160 45.3000 4.5430 10 482.5840 407.3000 14.1945 77.8498 138.3996 0.9899 1.6110 43.9000 4.5530 11 498.5830 413.7000 14.3987 78.6518 168.2368 1.0106 1.6060 44.3000 3.9237 13 498.5830 413.7000 14.3950 78.6518 174.5284 1.0126 1.6060 44.3000 4.1740 14 503.0020 401.3000 14.2636 76.2694 138.1049 1.0457 1.6230 46.1000 4.8080 15 503.0020 401.3000 14.2598 76.2694 135.7901 1.0411 1.6230 46.1000 4.8470 16 503.0020 401.3000 14.2598 76.2694 136.9149 1.0436 1.6230 46.1000 4.8100 18 536.5540 422.0000 14.3648 77.5204 234.1733 1.0925 1.5860 41.5000 5.1528 19 550.5810 437.2000 14.8190 80.6140 235.1974 1.0725 1.5830 40.4000 5.4858 Table 3. Comparison of squared correlation coefficients of the models. Models r2 Model 1 0.852 Model 2 0.835 Model 3 0.937 Model 4 0.853 Model 5 0.896 Table 4. Statistical parameters used for statistical quality of model. r r2 Q2 s F RMSE P value 0.968 0.937 0.880 0.117 192.638 0.109 0.000 Table 5. Experimental and predicted pEC50 for training set and cross validation against human pancreatic cancer cell line (MIA PaCa-2). Compound pEC50 exp. pEC50 pred. Residuals CV pred. Residuals 1 5.0000 5.1001 -0.1001 5.1383 -0.1383 3 4.3770 4.5695 -0.1925 4.7145 -0.3375 4 5.2080 4.9679 0.2401 4.9343 0.2737 5 5.1940 5.1165 0.0775 5.1021 0.0919 6 4.9590 5.0095 -0.0505 5.0151 -0.0561 7 5.0510 5.1412 -0.0902 5.1837 -0.1327 9 4.8540 4.8826 -0.0286 4.8875 -0.0335 10 5.2080 5.0816 0.1264 5.0616 0.1464 11 5.0710 5.0972 -0.0262 5.1141 -0.0431 13 5.0970 5.1425 -0.0455 5.1541 -0.0571 14 5.2010 5.2244 -0.0234 5.2324 -0.0314 15 5.2290 5.1971 0.0319 5.1860 0.0430 16 5.3770 5.2095 0.1675 5.1538 0.2232 18 6.0970 6.1753 -0.0783 6.2502 -0.1532 19 6.2080 6.2160 -0.0080 6.2228 -0.0148 pEC50 = 1.63148 + 0.60842 × log P(o/w) - 0.01141 × InR + 0.00547 × T-pSA (1) pEC50 = -5.14822 + 0.39163 × log P(o/w) + 0.00728 × MV + 5.51570 × D (2) pEC50 = 3.16492 + 0.12375 × log P(o/w) + 6.97797 × D - 0.12652 × ST (3) pEC50 = 1.85725 + 0.61188 × log P(o/w) + 0.00560 × T-pSA - 0.00363 × S-aPol (4) pEC50 = 5.53650 + 0.44173 × log P(o/w) - 0.06791 × ST + 0.00455 × T-pSA (5) 2.1.3. QSAR model development The QSAR model were developed from the training set compounds where the independent variables molecular descriptors and dependent response variable (pEC50) were subjected to multiple linear regressions (MLR) analysis, several QSAR models were developed. The comparison of squared correlation coefficients of the models reported in Table 3. The resulting QSAR model Equation (3) exhibited a high regression coefficient. The model was justified by statistical parameters such as the correlation coefficient (r), squared correlation coefficient (r2), cross-validated regression coefficient (Q2), standard error of estimate (s), F-test value (F), and the root mean squared error (RMSE), and validated using random test set compounds Table 4, and was evaluated for the robustness of its predictions via the cross-validation coefficient. 2.1.4. Validation of QSAR model The developed model was validated internally by training set compounds using leave-one-out (LOO) cross-validation technique. In this technique, one compound is eliminated from the data set at random in each cycle and the model is built using the rest of the compounds. The model thus formed is used for predicting the activity of the eliminated compound. The process is repeated until all the compounds are eliminated once. The cross-validated regression coefficient (Q2) was calculated. External validation was performed in order to determine the predictive capacity of the developed model as judged by its application for the prediction of test set activity values. The observed activities and those calculated by QSAR model (Equation 3) for training set and test set were presented in Table 5 and 6. 190 Mohamed et al. / European Journal of Chemistry 11 (3) (2020) 187-193 2020 – European Journal of Chemistry – CC BY NC – DOI: 10.5155/eurjchem.11.3.187-193.1976 Table 6. Predicted pEC50 values of test set Compound pEC50 exp. pEC50 pred. Residuals 2 5.0360 4.5665 0.4695 8 5.1940 4.9549 0.2391 12 5.2080 5.1417 0.0663 17 5.6380 6.1317 -0.4937 Table 7. Structures and predicted pEC50 values for designed 3,5-dimethyl-4-substituted-pyrazole derivatives against human pancreatic cancer cell line (MIA PaCa-2). N H N N CH3 N CH3 N OBr N O R N H N N CH3 N CH3 N OBr N NH R HN N H3C N CH3 N R O 1-5 6-10 11-15 Compound R pEC50pred. 1 5.1410 2 O 4.9845 3 CH2 6.0610 4 N CH3 CH3 5.3002 5 OH 4.8785 6 5.2189 7 O 4.9615 8 CH2 5.4439 9 N CH3 CH3 5.4630 10 OH 4.9848 11 5.0520 12 O 4.8002 13 CH2 5.2539 14 N CH3 CH3 5.2989 15 OH 4.7922 Mohamed et al. / European Journal of Chemistry 11 (3) (2020) 187-193 191 2020 – European Journal of Chemistry – CC BY NC – DOI: 10.5155/eurjchem.11.3.187-193.1976 Table 8. Binding scores and interactions of the docked designed 3,5-dimethyl-4-substituted-pyrazole derivatives (1-15) on the active site of 6s6a. Compound S (kcal/mol) Amino acid interaction Type of interaction Length (Å) 1 -24.8616 ArgA37 LysA128 ThrA21 ThrA42 π-cation interaction π-cation interaction Metal complexation (Mg) Metal complexation (Mg) - - 2.16 2.22 2 -22.0658 ArgA37 LysA128 ThrA21 ThrA42 π-cation interaction π-cation interaction Metal complexation (Mg) Metal complexation (Mg) - - 2.16 2.22 3 -20.1774 ArgA37 LysA128 π-cation interaction π-cation interaction - - 4 -24.8373 ArgA37 ThrA21 ThrA42 π-cation interaction Metal complexation (Mg) Metal complexation (Mg) - 2.16 2.22 5 -18.9748 ArgA37 AspA130 π-cation interaction Hydrogen bond - 2.03 6 -22.5543 ArgA37 LysA128 π-cation interaction π-cation interaction - - 7 -18.0398 ArgA37 ArgA37 π-cation interaction Hydrogen bond - 3.04 8 -19.4697 ArgA37 ArgA37 LysA128 π-cation interaction π -cation interaction π-cation interaction - - - 9 -20.3244 ArgA37 π-cation interaction - 10 -22.4642 LysC179 SerC76 π-cation interaction Hydrogen bond - 3.04 11 -21.7914 ArgA37 ArgA37 LysA128 π-cation interaction Hydrogen bond π-cation interaction - 2.97 - 12 -21.0138 ArgA37 LysA128 π-cation interaction π-cation interaction - - 13 -24.0026 ArgA37 LysA128 π-cation interaction π-cation interaction - - 14 -22.5484 ArgA37 LysA128 π-cation interaction π-cation interaction - - 15 -18.7338 ArgA37 LysA128 π-cation interaction π-cation interaction - - 2.1.5. Predict the activity of designed 3,5-dimethyl-4- substituted-pyrazole derivatives Chemical structures of the designed 3,5-dimethyl-4- substituted-pyrazole derivatives (1-15) were done using the ACD/ChemSketch, the developed QSAR model (Equation (3)) was used to predict their activity against human pancreatic ductal adenocarcinoma cell line MIA PaCa-2. The predicted activity expressed as pEC50 along with the structures reported in Table 7. 2.2. Molecular docking Docking is a molecular modelling technique that is used to predict how a protein interacts with small molecules (ligands) by predicting of the most possible type of interaction, the binding affinities, and the orientations of the docked ligands at the active site of the target protein. Molecular docking study was carried out in order to elucidate which of the designed 3,5- dimethyl-4-substituted-pyrazole derivatives (1-15) has the best binding affinity against the mechanistic (or mammalian) target of rapamycin complex 1 (mTORC1). The structure of mTORC1 used in the study was obtained from Protein Data Bank with PDB code 6s6a, structures of the designed 3,5- dimethyl-4-substituted-pyrazole derivatives (1-15) were prepared and saved as mol files, the prepared compounds were docked with prepared structure of 6s6a protein using MOE program. The binding score (S) of the complexes and amino acid interactions are reported in Table 8. 3. Results and discussion 3.1. QSAR studies The studied compounds which were an autophagy modulator showed a promising role as anticancer agents. In the present work, structure activity relationship model was developed that could correlate the structural features with biological activity. The developed model showed squared correlation coefficient (r2 = 0.937) which indicates the correlation between the activity (dependent variable) the molecular descriptors (independent variable) for the training set data, and squared cross-validation (Q2 = 0.880) which indicates that the newly developed QSAR model has a good prediction. Three molecular descriptors denoted as log octanol/water partition coefficient (log P(o/w)), density (D), and surface tension (ST) were significantly correlated with anticancer activity. It is evident from the Equation (3) that among the molecular descriptors, log P(o/w) and D are positively correlated, that mean the biological activity increases when the values of these descriptors are positively increased. On the other hand, the descriptor ST negatively correlated with anticancer activity, that mean the biological activity decreases when the value of this descriptor is increase. Four compounds denoted by (test set) were used as external validation for developed QSAR model, and it was found that the predicted values through the QSAR model show compliance with their experimental values and r2 = 0.990, all statistical parameters calculated to evaluate the quality of the QSAR model were in suitable range. Figure 1 shows the correlation plots of the experimental versus predicted pEC50 values for training set, cross-validation and test set compounds against pancreatic cancer cell line. 3.2. Docking study Molecular docking study was carried out between the target (mTORC1) and designed 3,5-dimethyl-4-substituted-pyrazole derivatives (1-15). All compounds were found to inhibit the receptor by occupying the active sites of the target (mTORC1). The binding affinity values for designed compounds range from -24.8616 to -18.0398 kcal/mol as reported in Table 8. 192 Mohamed et al. / European Journal of Chemistry 11 (3) (2020) 187-193 2020 – European Journal of Chemistry – CC BY NC – DOI: 10.5155/eurjchem.11.3.187-193.1976 (a) (b) (c) Figure 1. Predicted versus experimental pEC50 values of (a) training set, (b) cross validation set, and (c) test set against human pancreatic cancer MIA PaCa- 2. Compound 1 Compound 4 Compound 13 Figure 2. 2D molecular docking model of compounds 1, 4 and 13 with 6s6a. Compound 1 Compound 4 Compound 13 Figure 3. 3D model of the interaction between compounds 1, 4 and 13 with 6s6a. y = 0.9368x + 0.3292 r² = 0.9368 4.2 4.4 4.6 4.8 5.0 5.2 5.4 5.6 5.8 6.0 6.2 6.4 4.2 4.4 4.6 4.8 5.0 5.2 5.4 5.6 5.8 6.0 6.2 6.4 PE C 5 0 Pr ed PEC50 Exp y = 0.9089x + 0.4893 r² = 0.8801 4.2 4.4 4.6 4.8 5.0 5.2 5.4 5.6 5.8 6.0 6.2 6.4 4.2 4.4 4.6 4.8 5.0 5.2 5.4 5.6 5.8 6.0 6.2 6.4 CV Pr ed PEC50 Exp y = 2.5693x - 8.3391 r² = 0.9896 4.2 4.4 4.6 4.8 5.0 5.2 5.4 5.6 5.8 6.0 6.2 6.4 5.0 5.1 5.2 5.3 5.4 5.5 5.6 5.7 PE C 5 0 Pr ed PEC50 Exp Mohamed et al. / European Journal of Chemistry 11 (3) (2020) 187-193 193 2020 – European Journal of Chemistry – CC BY NC – DOI: 10.5155/eurjchem.11.3.187-193.1976 However, three ligands (1, 4, and 13) have higher binding score, which ranges from -24.8616 to -24.0026 kcal/mol, ligand 1 formed two π-cation interaction with ArgA37 and LysA128, and two metal complexations with Mg ion with ThrA21 and ThrA42. Ligand 4 formed three interactions, a π-cation interaction with ArgA37 and two metal complexations with Mg ion with ThrA21 and ThrA42. Ligand 13 formed two π-cation interactions with ArgA37 and LysA128 (Figure 2 and 3). 4. Conclusion In this work, a QSAR study was performed based on theoretical molecular descriptors, the built model serves as a guide for providing the structural requirements affecting the anticancer activity against pancreatic cancer cell line MIA PaCa- 2, through the identification of the most relevant selected molecular descriptors in the models, comprehensive assessment (internal and external validation) indicate that the built QSAR model was robust and satisfactory, and that the selected descriptors could account for the structural features responsible for anticancer drugs activity of the compounds. The QSAR model developed and molecular docking in this study can provide a useful tool to predict the activity of new compounds and also to design new compounds with high activity. Disclosure statement Conflict of interests: The authors declare that they have no conflict of interest. Author contributions: All authors contributed equally to this work. Ethical approval: All ethical guidelines have been adhered. ORCID Hiba Hashim Mahgoub Mohamed http://orcid.org/0000-0002-1294-6130 Amna Bint Wahab Elrashid Mohammed Hussien http://orcid.org/0000-0001-7588-0231 Ahmed Elsadig Mohammed Saeed http://orcid.org/0000-0002-7317-8040 References [1]. Levine, B.; Kroemer, G. Cell 2008, 132(1), 27-42. [2]. Glick, D.; Barth, S.; Macleod, K. F. J. Pathol. 2010, 221(1), 3-12. [3]. Shintani, T.; Klionsky, D. J. Science 2004, 306(5698), 990-995. [4]. Li, W. W.; Li, J.; Bao, J. K. Cell. Mol. Life Sci 2012, 69(7), 1125-1136. [5]. Arias, E.; Cuervo, A. M. Curr. Opin. Cell Biol. 2011, 23(2), 184-189. [6]. 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The full terms of this license are available at http://www.eurjchem.com/index.php/eurjchem/pages/view/terms and incorporate the Creative Commons Attribution-Non Commercial (CC BY NC) (International, v4.0) License (http://creativecommons.org/licenses/by-nc/4.0). By accessing the work, you hereby accept the Terms. This is an open access article distributed under the terms and conditions of the CC BY NC License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited without any further permission from Atlanta Publishing House LLC (European Journal of Chemistry). No use, distribution or reproduction is permitted which does not comply with these terms. Permissions for commercial use of this work beyond the scope of the License (http://www.eurjchem.com/index.php/eurjchem/pages/view/terms) are administered by Atlanta Publishing House LLC (European Journal of Chemistry). http://orcid.org/0000-0002-1294-6130 http://orcid.org/0000-0001-7588-0231 http://orcid.org/0000-0002-7317-8040 http://www.eurjchem.com/index.php/eurjchem/pages/view/terms http://creativecommons.org/licenses/by-nc/4.0 http://www.eurjchem.com/index.php/eurjchem/pages/view/terms 1. Introduction 2. Experimental 2.1. QSAR studies 2.1.1. Data set 2.1.2. Molecular descriptors generation 2.1.3. QSAR model development 2.1.4. Validation of QSAR model 2.1.5. Predict the activity of designed 3,5-dimethyl-4-substituted-pyrazole derivatives 2.2. Molecular docking 3. Results and discussion 3.1. QSAR studies 3.2. Docking study 4. Conclusion Disclosure statement ORCID References PrintField10: PrintField11: PrintField12: PrintField13: PrintField14: PrintField15: PrintField16: PrintField20: PrintField21: PrintField22: PrintField23: PrintField24: PrintField25: PrintField26: