QSAR and docking studies of pyrazole analogs as antiproliferative against human colorectal adenocarcinoma cell line HT-29 European Journal of Chemistry 13 (3) (2022) 319-326 European Journal of Chemistry ISSN 2153-2249 (Print) / ISSN 2153-2257 (Online) – Copyright © 2022 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. https://dx.doi.org/10.5155/eurjchem.13.3.319-326.2259 European Journal of Chemistry View Journal Online View Article Online QSAR and docking studies of pyrazole analogs as antiproliferative against human colorectal adenocarcinoma cell line HT-29 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, Khartoum, 2288, Sudan 2 Department of Chemistry, Ibn Sina University, Khartoum, 10995, Sudan 3 College of Animal Production Science and Technology, Sudan University of Science and Technology, Khartoum, 2288, Sudan * Corresponding author at: Department of Chemistry, College of Science, Sudan University of Science and Technology, Khartoum, 2288, Sudan. e-mail: hibahashim.m@hotmail.com (H.H.M. Mohamed). 10.5155/eurjchem.13.3.319-326.2259 Received: 10 March 2022 Received in revised form: 21 April 2022 Accepted: 01 July 2022 Published online: 30 September 2022 Printed: 30 September 2022 In-silico quantitative structure-activity relationship (QSAR) study was performed to develop a model on a series of novel pyrazole derivatives containing acetamide moiety which exhibited considerable antiproliferative activity against human colorectal adenocarcinoma cell line HT-29. The model obtained has a correlation coefficient (r) of 0.9693, squared correlation coefficient (r2) of 0.9395 and a leave-one-out (LOO) cross-validation coefficient (Q2) value of 0.8744. The predictive power of the developed model was confirmed by the external validation which has an r2 value of 0.9488. These parameters confirm the stability and robustness of the model to predict the activity of a new designed set of 3,5-dimethyl- pyrazole derivatives (22-36), results indicated that the compounds 26, 31, 35, and 36 showed the strongest antiproliferative activity with (IC50 = 0.182, 0.172, 0.166 and 0.024 μM, respectively) against human colorectal adenocarcinoma cell line HT-29 compared to the reference vemurafenib with (IC50 = 1.52 μM). Molecular docking was performed on the new designed compounds with the human colorectal adenocarcinoma cell line 5JRQ protein. The docking results showed that compounds 26, 31, 35, and 36 have docking affinity of -8.528, - 5.932, 23.017 and 18.432 kcal/mol, respectively. QSAR HT-29 Pyrazole Molecular modeling Antiproliferative activity Colorectal adenocarcinoma Cite this: Eur. J. Chem. 2022, 13(3), 319-326 Journal website: www.eurjchem.com 1. Introduction Colorectal cancer (CRC) is the term that refers to colon cancer (CC) and rectal cancer (RC), which is considered as a single tumor entity [1], is the third most common malignancy and the second in mortality [2]. The reasons for these increases are complex, but genetic and environmental factors play an important role [3-5]. CRC is caused by uncontrolled cell proliferation and division, which occurs when genetic [6], metabolic [7], and carcinogenic factors [8] damage the DNA and inducing mutations [9,10]. This process involves the activation of oncogenes and/or the deactivation of a tumor suppressor gene, leading to uncontrolled cell cycle progression and inactivation of apoptosis. The standard treatment for CRC is chemotherapy, surgery and radiation or a combination of these for advanced stage disease, this approach depends on tumor characteristics, such as location, size, extent of cancer metastasis and the health status of the patient [11]. However, these treatment options have had limited impact as cancer progress which make the development of new anticancer drugs an emergency need. Pyrazole is an unsaturated five-membered ring containing two nitrogen atoms at positions 1 and 2 and, among hetero- cyclic compounds, represents one of the most important chemical scaffolds in medicinal chemistry [12], it is displaying a broad spectrum of pharmaceutical and biological activity such as anti-cancer, anti-inflammatory, anti-fungal, anti-bacterial, anti-insecticidal, analgesic, anticonvulsant, anti-diabetic, anti- pyretic, anti-arrhythmic, anti-depressant, anti-hyperglycemic, anti-oxidant, and herbicidal etc. [13-15]. Quantitative structure-activity relationships have been applied for decades in the development of relationships between physicochemical properties of chemical substances and their biological activities to obtain a reliable statistical model for prediction of the activities of new chemical entities and improve the inhibitory of drugs. The fundamental principle underlying the formalism is that the difference in structural properties is responsible for the variations in biological activities of the compounds [16], QSAR modeling involves main steps: (i) Model building by collecting the data set compounds, (ii) Model validation with internal validation using training set compounds to assess its quality, and (iii) Model validation with ABSTRACT RESEARCH ARTICLE KEYWORDS https://dx.doi.org/10.5155/eurjchem.13.3.319-326.2259 https://www.eurjchem.com/ https://dx.doi.org/10.5155/eurjchem.13.3.319-326.2259 mailto:hibahashim.m@hotmail.com http://www.eurjchem.com/ https://crossmark.crossref.org/dialog/?doi=10.5155/eurjchem.13.3.319-326.2259&domain=pdf&date_stamp=2022-09-30 320 Mohamed et al. / European Journal of Chemistry 13 (3) (2022) 319-326 2022 – European Journal of Chemistry – CC BY NC – DOI: 10.5155/eurjchem.13.3.319-326.2259 Table 1. Biological activities and structures of pyrazole containing acetamide moiety compounds obtained from literature [19]. N N R1 H N R2 O Compound R1 R2 IC50 (µM) pIC50 1 -CH3 H3CO H3CO 32.80 4.48 2 -CH2CH3 25.62 4.59 3 8.43 5.07 4 11.54 4.94 5 H3C 8.73 5.06 6 O H3C 9.33 5.03 7 O H3C 7.98 5.10 8 -CH2CH3 N 49.87 4.30 9 12.35 4.91 10 26.55 4.58 11 H3C 24.14 4.62 12 O H3C 12.74 4.89 13 O H3C 10.13 4.99 14 -CH3 N N Cl 22.56 4.65 15 -CH2CH3 12.92 4.89 16 5.70 5.24 17 O H3C 2.17 5.66 18 -CH3 N N N 25.36 4.60 19 -CH2CH3 26.79 4.57 20 12.65 4.90 21 O H3C 6.16 5.21 Vemurafenib S O OH N F F O N H N Cl 1.52 5.82 an external validation using test set compounds to assess its predictability [17,18]. The current study aimed to obtain a QSAR model of a series of novel pyrazole derivatives containing acetamide moiety in order to predict antiproliferative activity against human colorectal adenocarcinoma cell line HT-29, 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 antiproli- ferative activity against human colorectal adenocarcinoma cell line HT-29, on a set of designed compounds (22-36) and conducting docking studies for the designed compounds on the active site of selected protein 5JRQ. Mohamed et al. / European Journal of Chemistry 13 (3) (2022) 319-326 321 2022 – European Journal of Chemistry – CC BY NC – DOI: 10.5155/eurjchem.13.3.319-326.2259 Table 2. Values of molecular descriptors calculated for the training set *. Compound Dipole moment, D ETotal, kcal/mol E, kcal/mol Etor, kcal/mol a-acc a-don MW, g/mol HVSA Log P(o/w) 1 1.515 -83153.773 57.982 2.660 4.000 1.000 275.308 264.303 0.621 2 1.282 -86747.898 55.685 -0.516 4.000 1.000 289.335 281.535 0.962 3 1.646 -102128.05 82.666 3.463 4.000 1.000 351.406 334.802 2.409 4 1.981 -98533.953 84.380 2.105 4.000 1.000 337.379 317.569 2.275 5 1.556 -102129.97 84.620 3.244 4.000 1.000 351.406 334.802 2.573 7 1.175 -109505.21 91.910 1.518 5.000 1.000 367.405 347.301 2.268 8 1.373 -75766.867 28.839 -0.913 2.000 1.000 268.320 254.273 1.762 9 0.575 -91148.398 55.477 3.107 2.000 1.000 330.391 307.539 3.209 10 1.082 -87554.789 55.431 2.594 2.000 1.000 316.364 290.307 3.075 11 1.803 -98528.367 65.436 2.663 3.000 1.000 346.390 320.039 3.031 13 1.081 -98526.953 62.793 2.650 3.000 1.000 346.390 320.039 3.068 14 3.334 -88489.617 35.376 1.618 3.000 1.000 315.764 280.564 1.189 15 3.278 -92083.945 33.123 -1.529 3.000 1.000 329.791 297.796 1.530 16 3.725 -107464.77 59.373 2.556 3.000 1.000 391.862 351.062 2.977 17 2.671 -81687.093 39.118 2.625 4.000 1.000 282.307 255.444 -0.516 19 2.693 -85274.539 44.899 -0.677 4.000 1.000 296.334 272.676 -0.175 20 3.306 -100656.38 71.281 3.465 4.000 1.000 358.405 325.942 1.273 21 2.047 -108038.72 76.969 3.103 5.000 1.000 374.404 338.442 1.132 * Total energy (ETotal), potential energy (E), torsion energy (ETor), number of H-bond acceptor atoms (a-acc), number of H-bond donor atoms (a-don), molecular weight (MW), total hydrophobic VdW surface area (HVSA), and log octanol/water partition coefficient Log P(o/w). Table 3. Statistical parameters used for statistical quality of model. r r2 Q2 s F RMSE p-value 0.9693 0.9395 0.8744 0.0680 248.592 0.0640 0.0000 2. Experimental 2.1. QSAR studies 2.1.1. Data set A set comprised of 21 derivatives of pyrazole containing acetamide moiety which showed antiproliferative activity against human colorectal adenocarcinoma cell line HT-29 reported by Wang et al. [19] was used in the present study, and their antiproliferative activities were expressed as IC50 values half maximal inhibitory concentration. The IC50 (μM) values were converted into IC50 (M)and then to pIC50 values using the formula pIC50 = - log [IC50]. The structures and pIC50 values of the derivatives of pyrazole containing the acetamide moiety are listed in Table 1. The chemical structures of the compounds done made using the ACD/ChemSketch v.14.01 software (Copyright 1994-2013, Advanced Chemistry Development, Inc.) [20]. Molecular modeling was performed using the Molecular Operating Environment software package (MOE, v2009.10; Chemical Computing Group Inc.) [21]. The data set was randomly divided into a training set that comprises 80% of the dataset that was used to build the QSAR model, while the remaining 20% of the dataset test set was used to validate the QSAR model (18 and 3 molecules, respectively). 2.1.2. Molecular descriptor generation Molecular descriptors were calculated for each molecule after they were subjected to energy minimization, and these descriptors include 3D descriptors (e.g., dipole moment, total energy, potential energy, and torsion energy) and 2D descriptors (e.g., number of H-bond acceptor atoms, number of H-bond donor atoms, molecular weight, total hydrophobic van der Waals (VdW) surface area, and log octanol/water partition coefficient). These descriptors were calculated using MOE programs. In order to select the best subset of descriptors, highly correlated descriptors were excluded using correlation matrix then ratio of molecules to the descriptors used is 5:1. The nine descriptors used to generate the QSAR model are denoted as dipole moment, total energy (ETotal), potential energy (E), torsion energy (ETor), number of H-bond acceptor atoms (a-acc), number of H-bond donor atoms (a-don), molecular weight (MW), total hydrophobic VdW surface area (HVSA) and log octanol/water partition coefficient Log P(o/w) and are listed in Table 2. 2.1.3. QSAR model development The correlation of the calculated descriptors with each other was calculated and collinear descriptors were specified, those with higher correlation towards activity were retained and the others were eliminated. Subsequently, multiple linear regression (MLR) analysis were performed on the training set. Where calculated molecular descriptors served as an independent variable and observed inhibition (pIC50) values were used as a dependent variable. Several QSAR models were developed and the resulting QSAR model Equation (1) showed a high regression coefficient. The values of the regression coefficient and statistical parameters are listed in Table 3. pIC50 = 1.94587 – 0.00342 × E + 0.01671 × ETor + 0.01030 × HVSA – 0.04588 × log P(o/w) (1) 2.1.4. Validation of the QSAR model To evaluate the robustness of the model, internal validation of the training set was performed using the 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 cross- validated regression coefficient (Q2) values were thereafter calculated according to Equation (1). External validation was performed to determine the predictive capacity of the developed model by its application to predict the values of the test set. The observed activities and those calculated by QSAR model (Equation (1)) for the training set and test set were presented in Tables 4 and 5. 2.1.5. Predict the activity of designed 3,5-dimethylpyrazole derivatives (22-36) The chemical structures of the 3,5-dimethylpyrazole derivatives designed (22-36) were carried out using the ACD/ChemSketch, the QSAR model developed (Equation (1)) was used to predict their activity against the human colorectal adenocarcinoma cell line (HT-29). The predicted activity expressed as pIC50 along with the structures reported in Table 6. 322 Mohamed et al. / European Journal of Chemistry 13 (3) (2022) 319-326 2022 – European Journal of Chemistry – CC BY NC – DOI: 10.5155/eurjchem.13.3.319-326.2259 Table 4. Experimental and predicted pIC50 for training set and cross-validation against human colorectal adenocarcinoma cell line (HT-29). Compound pIC50exp. pIC50pred. Residuals CVpred. Residuals 1 4.480 4.486 -0.006 4.489 -0.009 2 4.590 4.602 -0.012 4.607 -0.017 3 5.070 5.059 0.011 5.057 0.013 4 4.940 4.859 0.081 4.831 0.109 5 5.060 5.041 0.019 5.038 0.023 7 5.100 5.130 -0.030 5.146 -0.046 8 4.300 4.370 -0.070 4.411 -0.111 9 4.910 4.828 0.082 4.803 0.107 10 4.580 4.649 -0.069 4.676 -0.096 11 4.890 4.924 -0.034 4.929 -0.039 13 4.990 4.931 0.060 4.921 0.069 14 4.650 4.687 -0.037 4.697 -0.047 15 4.890 4.804 0.086 4.732 0.158 16 5.240 5.265 -0.025 5.282 -0.042 17 4.600 4.511 0.090 4.428 0.173 19 4.570 4.598 -0.028 4.610 -0.040 20 4.900 5.059 -0.159 5.100 -0.200 21 5.210 5.168 0.042 5.152 0.058 Table 5. Predicted pIC50 values of the test set. Compound pIC50exp. pIC50pred. Residuals 6 5.030 5.132 -0.102 12 4.620 4.803 -0.183 18 5.660 5.358 0.302 Table 6. Structures and predicted pIC50 values for 3,5-dimethylpyrazole derivatives designed against human colorectal adenocarcinoma cell line (HT-29). N N O NH R1 R2 Compound R1 R2 pIC50pred. 22 - Br N N 4.970 23 - Br 5.237 24 H2N O O S 5.156 25 O O S H N H3C O N 6.000 26 CH3O H3C O N N H N O O S 6.739 27 - N N S O O H2N 4.890 28 - Br 5.152 29 H2N O O S 5.061 30 O O S H N H3C O N 5.933 31 CH3O H3C O N N H N O O S 6.764 32 - N N S O O N H NO H3C 5.745 33 - Br 6.003 34 H2N O O S 5.916 35 O O S H N H3C O N 6.780 36 CH3O H3C O N N H N O O S 7.627 Mohamed et al. / European Journal of Chemistry 13 (3) (2022) 319-326 323 2022 – European Journal of Chemistry – CC BY NC – DOI: 10.5155/eurjchem.13.3.319-326.2259 Table 7. Binding scores and interactions of the designed 3,5-dimethyl-pyrazole derivatives (22-36) docked on the active site of (5JRQ). Compound S (kcal/mol) Amino acid interaction Type of interaction 22 -6.087 Phe583 Arene - Arene interaction 23 -9.438 No interaction - 24 -9.175 Thr529 Phe583 Hydrogen bond Arene - Arene interaction 25 -9.924 Phe583 Arene - Arene interaction 26 -8.528 Lys483 Phe583 Arene - Cation interaction Arene - Arene interaction 27 -2.714 Gly596 Lys483 Phe583 Hydrogen bond Arene - Cation interaction Arene - Arene interaction 28 -3.594 Gly596 Lys483 Phe583 Hydrogen bond Arene - Cation interaction Arene - Arene interaction 29 -4.358 Lys483 Phe583 Arene - Cation interaction Arene - Arene interaction 30 -6.082 Lys483 Phe583 Arene - Cation interaction Arene - Arene interaction 31 -5.932 Lys483 Phe583 Arene - Arene interaction Arene - Arene interaction 32 7.929 Lys483 Phe583 Hydrogen bond Arene - Arene interaction 33 11.112 Lys483 Phe583 Arene - Cation interaction Arene - Arene interaction 34 11.500 Lys483 Phe583 Arene - Cation interaction Arene - Arene interaction 35 23.017 Asp594 Lys483 Lys483 Phe583 Hydrogen bond Arene - Cation interaction Arene - Cation interaction Arene - Arene interaction 36 18.432 Thr529 Asp594 Lys483 Hydrogen bond Arene - Cation interaction Arene - Cation interaction Figure 1. Predicted versus experimental pIC50 values of training set against human colorectal adenocarcinoma cell line (HT-29). 2.2. Molecular docking The docking simulation was carried out using the MOE program [21]. For this purpose, the structure of protein was obtained from Protein Data Bank with PDB code (5JRQ) [22], structures of the new designed 3,5-dimethylpyrazole deriva- tives (22-36) were build using ACD/ChemSketch v.14.01 software [20] then saved as mol file, then docking simulation was performed. The binding score (S) of the complexes and amino acid interactions are listed in Table 7. 3. Results and discussion 3.1. QSAR studies In the present work, the structure-activity relationship model was developed to correlate the structural characteristics with the biological response of the compounds studied that had antiproliferative activity against the human colorectal adeno- carcinoma cell line HT-29. The developed model showed a squared correlation coefficient (r2 = 0.9395) which indicates the correlation between the activity (dependent variable) the molecular descriptors (independent variable) for the training set data, and squared cross-validation (Q2 = 0.8744) which indicates that the newly developed QSAR model has a good prediction. Four molecular descriptors denoted as potential energy (E), torsion energy (ETor), total hydrophobic VdW surface area and log octanol/water partition coefficient Log P(o/w) were significantly correlated with antiproliferative activity against the human colorectal adenocarcinoma cell line HT-29. Equation (1) shows that among the molecular descriptors, ETor and HVSA are positively correlated, which means that biological activity increases when the values of these descriptors are positively increased. On the other hand, the descriptor E and Log P(o/w) are negatively correlated with antiproliferative activity, that mean the biological activity decreases when the value of this descriptor is increase. y = 0.9395x + 0.2924 r² = 0.9395 4.20 4.40 4.60 4.80 5.00 5.20 5.40 4.20 4.40 4.60 4.80 5.00 5.20 5.40 pI C5 0 p re d pIC50exp 324 Mohamed et al. / European Journal of Chemistry 13 (3) (2022) 319-326 2022 – European Journal of Chemistry – CC BY NC – DOI: 10.5155/eurjchem.13.3.319-326.2259 Figure 2. Predicted versus experimental pIC50 values of cross validation against human colorectal adenocarcinoma cell line (HT-29). Figure 3. Predicted versus experimental pIC50 values of the test set against the human colorectal adenocarcinoma cell line (HT-29). (a) (b) Figure 4. (a) 2D molecular docking model of compound 36 with 5JRQ and (b) 3D model of the interaction between compound 36 and the binding site of 5JRQ. Test set compress of three compounds which 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 = 0.9488), all statistical parameters calculated to evaluate the quality of the QSAR model were in suitable range. Figures 1-3 show the correlation graphs of the experimental versus predicted pIC50 values for the training set, cross-validation, and test set compounds against the human colorectal adenocarcinoma cell line (HT-29), respectively. 3.2. Docking study A molecular docking study was performed between the target (5JRQ) and designed 3,5-dimethylpyrazole derivatives (22-36). All compounds were found to inhibit the receptor by occupying the active sites of the target (5JRQ). The binding affinity values for designed compounds range from to -9.438 to 23.017 kcal/mol as reported in Table 7 (Figures 4-6). y = 0.923x + 0.3686 r² = 0.8744 4.20 4.40 4.60 4.80 5.00 5.20 5.40 4.20 4.40 4.60 4.80 5.00 5.20 5.40 CV pr ed pIC50exp y = 0.5189x + 2.4494 r² = 0.9488 4.60 4.70 4.80 4.90 5.00 5.10 5.20 5.30 5.40 5.50 4.50 4.70 4.90 5.10 5.30 5.50 5.70 5.90 pI C5 0 p re d pIC50exp Mohamed et al. / European Journal of Chemistry 13 (3) (2022) 319-326 325 2022 – European Journal of Chemistry – CC BY NC – DOI: 10.5155/eurjchem.13.3.319-326.2259 (a) (b) Figure 5. (a) 2D molecular docking model of compound 35 with 5JRQ and (b) 3D model of the interaction between compound 35 and the binding site of 5JRQ. (a) (b) Figure 6. (a) 2D molecular docking model of compound 31 with 5JRQ and (b) 3D model of the interaction between compound 31 and the binding site of 5JRQ. 4. Conclusions The developed QSAR model presents a satisfactory correlation with inhibition activity against the human colorectal adenocarcinoma cell line HT-29, and met the criteria for the minimum recommended value of validation parameters for a generally acceptable QSAR model, and molecular docking analysis has shown that all new compounds have good inhibitor activity. The generated QSAR model provides a valuable approach for ligand-base design, while molecular docking studies provide a valuable approach for structure-base design. These two approaches will be of great help to pharmaceutical and medicinal chemists in designing and synthesis of a new antiproliferative agent. Disclosure statement Conflict of interest: The authors declare that they have no conflict of interest. Ethical approval: All ethical guidelines have been adhered. Sample availability: Samples of the compounds are available from the author. CRediT authorship contribution statement Conceptualization: Ahmed Elsadig Mohammed Saeed, Hiba Hashim Mahgoub Mohamed; Methodology: Hiba Hashim Mahgoub Mohamed, Ahmed Elsadig Mohammed Saeed; Software: Hiba Hashim Mahgoub Mohamed; Validation: Hiba Hashim Mahgoub Mohamed; Formal Analysis: Hiba Hashim Mahgoub Mohamed; Investigation: Amna Bint Wahab Elrashid Mohammed Hussien, Ahmed Elsadig Mohammed Saeed; Resources: Hiba Hashim Mahgoub Mohamed, Amna Bint Wahab Elrashid Mohammed Hussien; Data Curation: Hiba Hashim Mahgoub Mohamed; Writing - Original Draft: Hiba Hashim Mahgoub Mohamed; Writing - Review and Editing: Hiba Hashim Mahgoub Mohamed, Ahmed Elsadig Mohammed Saeed, Amna Bint Wahab Elrashid Mohammed Hussien. ORCID and Email Hiba Hashim Mahgoub Mohamed hibahashim.m@hotmail.com https://orcid.org/0000-0002-1294-6130 Amna Bint Wahab Elrashid Mohammed Hussien amnaelrasheed@gmail.com https://orcid.org/0000-0001-7588-0231 Ahmed Elsadig Mohammed Saeed aemsaeed@gmail.com https://orcid.org/0000-0002-7317-8040 References [1]. El Bali, M.; Bakkach, J.; Bennani Mechita, M. Colorectal cancer: From genetic landscape to targeted therapy. J. Oncol. 2021, 2021, 9918116. [2]. Sawicki, T.; Ruszkowska, M.; Danielewicz, A.; Niedźwiedzka, E.; Arłukowicz, T.; Przybyłowicz, K. E. A review of colorectal cancer in terms of epidemiology, risk factors, development, symptoms and diagnosis. Cancers (Basel) 2021, 13. [3]. Bray, F.; Ferlay, J.; Soerjomataram, I.; Siegel, R. L.; Torre, L. A.; Jemal, A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2018, 68, 394–424. [4]. Picard, E.; Verschoor, C. P.; Ma, G. W.; Pawelec, G. 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Design, synthesis, and biological evaluation of pyrazole derivatives containing acetamide bond as potential BRAF V600E inhibitors. Bioorg. Med. Chem. Lett. 2018, 28, 2382–2390. [20]. ACD/ChemSketch, version 14.01, Advanced Chemistry Development, Inc. (ACD/Labs), Toronto, ON, Canada, www.acdlabs.com (accessed June 2, 2022). [21]. Molecular Operating Environment (MOE), 2009.10 Chemical Computing Group ULC, 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2022. [22]. Berman, H. M.; Henrick, K.; Nakamura, H. Announcing the worldwide Protein Data Bank Nature Structural Biology 10 (12): 980, 2003, https://www.rcsb.org/structure/5JRQ (accessed June 2, 2022). Copyright © 2022 by Authors. This work is published and licensed by Atlanta Publishing House LLC, Atlanta, GA, USA. 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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://www.acdlabs.com/ https://www.rcsb.org/structure/5JRQ 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 descriptor generation 2.1.3. QSAR model development 2.1.4. Validation of the QSAR model 2.1.5. Predict the activity of designed 3,5-dimethylpyrazole derivatives (22-36) 2.2. Molecular docking 3. Results and discussion 3.1. QSAR studies 3.2. Docking study 4. Conclusions Disclosure statement CRediT authorship contribution statement ORCID and Email References PrintField10: PrintField11: PrintField12: PrintField13: PrintField14: PrintField15: PrintField16: PrintField17: PrintField20: PrintField21: PrintField22: PrintField23: PrintField24: PrintField25: PrintField26: PrintField27: