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\title{A DFT-Based QSAR and Molecular Docking Studies on Potent Anti-Colon Cancer Activity of Pyrazole Derivatives}
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             \author[1]{Abel Kolawole  Oyebamiji}

             \affil[1]{  Ladoke Akintola University of Technology}

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\date{\small \em Received: 6 December 2017 Accepted: 31 December 2017 Published: 15 January 2018}

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\begin{abstract}
        


Pyrazolederivatives have been described as a group of compounds with various biological activities including anticancer effect. Therefore, a set of twenty Pyrazole based compounds which had been previously shown to be active against human colon cancer cell (HT29) are use in the study. These compounds were optimized using Density Functional Theory (DFT) for the calculations of molecular descriptors that related the bioactivity of these compounds to their structures. The developed quantitative structure activity relation (QSAR) was validated, and it showed the reliability and acceptability of the model. The in silico simulations were carried out on the twenty Pyrazole based compounds with colon cancer cell line, HT29 (PDB ID: 2N8A) using Autodock vina software. The docked complexes were validated and enumerated based on the AutoDock Scoring function to pick out the best inhibitors based on docked Energy. The analysis of the ligand-receptor complexes showed that H-bonds played a prominent role in the binding and posed stability of the ligand in the ligandreceptor complexes. The binding free energy, Î?"G calculated ranged from - 6.10 kcal/mol â??" 8.20 Kcal/mol. 

\end{abstract}


\keywords{pyrazole derivatives, DFT-QSAR, molecular docking.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
ancer is not a contagious neither infectious disease, but it has become a second leading cause of death worldwide and travels from one end to the other via bloodstream within the body system 1,2   . It can be caused by both external and internal factors, e.g. tobacco, infectious organisms, chemicals, and radiation are for outside while inherited mutations, hormones, and immune conditions for internal factor. Moreover, all of these factors may work together or in series to start or enhance carcinogenesis \hyperref[b11]{3} . The cure for cancer remain surgery, chemotherapy and radiation therapy and adult, as well as, children can be affected. However, cases of children having cancer are limited \hyperref[b12]{4} .\par
Colon cancer as a worldwide known health problem forays more than a million people every year, which has been the cause of death to over 600,000 people \hyperref[b13]{5} . It is found to be the usual cause of death in comparison to other types of cancer that exist  {\ref 6,}\hyperref[b15]{7} . Several features that may cause an increment in colon cancer risks comprise diet, diabetes, aging, obesity, genomic instability, etc. Over the last half-century, the United States Food and Drug Administration (USFDA) has approved more than one hundred drugs for clinical treatment of cancers. Nevertheless the search for new and/or improved chemical compounds as potential anticancer agents continues with the hope that better efficacy and more manageable adverse side effects of pharmaceutical drugs may be achieved. Molecular modeling, screening and mimicking of natural compound derivatives have been among several drug discovery approaches to rationally design and modified structures that may confer a better therapeutic index \hyperref[b16]{8} or that can cure cancer in the human race \hyperref[b17]{9} .\par
Among such compounds are pyrazole and its derivatives, pyrazoles are class of heterocyclic compounds used for the development of drugs, and they have attracted the attention of several researchers due to their extensive biotic actions such as anticancer \hyperref[b18]{10} , antifungal \hyperref[b19]{11} , antiviral \hyperref[b21]{12} , antiinflammatory \hyperref[b23]{14,}\hyperref[b24]{15} . More so, pyrazole derivatives such as Pyrazolopyrimidine and pyrazolo [4,3-d]pyrimdin-7-one perform some pharmacological activities which can never be put aside in the medical world, for example as antihypertensive \hyperref[b24]{15} , antiviral \hyperref[b25]{16,}\hyperref[b26]{17} , tuberculostatic \hyperref[b27]{18} , herbicidal agents \hyperref[b28]{19} , antileishmanial \hyperref[b29]{20} and treatment of heart diseases \hyperref[b30]{21} . Therefore, the structural features of the Pyrazoles have been recognized as vital parameters due to their bioactivity as therapeutic aids. Several pyrazoles have been commercialized such as omeprazole, Albendazole, mebendazole, candesartan, telmisartan, astemizole \hyperref[b31]{[22]}\hyperref[b32]{[23]}\hyperref[b33]{[24]} .\par
Quantitative Structural Activity Relationship (QSAR) as a statistical model embroils the relationship between physicochemical parameters of a chemical compound to its biological activity \hyperref[b34]{25} . It has attracted vast usefulness for linking molecular evidence with biotic activities and many other physicochemical properties as well as its helpfulness for drug design, discovery, and development \hyperref[b35]{26} . QSAR helps in the prediction of toxicity of materials in bulk system, for instance, drug-like compounds and are very useful in case of the classic chemicals \hyperref[b36]{[27]}\hyperref[b37]{[28]}\hyperref[b39]{[29]}\hyperref[b40]{[30]} .\par
The use of molecular descriptors calculated from quantum chemical methods for development of QSAR models has been described to be sufficient for generating comprehensive QSAR. Thus the use of quantum chemical descriptors has countless potential \hyperref[b41]{[31]}\hyperref[b42]{[32]}\hyperref[b43]{[33]}\hyperref[b44]{[34]} .\par
Molecular docking studies divulge information on the interaction between the drug-like compound known as a ligand and an enzyme/receptor through recognizing the active positions within the enzyme along with the binding energy calculation \hyperref[b44]{34} . In molecular docking, scoring is a statistical way of predicting the strength of the interactions which are non-covalent in between a ligand and a receptor. Therefore, the calculations of interaction energy can be offered in the form of "dock score" \hyperref[b45]{35} .\par
Consequently, in this research, twenty pyrazole derivatives with known anti-colon cancer activities \hyperref[b30]{21} as displayed in Figure  {\ref 1} were optimized using Density Functional Theory (DFT) method so as to obtain molecular descriptors for the compounds. These   {\ref , b}). Thus, the major objectives of this work are: (i) to calculate molecular descriptors with the use of quantum chemical method via Density Functional Theory (DFT), (ii) to develop QSAR model which probe into biological activity of the studied compounds, and (iii) to calculate the free energy of interactions (binding affinity, Î?"G) of the ligand with the receptor in the binding site through molecular docking.\par
Figure  {\ref 1:} The schematic structures of the pyrazole derivatives, the compounds were numbered as used in [20]   II. 
\section[{Computational Details a) Ligand optimization and molecular descriptors}]{Computational Details a) Ligand optimization and molecular descriptors}\par
The equilibrium geometries for the twenty optimized at Density Functional Theory (DFT). The use of DFT method entails three-parameter density functional, which comprises Becke's gradient exchange correction \hyperref[b47]{37} and the Lee, Yang, Parr correlation functional (i.e., B3LYP) \hyperref[b48]{38} . The accuracy of DFT calculations depends on the particular functional chosen and basis sets. However, 6-31G** basis set has been found to be appropriate for the confirmation search and calculation of drug-like compounds \hyperref[b49]{39} . Therefore 6-31G** basis set was used in this work. Also, the optimized compounds were used to calculate molecular parameters/descriptors that described the bioactivity (IC 50 ) of the compounds. The optimized molecular structures were used for the docking study to estimate the binding affinity of the compounds to the colon cancer cell line, HT29 receptor (PDB ID: 2N8A). The optimization of the compounds was carried out using quantum chemical software Spartan '14 by wavefunctionInc \hyperref[b50]{40} .  (S = 1/?) eV -1 Electro negativity [µ = 1/2 (E HOMO -E LUMO )] eV Nucleophilicity (? = µ 2 /2?) eV DM P HOMO LUMO SE (N+N)/2 BD ? S µ ? 
\section[{b) Data processing and QSAR modeling}]{b) Data processing and QSAR modeling}\par
Furthermore, the chosen calculated parameters were engaged to develop quantitative structure-activity relationship (QSAR) model to link the bioactivity to the calculated molecular descriptors obtained from the studied compounds \hyperref[b51]{41} . This was achieved using multiple linear regression (MLR) method which is a recurrent statistical technique used in developing QSAR model. MLR and correlation analyses were carried out by the statistics software SPSS 13.0 version. Before MLR analysis, the person correlation table was used to examine collinearity among the descriptors (r > 0.90). The descriptors with higher correlation with the dependent variable (IC 50 ] were retained, and the others were removed from the descriptor data matrix. The remaining descriptors were used to construct the MLR model, by the stepwise method. Moreover, the QSAR model was validated using some statistical equations such as cross validation (R 2 ) and adjusted R 2 . Cross validation is a mathematical method which oversees the reliability of QSAR model that can be used for a set of facts as shown in equation 1.CV. R 2 = 1 ? ?(? obs ?? cal ) 2 ?(? obs ?? obs ) 2 (1)\par
The adjusted R 2 could be calculated using equation ( \hyperref[formula_2]{2})R a 2 = (N?I)×R 2 ?P N?P?1 (\textbf{2})\par
where N is number of observations (compounds), p is number of descriptors, Also, for a good model, the standard error of estimate (s) of a set of data should be low, and this is defined as follows:?? = ? ?(?? ?????? ??? ?????? ) 2 ??????1 (3)\par
To judge the overall significance of the regression coefficients, the variance ratio (F) which is the ration of regression mean square to deviations mean square can be defined as follows: \par
The F value has two degrees of freedom: p, N ? p ? 1. The computed F value of a model should be significant at p < 0.05; thus for overall significance of the regression coefficients, the F value should be high. 
\section[{c) Molecular Docking and binding affinity}]{c) Molecular Docking and binding affinity}\par
The downloaded HT29 receptor (PDB ID: 2N8A \hyperref[b52]{42} ) from protein data bank was treated i.e., removal of water molecules, ligand, and cofactors from the receptor with the use of discovery studio. Then, both the receptor and ligand were converted to the acceptable format (pdbqt) for AutoDockvina programme. The grid dimension used for all the 2N8A protein was are 50 × 40 ×40 Å (grid size) with point separated by 1.000 Å (grid-point spacing). The docking was done using autodock vina which was inspired by Darwinian evolution theory to be iterative optimization method \hyperref[b54]{43} which involves search Algorithm. At the completion of the docking runs, ligand showing different conformations known as Binding modes were obtained with their respective binding affinity. The stable pattern was assumed to be the one with the lowest binding affinity and was taken for post-docking analysis using Edupymol version 1.7.4.4. 
\section[{III.}]{III.}\par
Result and Discussion a) QSAR modeling The molecular descriptors calculated for the twenty pyrazole compounds served as independent variables, while the observed inhibitory actions (IC 50, µM) against cancer cells line as the dependent variable in the development of QSAR model via multiple linear regression (MLR). These molecular descriptors used for QSAR model were displayed in Tables \hyperref[tab_2]{2 and 3}. In QSAR study, the quality of a model is evaluated by its fitting and prediction abilities; however, for a model to be acceptable, its predictability power is of paramount important. Therefore, Pearson's matrix was used for the selection of suitable descriptors for the QSAR study (Table \hyperref[tab_3]{4}).  The selected descriptors were used to build a linear QSAR model to understand how multiple linear regression (MLR) equations can explain the structural key points correlating to differential behavior in bioactivity against colon cancer cell (HT29) as shown in equation 4. This model was validated statistically by using the squared fitting factor (R 2 ), cross validation (CV.R 2 ), adjusted fitting factor ( adj R 2 ) and variation ratio (F). The developed model was very robust in predicting satisfactory the experimental values. The high values of F, R 2 , CV.R 2 and adj R 2 as shown in Table \hyperref[tab_4]{5} indicated that the models are statistically acceptable and also have good external predictability \hyperref[b55]{44,}\hyperref[b56]{45} . The calculated R 2 is 0.9564; this revealed a reasonable fitness, and it also uncovered the efficiency of the model as displayed in equation 4. The value for CV.R 2 was calculated to be 0.9542 which is greater than 0.5 (standard) \hyperref[b57]{46} , and this showed the reliability and acceptability of the model as well as the adjusted R 2 with 0.9247 which was greater than 0.6 (Table \hyperref[tab_4]{5}) IC\par
The QSAR model contained eight descriptors in different combinations; each descriptor with either positive or negative coefficient attached to it. However, the magnitudes of the coefficients as well as, the values of descriptors have significant roles in deciding the overall biological activity of the molecule. The descriptors with negative coefficients in the model were very significant because they contributed towards increasing the value of the biological activity of anti-colon cancer agents. Therefore, the descriptors with a negative coefficients were most significant followed by descriptors with low weight positive coefficients and lastly the parameters with high weight positive coefficients. The predicted anti-colon cancer activity of the ligands using the QSAR model as well as deviation from the experimental values was displayed in Table \hyperref[tab_5]{6} and graphically presented in Figures 2 and 3.  Observed IC 50 (?M)\par
Predicted IC 50 (?M) The molecular docking studies were performed on the twenty pyrazole derivatives together with colon cancer cells line (PDB ID: 2N8A) \hyperref[b52]{42} obtained from protein data bank. This was achieved with the use of several software such as Discovery studio, Autodock tool, Autodock vina and Pymol as post-dock software. Docking of each compound was carried using autodock vina and conformations obtained varies in number but ranged from 8 -15 conformations for compounds 3a-19b. The structure with lowest binding energy (i.e., highest negative free energy of binding, Î?"G) in each docking simulation was taken to be most stable and analyzed for detailed interactions using Discovery Studio Visualizer4.0 software. Docking simulations can be understood by comparing the values of the free energy of binding (Gibbs energy, Î?"G) of the ligands to the protein receptor. Î?"G is an indicator to show the stability interaction between ligand and receptor, and it can be used to explain the strength of binding energies of different docking conformation \hyperref[b58]{47,} {\ref 48} .\par
The poses of the lowest conformation of each ligand were examined based on Î?"G, and interaction of the ligand with the 2N8A protein structure in ligandreceptor complex. The free binding energy (Î?"G) calculated for the docked twenty pyrazole derivatives ranged from -6.10 kcal/mol (ligand 7a) to -8.20 kcal/mol (ligand 19b) as displayed in Table \hyperref[tab_6]{7}. The interaction of ligand with the 2N8A protein structure was discussed by of H-bonding between the ligand and the receptor molecule as shown in Figure \hyperref[fig_0]{4}. Analysis of the ligandreceptor complex showed that H-bonds played a prominent role in the binding and posed stability of the ligand in the ligand-receptor complex; thus affect the potency/function of biological molecules. The number of H-bonds present in the ligand-protein complex as well as H-bonds distances was shown in Table \hyperref[tab_6]{7}   IV. 
\section[{Conclusion}]{Conclusion}\par
In this study, the quantum chemical method via density functional theory (DFT) method was used for calculation of molecular descriptors relating to the anticancer activity of pyrazole derivatives. The QSAR analysis revealed the efficiency of the model developed using multiple linear regression (MLR), and that the QSAR model replicated the observed bioactivities of the studied compounds against colon cancer cells line (ID: 2N8A). Furthermore, the simulated molecular docking predicted stable conformations of the drug-like molecules (Pyrazolederiatives) in the active gouge of the receptor. Also, the binding energy as well as, nature of electrostatic interactions of the ligands in the ligandreceptor complexes were obtained for the twenty compounds.\begin{figure}[htbp]
\noindent\textbf{4}\includegraphics[]{image-2.png}
\caption{\label{fig_0}compounds are 4 -}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-4.png}
\caption{\label{fig_2}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-5.png}
\caption{\label{fig_3}}\end{figure}
     \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.15725\textwidth}P{0.6417499999999999\textwidth}P{0.051\textwidth}}
Descriptors\tabcellsep Symbol\tabcellsep Abbreviation\\
Quantum\tabcellsep Molecular dipole moment\tabcellsep \\
chemical\tabcellsep Molecular polarizability\tabcellsep \\
descriptors\tabcellsep Highest occupied molecular orbital, eV\tabcellsep \\
\tabcellsep Lowest unoccupied molecular orbital, eV\tabcellsep \\
\tabcellsep Solvation energy (au)\tabcellsep \end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.03692162417374882\textwidth}P{0.0834749763928234\textwidth}P{0.0834749763928234\textwidth}P{0.06581680830972615\textwidth}P{0.06581680830972615\textwidth}P{0.13404154863078374\textwidth}P{0.10514636449480642\textwidth}P{0.08106704438149197\textwidth}P{0.09711992445703493\textwidth}P{0.09711992445703493\textwidth}}
MOL\tabcellsep HOMO\tabcellsep LUMO\tabcellsep BG\tabcellsep DM\tabcellsep SE (au)\tabcellsep (N+N)/2 Het\tabcellsep ?\tabcellsep µ\tabcellsep ?\\
3a\tabcellsep -5.54\tabcellsep -1.66\tabcellsep 3.98\tabcellsep 5.26\tabcellsep -0.01389\tabcellsep -0.379\tabcellsep 3.600\tabcellsep -1.940\tabcellsep 0.5227\\
3b\tabcellsep -5.66\tabcellsep -1.85\tabcellsep 3.81\tabcellsep 3.33\tabcellsep -0.01208\tabcellsep -0.379\tabcellsep 3.755\tabcellsep -1.905\tabcellsep 0.4832\\
4\tabcellsep -5.87\tabcellsep -1.60\tabcellsep 4.27\tabcellsep 6.35\tabcellsep -0.02134\tabcellsep -0.353\tabcellsep 3.735\tabcellsep -2.135\tabcellsep 0.6102\\
5\tabcellsep -6.58\tabcellsep -1.94\tabcellsep 4.64\tabcellsep 2.54\tabcellsep -0.03482\tabcellsep -0.355\tabcellsep 4.260\tabcellsep -2.455\tabcellsep 0.7074\\
7a\tabcellsep -5.74\tabcellsep -1.41\tabcellsep 4.33\tabcellsep 3.51\tabcellsep -0.02187\tabcellsep -0.398\tabcellsep 3.575\tabcellsep -2.165\tabcellsep 0.6556\\
7b\tabcellsep -5.66\tabcellsep -1.80\tabcellsep 3.86\tabcellsep 3.76\tabcellsep -0.01747\tabcellsep -0.399\tabcellsep 3.730\tabcellsep -1.930\tabcellsep 0.4993\\
9\tabcellsep -5.96\tabcellsep -1.36\tabcellsep 4.60\tabcellsep 4.23\tabcellsep -0.01815\tabcellsep -0.405\tabcellsep 3.660\tabcellsep -2.300\tabcellsep 0.7227\\
10a\tabcellsep -5.84\tabcellsep -0.82\tabcellsep 5.02\tabcellsep 5.19\tabcellsep -0.01575\tabcellsep -0.368\tabcellsep 3.330\tabcellsep -2.510\tabcellsep 0.9460\\
10b\tabcellsep -5.84\tabcellsep -1.65\tabcellsep 4.33\tabcellsep 1.68\tabcellsep -0.01442\tabcellsep -0.368\tabcellsep 3.815\tabcellsep -2.165\tabcellsep 0.6143\\
10c\tabcellsep -6.07\tabcellsep -1.80\tabcellsep 4.27\tabcellsep 1.81\tabcellsep -0.01671\tabcellsep -0.377\tabcellsep 3.935\tabcellsep -2.135\tabcellsep 0.5792\\
10d\tabcellsep -5.56\tabcellsep -1.44\tabcellsep 4.12\tabcellsep 2.91\tabcellsep -0.01841\tabcellsep -0.368\tabcellsep 3.500\tabcellsep -3.128\tabcellsep 1.3973\\
12\tabcellsep -5.99\tabcellsep -1.31\tabcellsep 4.68\tabcellsep 4.11\tabcellsep -0.01977\tabcellsep -0.401\tabcellsep 3.650\tabcellsep -2.340\tabcellsep 0.7501\\
13\tabcellsep -6.23\tabcellsep -2.09\tabcellsep 4.14\tabcellsep 2.26\tabcellsep -0.01402\tabcellsep -0.413\tabcellsep 4.160\tabcellsep -2.070\tabcellsep 0.5150\\
14\tabcellsep -5.91\tabcellsep -2.09\tabcellsep 3.82\tabcellsep 4.67\tabcellsep -0.01784\tabcellsep -0.427\tabcellsep 4.000\tabcellsep -1.910\tabcellsep 0.4560\\
15\tabcellsep -5.91\tabcellsep -1.99\tabcellsep 4.39\tabcellsep 0.76\tabcellsep -0.01789\tabcellsep -0.424\tabcellsep 4.185\tabcellsep -2.195\tabcellsep 0.5756\\
16\tabcellsep -5.88\tabcellsep -1.45\tabcellsep 4.43\tabcellsep 6.68\tabcellsep -0.01861\tabcellsep -0.418\tabcellsep 3.665\tabcellsep -2.215\tabcellsep 0.6693\\
17\tabcellsep -6.18\tabcellsep -1.89\tabcellsep 4.29\tabcellsep 3.02\tabcellsep -0.02548\tabcellsep -0.391\tabcellsep 4.035\tabcellsep -2.145\tabcellsep 0.5701\\
18\tabcellsep -5.79\tabcellsep -1.41\tabcellsep 4.38\tabcellsep 4.84\tabcellsep -0.03355\tabcellsep -0.396\tabcellsep 3.600\tabcellsep -2.190\tabcellsep 0.6661\\
19a\tabcellsep -5.73\tabcellsep -1.24\tabcellsep 4.49\tabcellsep 4.68\tabcellsep -0.02043\tabcellsep -0.372\tabcellsep 3.485\tabcellsep -2.245\tabcellsep 0.7231\\
19b\tabcellsep -5.70\tabcellsep -1.30\tabcellsep 4.40\tabcellsep 4.39\tabcellsep -0.02085\tabcellsep -0.371\tabcellsep 3.485\tabcellsep -2.200\tabcellsep 0.6914\end{longtable} \par
 
\caption{\label{tab_1}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.03238724727838258\textwidth}P{0.03767496111975117\textwidth}P{0.03833592534992224\textwidth}P{0.03767496111975117\textwidth}P{0.03833592534992224\textwidth}P{0.03767496111975117\textwidth}P{0.03767496111975117\textwidth}P{0.03767496111975117\textwidth}P{0.03833592534992224\textwidth}P{0.03833592534992224\textwidth}P{0.03833592534992224\textwidth}P{0.03833592534992224\textwidth}P{0.03767496111975117\textwidth}P{0.03767496111975117\textwidth}P{0.03767496111975117\textwidth}P{0.03767496111975117\textwidth}P{0.03767496111975117\textwidth}P{0.03767496111975117\textwidth}P{0.038996889580093314\textwidth}P{0.03833592534992224\textwidth}P{0.03833592534992224\textwidth}P{0.05750388802488336\textwidth}}
H HET4r HBD HBA\tabcellsep 0.294 2 6\tabcellsep 0.294 2 6\tabcellsep 0.296 3 7\tabcellsep 0.300 3 8\tabcellsep 0.285 2 6\tabcellsep 0.282 2 6\tabcellsep 0.292 2 5\tabcellsep 0.292 3 6\tabcellsep 0.294 3 6\tabcellsep 0.295 3 6\tabcellsep 0.293 3 7\tabcellsep 0.294 1 4\tabcellsep 0.292 1 4\tabcellsep 0.277 1 5\tabcellsep 0.297 1 6\tabcellsep 0.287 1 6\tabcellsep 0.300 3 7\tabcellsep 0.287 5 8\tabcellsep 0.292 3 7\tabcellsep 0.293 3 7\tabcellsep \\
NHBL\tabcellsep 1.012\tabcellsep 1.011\tabcellsep 1.010\tabcellsep 1.010\tabcellsep 1.008\tabcellsep 1.008\tabcellsep 1.008\tabcellsep 1.010\tabcellsep 1.010\tabcellsep 1.010\tabcellsep 1.010\tabcellsep 1.008\tabcellsep 1.008\tabcellsep 1.006\tabcellsep 1.008\tabcellsep 1.007\tabcellsep 1.009\tabcellsep 1.009\tabcellsep 1.009\tabcellsep 1.009\tabcellsep Year 2018\\
NNBL\tabcellsep 1.322\tabcellsep 1.322\tabcellsep 1.320\tabcellsep 1.329\tabcellsep 1.333\tabcellsep 1.334\tabcellsep 1.345\tabcellsep 1.329\tabcellsep 1.334\tabcellsep 1.336\tabcellsep 1.333\tabcellsep 1.345\tabcellsep 1.354\tabcellsep 1.352\tabcellsep 1.352\tabcellsep 1.351\tabcellsep 1.339\tabcellsep 1.335\tabcellsep 1.327\tabcellsep 1.327\tabcellsep 13\\
Log P PSA Pol\tabcellsep 0.07 63.211 64.57\tabcellsep -0.06 63.228 65.69\tabcellsep -1.01 85.752 63.71\tabcellsep -1.52 100.075 62.61\tabcellsep 0.26 74.597 58.95\tabcellsep 0.83 64.537 59.82\tabcellsep -0.15 72.744 60.29\tabcellsep 0.60 88.836 66.90\tabcellsep 1.62 79.201 66.94\tabcellsep 2.02 79.118 67.71\tabcellsep 1.34 86.232 68.82\tabcellsep 0.10 56.316 59.33\tabcellsep 1.55 37.704 59.98\tabcellsep 1.67 42.724 65.70\tabcellsep 1.83 68.584 60.37\tabcellsep 0.76 64.473 64.95\tabcellsep 0.15 74.925 60.23\tabcellsep -0.96 106.292 61.96\tabcellsep 0.09 89.093 65.42\tabcellsep 1.49 88.388 70.75\tabcellsep Volume XVIII Issue II Version I\\
Ova\tabcellsep 1.48\tabcellsep 1.51\tabcellsep 1.48\tabcellsep 1.46\tabcellsep 1.39\tabcellsep 1.40\tabcellsep 1.42\tabcellsep 1.55\tabcellsep 1.45\tabcellsep 1.55\tabcellsep 1.57\tabcellsep 1.39\tabcellsep 1.40\tabcellsep 1.47\tabcellsep 1.40\tabcellsep 1.52\tabcellsep 1.41\tabcellsep 1.49\tabcellsep 1.53\tabcellsep 1.59\tabcellsep ( D D D D )\\
MOL MW Vol\tabcellsep 3a 315.764 297.32\tabcellsep 3b 350.209 310.90\tabcellsep 4 317.736 287.79\tabcellsep 5 319.708 275.32\tabcellsep 7a 261.672 229.28\tabcellsep 7b 277.739 238.68\tabcellsep 9 275.699 246.62\tabcellsep 10a 341.802 329.28\tabcellsep 10b 357.776 327.84\tabcellsep 10c 374.231 337.13\tabcellsep 10d 369.812 350.35\tabcellsep 12 260.684 235.01\tabcellsep 13 279.130 241.38\tabcellsep 14 326.787 311.07\tabcellsep 15 285.698 246.93\tabcellsep 16 332.747 303.52\tabcellsep 17 293.738 244.92\tabcellsep 18 310.679 266.54\tabcellsep 19a 348.818 309.47\tabcellsep 19b 410.889 374.88\tabcellsep Global Journal of Medical Research\end{longtable} \par
 
\caption{\label{tab_2}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.04707692307692308\textwidth}P{0.005230769230769231\textwidth}P{0.008892307692307692\textwidth}P{0.011507692307692307\textwidth}P{0.014123076923076922\textwidth}P{0.01673846153846154\textwidth}P{0.02196923076923077\textwidth}P{0.026676923076923077\textwidth}P{0.028246153846153844\textwidth}P{0.03295384615384615\textwidth}P{0.034\textwidth}P{0.03713846153846154\textwidth}P{0.0408\textwidth}P{0.04550769230769231\textwidth}P{0.048646153846153846\textwidth}P{0.05073846153846154\textwidth}P{0.053353846153846154\textwidth}P{0.06119999999999999\textwidth}P{0.061723076923076915\textwidth}P{0.06643076923076924\textwidth}P{0.06695384615384616\textwidth}P{0.07009230769230769\textwidth}}
HT29 HOMO LUMO BG DM SE N+N/2HET CP CH GN MW VOL OVA LOGP PSA POL NNBL NHBL HHET4r HBD HBA\tabcellsep HT29 1.000\tabcellsep HOMO -0.009 1.000\tabcellsep LUMO 0.306 0.476 1.000\tabcellsep BG 0.334 -0.349 0.655 1.000\tabcellsep DM 0.280 0.520 0.506 0.102 1.000\tabcellsep SE -0.370 0.372 -0.028 -0.342 -0.064 1.000\tabcellsep N+N/2HET 0.274 0.152 0.320 0.219 0.060 -0.173 1.000\tabcellsep CP -0.434 0.005 -0.448 -0.472 0.092 0.229 -0.343 1.000\tabcellsep CH -0.191 -0.828 -0.888 -0.235 -0.596 -0.177 -0.284 0.283 1.000\tabcellsep GN 0.423 0.230 0.567 0.397 0.039 -0.111 0.356 -0.970 -0.482 1.000\tabcellsep MW 0.283 0.285 0.195 -0.044 0.044 0.125 0.533 -0.225 -0.274 0.284 1.000\tabcellsep VOL 0.295 0.365 0.272 -0.027 0.130 0.233 0.499 -0.272 -0.365 0.355 0.973 1.000\tabcellsep OVALITY 0.456 0.419 0.414 0.078 0.303 0.061 0.501 -0.349 -0.484 0.442 0.913 0.921 1.000\tabcellsep LOGP -0.292 0.049 -0.199 -0.266 -0.375 0.587 -0.434 0.018 0.101 0.015 0.257 0.299 0.152 1.000\tabcellsep PSA 0.524 0.033 0.483 0.482 0.081 -0.631 0.672 -0.459 -0.325 0.439 0.392 0.311 0.457 -0.488 1.000\tabcellsep POL 0.288 0.372 0.258 -0.047 0.128 0.240 0.494 -0.262 -0.360 0.347 0.973 1.000 0.918 0.304 0.300 1.000\tabcellsep NNBL -0.095 -0.463 -0.331 0.031 -0.245 0.078 -0.861 0.044 0.454 -0.137 -0.422 -0.394 -0.446 0.488 -0.580 -0.395 1.000\tabcellsep NHBL 0.240 0.221 0.135 -0.013 -0.074 0.098 0.787 -0.137 -0.202 0.174 0.402 0.380 0.386 -0.334 0.435 0.381 -0.802 1.000\tabcellsep HHET4r 0.088 -0.447 -0.027 0.374 -0.354 -0.135 0.527 -0.289 0.251 0.164 0.143 0.058 0.059 -0.260 0.347 0.050 -0.293 0.606 1.000\tabcellsep HBD 0.565 0.168 0.346 0.219 0.066 -0.541 0.655 -0.273 -0.308 0.294 0.417 0.328 0.445 -0.432 0.873 0.323 -0.600 0.501 0.234 1.000\tabcellsep HBA 0.416 0.048 0.131 0.097 0.096 -0.658 0.579 -0.263 -0.109 0.241 0.422 0.308 0.442 -0.451 0.861 0.305 -0.583 0.410 0.267 0.787 1.000\end{longtable} \par
 
\caption{\label{tab_3}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.04903846153846154\textwidth}P{0.032692307692307694\textwidth}P{0.14711538461538462\textwidth}P{0.19615384615384615\textwidth}P{0.14711538461538462\textwidth}P{0.04903846153846154\textwidth}P{0.11442307692307692\textwidth}P{0.11442307692307692\textwidth}}
N\tabcellsep p\tabcellsep R 2\tabcellsep CV.R 2\tabcellsep R 2\tabcellsep adj\tabcellsep s\tabcellsep F\\
20\tabcellsep 8\tabcellsep 0.9564\tabcellsep 0.9542\tabcellsep \multicolumn{2}{l}{0.9247}\tabcellsep 0.4141\tabcellsep 30.168\end{longtable} \par
 
\caption{\label{tab_4}Table 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6} \par 
\begin{longtable}{P{0.057954545454545446\textwidth}P{0.11590909090909089\textwidth}P{0.12073863636363637\textwidth}P{0.13039772727272728\textwidth}P{0.06519886363636364\textwidth}P{0.11590909090909089\textwidth}P{0.11832386363636362\textwidth}P{0.12556818181818183\textwidth}}
Comp\tabcellsep Observed\tabcellsep Predicted\tabcellsep Residual\tabcellsep Comp\tabcellsep Observed\tabcellsep Predicted\tabcellsep Residual\\
3A\tabcellsep 1.45\tabcellsep 1.45\tabcellsep -0.00\tabcellsep 10D\tabcellsep 3.57\tabcellsep 3.05\tabcellsep 0.52\\
3B\tabcellsep 1.10\tabcellsep 1.57\tabcellsep -0.47\tabcellsep 12\tabcellsep 0.78\tabcellsep 0.54\tabcellsep 0.24\\
4\tabcellsep 2.78\tabcellsep 2.53\tabcellsep 0.25\tabcellsep 13\tabcellsep 1.52\tabcellsep 1.23\tabcellsep 0.29\\
5\tabcellsep 2.99\tabcellsep 2.82\tabcellsep 0.17\tabcellsep 14\tabcellsep 0.88\tabcellsep 0.96\tabcellsep -0.08\\
7A\tabcellsep 0.28\tabcellsep -0.26\tabcellsep 0.54\tabcellsep 15\tabcellsep 0.29\tabcellsep 0.70\tabcellsep -0.41\\
7B\tabcellsep 0.20\tabcellsep 0.31\tabcellsep -0.11\tabcellsep 16\tabcellsep 3.88\tabcellsep 3.86\tabcellsep 0.02\\
9\tabcellsep 1.77\tabcellsep 2.19\tabcellsep -0.42\tabcellsep 17\tabcellsep 1.33\tabcellsep 1.24\tabcellsep 0.09\\
10A\tabcellsep 4.08\tabcellsep 4.16\tabcellsep -0.08\tabcellsep 18\tabcellsep 5.59\tabcellsep 5.65\tabcellsep -0.06\\
10B\tabcellsep 2.10\tabcellsep 2.18\tabcellsep -0.08\tabcellsep 19A\tabcellsep 0.42\tabcellsep 1.09\tabcellsep -0.67\\
10C\tabcellsep 2.92\tabcellsep 2.83\tabcellsep 0.09\tabcellsep 19B\tabcellsep 0.78\tabcellsep 0.62\tabcellsep 0.16\end{longtable} \par
 
\caption{\label{tab_5}Table 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7} \par 
\begin{longtable}{P{0.85\textwidth}}
Year 2018\\
17\end{longtable} \par
 
\caption{\label{tab_6}Table 7 :}\end{figure}
 			\footnote{© 2018 Global JournalsB A DFT-Based QSAR and Molecular Docking Studies on Potent Anti-Colon Cancer Activity of Pyrazole Derivatives} 		 		\backmatter  			  				\begin{bibitemlist}{1}
\bibitem[Likewise]{b5}\label{b5} 	 		\textit{},  		 			Likewise 		.  		 	 	 (ligand 12receptor complex presented two H-bonds) 
\bibitem[]{b38}\label{b38} 	 		\textit{},  		 \xref{http://dx.doi.org/10.7537/marsnys09061610}{10.7537/marsnys09061610}.  		9 p. .  	 
\bibitem[ Inca ()]{b14}\label{b14} 	 		\textit{},  	 	 		\textit{Inca}  		2008. 628.  		 			INCA -Institutonacional de câncerAções de prevençãoprimáriaesecundária no controle do câncer. Rio de Janeiro 		 	 
\bibitem[ Eur J Med ()]{b20}\label{b20} 	 		\textit{},  	 	 		\textit{Eur J Med}  		Chem2008. 43 p. 435.  	 
\bibitem[Pharm ()]{b53}\label{b53} 	 		\textit{},  		 			Ame J Of Pharm 		.  		Sci2014. p. .  	 
\bibitem[However]{b3}\label{b3} 	 		‘(H-amino group of pyrazole ring) and ALA-89/LIG:H (hydrogen of N-H of pyrazole ring). Two H-bonds were observed in docked complex of ligand 10b and receptor; THR-88/LI G: H (H-amino group of pyrazole ring) and ALA-91/LIG:H (hydrogen of N-H of pyrazole ring). For ligand 10c, five H-bonds were observed via THR-176/LIG:H (hydrogen of N-H of carboxamide’.  		 			However 		.  		 GLU-107/LIG:H.  	 	 		\textit{one H-bond was observed between LYR-47 and LIG:N (amino group of pyrazole ring); whereas two H-bonds for 7b with},  				 	 	 (GLU-107/LIG:H (H-amino group of pyrazole ring), GLU-107/LIG:H (hydrogen of N-H of pyrazole ring), GLU-107/LIG: O (Carbonyl oxygen of pyrimidinone). Others were ALA-89/LIG: O (Carbonyl oxygen of pyrimidinone. hydrogen of N-H of pyrazole ring), GLU-107/LIG:O (pyrazole-carbonyl oxygen) and ARG-65/LIG:O (pyrazole-carbonyl oxygen) 
\bibitem[Lig:O (ii Gly-111 Lig:O (iii Thr-109 Lig:H (iv Glu-107 Lig:H (v) Glu-107 Lig: N]{b1}\label{b1} 	 		‘(i) MET-38, LIG: H (ii) GLN-40, LIG:H (i) 2.4 (i) 2.3 9 -6.8 (i) THH-109, LIG:N (ii) GLU-107, LIG:H(iii) GLU-107, LIG:H (iv) GLU-107, LIG: O (v) ALA-89, LIG: O (vi) ILE-64 LIG: O (vii) ILE-64, LIG: H (i) 3.0 (ii) 2.9 (iii) 2.1 (iv) 3.4 (v) 3.2 (vi) 3.6 (vii) 2.5 10a -7.4 (i) THR-109’.  		 LIG: H (i) 3.3 (ii) 2.1 (iii) 2.7 (iv) 2.0 10b -7.2 (i) THR-88.  	 	 		\textit{H (i) 2.8 (ii) 2.5 10c -7.2 (i) THR-176, LIG: H (ii) THR-109, LIG: H (iii) GLU-107, LIG:H (iv) GLU-107},  				 			) Lig:O (ii,  			Gly-111,  			) Lig:O (iii,  			Thr-109,  			) Lig:H (iv,  			Glu-107,  			Lig:H (v) Glu-107,  			Lig: N 		 (ed.)  		 (LIG: H, (ii) ALA-91, LIG)  		 	 	 (LIG: O (ii) THR-109. LIG:H (i) 3.3 (ii) 2.2 13 -6.4 MET-38, LIG:H (i) 2.2 14 -7.0 (i) ASP-6, LIG: H (ii) THR-109, LIG:H (i) 2.4 (ii) 2.5 15 -7.3 (i) GLU-107, LIG:H (ii) THR-109, LIG: N (iii) THR-109, LIG: N (iv) LYS-108, LIG: N (v) LYS-108, LIG: N (i) 2.5 (ii) 2.0 (iii) 2.7 (iv) 
\bibitem[Lig:H (ix Ala-89 Lig:H (x) Glu-107 Lig:H (xi Glu-107 Lig: H (i) 3.5]{b2}\label{b2} 	 		‘(ii) 2.5 (iii) 2.2 (iv) 2.1 (v) 2.2 (vi) 2.6 (vii) 2.4 (viii) 2.5 (ix) 2.5 (x) 2.5 (xi) 2.8 19a -7.2 (i) ALA-106’.  		 LIG: H (viii) GLU-90.  	 	 		\textit{LIG: O (ii) THR-109, LIG:O (iii) LYS-108, LIG: O (iv) GLU-107, LIG:H (v) GLU-107},  				 			) Lig:H (ix,  			Ala-89,  			Lig:H (x) Glu-107,  			) Lig:H (xi,  			Glu-107,  			Lig: H (i) 3.5 		 (ed.)  		 	 	 (LIG:N (ii) GLU-90,LIG:H (iii) GLU-90. LIG: H (i) 2.3 (ii) 2.8 (iii) 2.0 (iv) 2.9 (v) 2.4 (vi) 2.5 (vii) 2.5 (viii) 2.0) 
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\bibitem[Also, five H-bonds were recorded for ligand 10d; ARG-65/LIG:O (pyrazole-carbonyl oxygen]{b4}\label{b4} 	 		 GLU-107/LIG: N.  		\textit{Also, five H-bonds were recorded for ligand 10d; ARG-65/LIG:O (pyrazole-carbonyl oxygen},  				 	 	 (GLY-111/LIG:O (pyrazole-carbonyl oxygen. amino group of pyrazole ring) 
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\bibitem[LIG:O (ii) GLN-40, LIG:H (i)2.5 (ii) 1.8 4 -7.1 (i) ALA-106 LIG: H (ii) THR-109]{b0}\label{b0} 	 		‘LIG:O (ii) GLN-40, LIG:H (i)2.5 (ii) 1.8 4 -7.1 (i) ALA-106’.  		 LIG:O 2.3 3b -7.4 (i) GLN-40.  	 	 		\textit{LIG: H (ii) THR-109},  				 	 	 (LIG:O (i) 2.5 (ii) 1.9 5 -6.9 (i) ALA-89, LIG:O (ii) ILE-64, LIG: O (iii) GLU-107, LIG: O (iv) 
\bibitem[LYS-108/LIG: N (one of N of triazolyl ring) and LYS-108/LIG: N (one of N of triazolyl ring). Moreover, one H-bond was observed for ligand 16-receptor complex viaLYS-47/LIG:O(carboxylic-group) with Hbond distance of 2.4 Å; whereas for ligand 17-receptor complex, six H-bonds were detected. The H-bonds were ALA-89/ LIG:H (hydrogen of N-H of pyrazole ring /LIG:H (hydrogen of N-H of pyrazole ring) with the distance of 2.4 Å and THR-109/LIG:H (hydrogen of N-H of pyrazole ring) with of distance 2.5 Å. Also, for ligand 15, five H-bonds were identified through GLU-107/LIG:H (hydrogen of N-H of pyrazole ring]{b7}\label{b7} 	 		‘LYS-108/LIG: N (one of N of triazolyl ring) and LYS-108/LIG: N (one of N of triazolyl ring). Moreover, one H-bond was observed for ligand 16-receptor complex viaLYS-47/LIG:O(carboxylic-group) with Hbond distance of 2.4 Å; whereas for ligand 17-receptor complex, six H-bonds were detected. The H-bonds were ALA-89/ LIG:H (hydrogen of N-H of pyrazole ring’.  		 GLU-107/LIG:H.  	 	 		\textit{/LIG:H (hydrogen of N-H of pyrazole ring) with the distance of 2.4 Å and THR-109/LIG:H (hydrogen of N-H of pyrazole ring) with of distance 2.5 Å. Also, for ligand 15, five H-bonds were identified through GLU-107/LIG:H (hydrogen of N-H of pyrazole ring},  				 		 			First ketonic-group of pyrimidine-dione ring ; Second Ketonic-group of pyrimidinedione ring ; First ketonic-group of pyrimidine-dione ring 		 	 	 (THR-109/LIG: N (N of N-H of pyrazole ring). hydrogen of N-H of pyrazole ring) and THR-109/LIG:N (N of N-H of pyrazole ring) 
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\bibitem[Also (ed.)]{b8}\label{b8} 	 		\textit{Similarly, for ligand 19a formed six H-bonds with 2N8A},  		 			Also 		.  		 receptor residues vis- à-vis GLY-93/LIG:N, GLU-90/LIG.  		ALA-106/LIG: N, ALA-106/LIG:H, GLU-107/LIG: H, GLU-107/LIG:H, GLU-90/LIG:H, GLY-93/LIG:H. However (ed.)  		18.  	 	 (for ligand 19b, eight H-bonds were observed between the ligand and 2N8A residues. These were THR-109/LIG:O, THR-109/LIG:O, LYS-108/LIG:O, GLU-107/LIG:H, GLU-107/LIG:H, GLU-107/LIG:H, ALA-106/LIG:H and GLU-90/LIG: H. Some selected ligandreceptor (2N8A)) 
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\bibitem[THR-109/LIG:H (hydrogen of N-H of pyrazole ring); whereas MET-38 was H-bonded with hydrogen of N-H of pyrazole ring of the ligand 13]{b6}\label{b6} 	 		 THR-109/LIG:O.  		\textit{THR-109/LIG:H (hydrogen of N-H of pyrazole ring); whereas MET-38 was H-bonded with hydrogen of N-H of pyrazole ring of the ligand 13},  				 	 	 (with bond distance of 2.2 Å. For ligand 14, two H-bonds were observed in the ligand-receptor complex; ASP-) 
\end{bibitemlist}
 			 		 	 
\end{document}
