Biomedical Research

Research Article - Biomedical Research (2017) Volume 28, Issue 13

Molecular docking and in silico ADMET study reveals 3-(5-{[4-(aminomethyl) piperidin-1-yl] methyl}-1h-indol-2-yl)-1h-indazole-6-carbonitrile as a potential inhibitor of cancer Osaka thyroid kinase

Afzal Hussain* and Chandan Kumar Verma

Department of Bioinformatics, MANIT, Bhopal, Madhya Pradesh, India

*Corresponding Author:
Afzal Hussain
Department of Bioinformatics
MANIT, Madhya Pradesh, India

Accepted on May 17, 2017

Visit for more related articles at Biomedical Research

Abstract

COT (Tpl2/MAP3K8) is a serine/threonine kinase that plays a major role in the TNF-alpha production through MEK, ERK pathway and the production of other pro-inflammatory cytokines such as IL-1 beta. It's crucial role in inflammatory diseases and cancer, for finding out the best potent anticancer agent glide tool of the Schrodinger software was used for virtual screening using ligand databases like Drug Bank, MDPI and MayBridge Hitfinder against the target protein. The best compound was identified which can bind in the active site fully and showing good docking score as well as good pharmacokinetics characteristics. ADMET, MM-GBSA and DFT analysis was also done on the selected screened compounds. The compound 3-(5-{[4-(aminomethyl) piperidin-1-yl] methyl}-1H-indol-2-yl)-1Hindazole- 6-carbonitrile (DB07075) was found to be most potent among all the screened compounds. Hopefully compound (DB07075) could be used as anti-cancer agent against cancer, rheumatoid arthritis and various death leading diseases.

Keywords

COT kinase, Drug bank, Cancer, MDPI, Inhibitors, Potent.

Abbreviations

SBVS: Structure Based Virtual Screening; COT: Cancer Osaka Thyroid Kinase; DFT: Density Functional Theory; MW: Molecular Weight; HOMO: High Occupied Molecular Orbital; LUMO: Lowest Un-Occupied Molecular Orbital; MDPI: Molecular Diversity Preservation International; MH: MayBridge HitFinder; BBB: Blood Brain Barrier; HIA: Human Intestinal Absorption.

Introduction

Protein kinases play a very vital role in the cell biology where it can modify the functioning of a protein in the cell. Protein phosphorylation is a process which can increase or decrease the enzyme activity to alter the cellular processes like transcription and translation and play a major role in signal transduction pathways which can activate other protein to be functioned. Dysregulation of the protein kinases is related with many diseases such as cancer and inflammatory disorders [1,2].

One of the protein kinase is Cancer Osaka Thyroid Kinase which is a Mitogen Activated Protein Kinase 8 (MAP3K8) (Figure 1). It involve to regulate various signaling pathways like cell survival, cell proliferation and cell death as well as various inflammatory pathways including ERK, JNK, p38, NF- κB and plays an important role in the immune system also. COT kinase activation has been done with various proinflammatory stimuli like Lipopolysaccharide (LPS), TNF, CD40 ligand through Toll-Like Receptors (TLRs), TNF receptor 1, CD40 and Interleukin (IL-1) receptor [3]. COT kinase plays an important role for the activation of the downstream pathway like MEK/ERK with the stimulation of the pro-inflammatory receptors IL-1/TNF/TLR, belongs to the receptor super families, while MAP3K Tak1 facilitate to activate the p38, JNK and NF-kB pathways (Figure 2). Talking about the structure configuration, COT kinase forms a heterotrimeric complex together with NF-kB1 precursor protein p105 and the ubiquitin-binding protein ABIN-2. Its activation depends on the IKKB which triggers proteasomal degradation of p105 and releases the ABIN-2 from the association with p105 and COT [4-6].

biomedres-helix-sheets-turns

Figure 1: (a) COT Kinase 3D diagram with the active site in red box represented by using PyMOL visualization software. (b) Ligand protein interaction diagram. (c) COT Kinase secondary structure diagram shows helix, sheets and turns. (d) Ligplot interaction diagram. (e) Ramachandran plot of COT kinase complex with their statistical data. (f) Domain topology diagram of the COT kinase.

biomedres-signaling-pathway

Figure 2: Mechanism of the activation of the Cot kinase and further activation of the MEK and ERK signaling pathway and inactivation process followed by screened inhibitors shows in red box.

COT kinase involvement in various diseases like papillary thyroid cancer [7], breast cancer [8-11], ovarian cancer [12], multiple sclerosis [13], obesity-induced adipose tissue inflammation [14], liver injury [15], metastasis of clear cell renal cell carcinoma [16], androgen depletion-independent prostate cancer growth [17], obesity [18], pancreatic and lung inflammations [19].

In the literature study we have found the inhibitors like indazoles [20], thieno (2, 3-c) pyridine [21,22], 4-alkylamino- (1, 7) naphthyridine-3-carbonitriles [23], 8-substituted-4- anilino-6-aminoquinoline-3-carbonitriles [24], 1, 7- naphthyridine-3-carbonitriles [25], substituted indolyl indazoles [26].

Nowadays the challenge is to find out the small molecule cancer drugs [27,28]. Structure based drug design is the accurate approach for the development of cell cycle inhibitor for cancer therapy. SBVS technique is very important to find out the drug compound very rapidly from the large library of the compounds in the medicinal chemistry research [29]. COT is the most important therapeutic target for cancer research [30]. A lot of inhibitors have been approved which shows inhibition of cancer progression. Therefore, it is a most important work to find out a selective and novel inhibitor, which can recognize the target active site easily for reducing its infectious properties. In this current study the central aim is to find out a selective and most potent inhibitor for reducing the death leading diseases like cancer, cardiac hypertrophy and Alzheimer’s.

Materials and Methods

Retrieval and preparation of the protein structure of COT kinase

The 3D structure of the COT kinase as receptor (PDB ID: 4Y83) was retrieved from the protein data bank (http:// www.rcsb.org/pdb/home/home.do). Protein preparation wizard of the Schrodinger software has been used for preparing the protein structure where bond orders were assigned and hydrogen atoms were added as well as restrained minimization step has also been done using OPLS force field with RMSD cut off of 0.30 A0.

Ligand preparation

Total 62,801 small molecules of the ligand were retrieved from Drug Bank database (http://www.drugbank.ca/), MayBridge HitFinder database (http://www.maybridge.com/portal/ alias__Rainbow/lang__en/tabID__229/DesktopDefault.aspx, and MDPI database (http://www.mdpi.org/cumbase.htm). LigPrep module of the Schrodinger software was used for preparing all the retrieved Ligands where bond order and the bond angle were assigned as well as minimization was done by using OPLS-2005 force field. The Epik option was used for keeping ligand in the correct protonation state [31].

Grid preparation

The generation of the Grid of the receptor protein was done using the Glide protocol of the Schrodinger software where the reference ligand binding site was selected as centroid for Grid where on the active site the screened ligand bind. Scaling factor was selected as 1.0 and the partial charge cut-off was selected as the 0.25 [32].

Preparation of the reference compounds

The reference ligand compounds were retrieved from the cocrystallized structure of the protein-ligand structure complex (Figure 3), which is available in the protein data bank (PDB Id: 4Y83) (http://www.rcsb.org/pdb/explore/explore.do? structureId=4Y83). The reference compounds were prepared using a LigPrep module of the Schrodinger software and docked with the prepared receptor structure. This docking score of the reference compounds kept for comparing with the best potent compounds of the Drug Bank, MDPI and MayBridge HitFinder database.

biomedres-Data-Bank

Figure 3: Co crystallized inhibitor found in the Protein Data Bank with their Ligplot interaction diagram.

Virtual screening

High throughput virtual screening is a bioinformatics technique where we can screen a huge number of ligands against the target protein rapidly. A total number of 62,801 ligand compounds were retrieved from the Drug Bank, MDPI and MayBridge HitFinder databases. The reference compounds were also incorporated in the ligand database. These compounds and the reference compounds were subjected to the Lipinski filtration and reactive functionality. The Glide maestro protocol of the Schrödinger software was used for the virtual screening. Docking was performed in the three different phases like HTVS (High Throughput Virtual Screening), SP (Standard-Precision), XP (Extra-Precision). The library contains a large number of compounds so that after performing HTVS screening remaining 10% was performed using the Standard-precision docking, and remaining 10% was performed using the Extra-precision docking for getting the best potent compounds. The virtual screening workflow is shown in (Figure 4).

biomedres-Virtual-screening

Figure 4: Virtual screening workflow.

Density functional theory analysis

It is also very essential to find out the molecular orbital energy of the selected ligand. For this study HOMO, LUMO molecular orbital energy analysis was done. The gap energy is defined as difference between HOMO and LUMO molecular orbital energy, that energy indicates the excitation energy as well as showing the stability and reactivity of the compounds [33].

Drug like properties analysis

The best potent compounds were selected for finding the drug like properties. These properties follow the Lipinski’s rule of five. The properties which have been considered such as Molecular Weight (MW), Hydrogen Bond Acceptor (HBA), Lipophilicity (log P), Hydrogen Bond Donor (HBD), human oral absorption [34].

ADMET profiles analysis

ADMET properties of the compounds are related to absorption, distribution, metabolism, excretion and toxicity through the human body. The ADMET profile is very essential for evaluating the pharmacodynamic activities of the ligand compounds. In this study we have used the bioinformatic tool admetSAR (http://lmmd.ecust.edu.cn:8000/) [35].

MM-GBSA approach for drug-target binding energy estimation

Molecular mechanics generalized born surface area is used to finding the drug-target binding energy and it was calculated by using Schrodinger software. This binding energy estimates the stability of the protein with the ligand complexes [36].

Result and Discussion

Database screening and docking against the target

Total eleven potent novel lead compounds were found from different databases like Drug Bank, MDPI and MayBridge HitFinder using virtual screening and docking approach against the target protein, which showed best docking score.

Compound DB07075 was found to be more potent and selective, which interacts with the target protein active site residues. It makes H-bonding with residues GLY 210, GLU 208, ASP 270, and GLU 220, Pi-Pi stacking with TRP 132, ARG 146 and hydrophobic interaction with LEU 134, LEU 216, PRO 145, ILE 144, VAL 269, VAL 152, ALA 209, ALA 165, ALA 191 with docking score -14.860. The docking score is high and it covered the reference ligand binding site completely. It means this lead compound tightly docked in the active site of the target protein and showing good inhibition property. The docking score for DB07075, DB01940, DB07025, DB02224, DB01094, DB08009, MDPI 15603, MDPI 15661, MDPI 15605, MayBridge 13092 and MayBridge 10963 has been identified (Table 1). The docking and ligPlot interaction diagram gives a better view of interaction between target and lead compounds (Figures 5 and 6). The docking score comparisons graph also shown the better comparisons (Figure 7).

Compound Compound structure Mol. Wt. Mol. formula Docking score
Hit 1 DB07075 equation 384.483 C23H24N6 -14.86
Hit 2 DB01940 equation 474.512 C27H26N2O6 -13.304
Hit 3 DB07025 equation 386.496 C24H26N4O -12.673
Hit 4 DB02224 equation 304.256 C15H12O7 -12.49
Hit 5 DB01094 equation 302.283 C16H14O6 -11.92
Hit 6 DB08009 equation 414.934 C22H27ClN4O2 -11.685
Hit 7 MDPI 15603 equation 311.34 C18H17NO4 -12.408
Hit 8 MDPI 15661 equation 311.34 C18H17NO4 -12.335
Hit 9 MDPI 15605 equation 311.34 C18H17NO4 -11.525
Hit 10 MH 13092 equation 290.317 C15H18N2O4 -11.491
Hit 11 MH 10963 equation 302.281 C16H14O6  
5-(2-amino-5-(quinolin-3-yl)pyridin-3-yl)-1,3,4-oxadiazole-2(3H)-thioner  - - - -11.1403
r=reference compound.

Table 1. 2D structure of the selected inhibitors from DrugBank, MDPI and MayBridge HItFinder databases and reference compounds respectively with their docking scores.

biomedres-molecular-formula

Figure 5: Screened chemical structure with their molecular formula and molecular weight.

biomedres-inhibitor-protein

Figure 6: (a) Binding mode of compound DB07075 into the active site of COT kinase with their LigPlot interaction diagram. b) Binding mode of compound DB01940. c) Binding mode of the Ligand DB07025. d) Binding mode of the Ligand DB02224. e) Binding mode of the Ligand DB01094. f) Binding mode of the Ligand DB08009. g) Binding mode of the Ligand MDPI15603. h) Binding mode of the Ligand MDPI15661. i) Binding mode of the MDPI15605. j) Binding mode of the MH13092. k) Binding mode of the MH10963. The inhibitors have been shown in stick form and yellow dotted lines indicate the inhibitor protein H-bonding. The critical protein residues have been shown in white color.

biomedres-inhibitor-protein

Figure 7: Docking score of the screened ligands compared with the reference ligand.

The series, according to decreasing docking scores is as follows:

DB07075>DB01940>DB07025>DB02224>MDPI15603>MD PI15661>DB01094>DB08009>MDPI15605>MayBridge1309 2>MayBridge10963>Reference_ligand

HOMO LUMO stability analysis of the screened compounds

HOMO LUMO stability analysis was done on selected best potent ligand compounds which show drug and protein interaction stability and contribution of selected inhibitors (Table 2). All the selected ligand showed minimal HOMOLUMO gap with the average energy difference of 0.14 eV, signifying molecular reactivity. The orbital energy and the difference between HOMO and LUMO energy (gap energy) were estimated. HOMO LUMO energy of the inhibitor DB07075 (-0.35210, -0.26869) and remaining inhibitor DB01940 (-0.27794, -0.15153), DB07025 (-0.34105, -0.27646), DB02224 (-0.21787, -0.05554), DB01094 (-0.20716, -0.05019), DB08009 (-0.26440, -0.13994), MDPI 15603 (-0.20165, -0.05099), MDPI 15661 (-0.19274,-0.04465), MDPI 15605 (-0.19934, -0.05680), MH 13092 (-0.19795, -0.05815), MH 10963 (-0.21486, -0.04716). The lowest energy gap was found for DB07075 (0.08341) and DB07025 (0.06459). HOMO energy is higher compared to LUMO energy for all ligand which represent an ability to donate the electrons rather than accept electrons with their partner receptor binding site region.

S. no. Compound ID HOMO energy LUMO energy HLG
1 DB07075 -0.3521 -0.26869 0.08341
2 DB01940 -0.27794 -0.15153 0.12641
3 DB07025 -0.34105 -0.27646 0.06459
4 DB02224 -0.21787 -0.05554 0.16233
5 DB01094 -0.20716 -0.05019 0.15697
6 DB08009 -0.2644 -0.13994 0.12446
7 MDPI 15603 -0.20165 -0.05099 0.15066
8 MDPI 15661 -0.19274 -0.04465 0.14809
9 MDPI 15605 -0.19934 -0.0568 0.25614
10 MH 13092 -0.19795 -0.05815 0.1398
11 MH 10963 -0.21486 -0.04716 0.1677

Table 2. Orbital energy of the selected inhibitors of the DrugBank, MDPI and MayBridge HItFinder databases.

Drug-likeliness property analysis against screened compounds

These selected ligand compounds were evaluated for the drug likeliness properties. These selected hits followed Lipinski’s rule of five. Evidence for drug like characteristics. A good drug compound is recognized which shows effective metabolism, action and absorbed in record time and well distributed throughout the system. The QikProp tool of the Schrodinger software was used to evaluate the drug-likeliness property analysis. Our selected various properties such as Molecular Weight (MW), Total solvent accessible surface; Hydrogen Bond Acceptor (HBA), Hydrogen Bond Donor (HBD), Predicted aqueous solubility (QP log S) and Human oral absorption were assessed. The lower molecular weight is an indication of better absorption of the drug compounds. A Log S value represents solubility, the lesser the Log S value means higher the solubility, which would enhance the absorption property. Lower the total solvent accessible surface was favorable for drug like properties. The chemical name of these eleven lead molecules with their corresponding Drug Bank Id and MDPI database Id and MayBridge HitFinder database Id has been given to their chemical properties (Table 3).

S. no. Compound Ida Mol. Wt.b SASAc Human oral absorptiond HB donore HB acceptorf QP log Sg
1 DB07075 384.483 716.679 54.797 4 6 -4.593
2 DB01940 474.512 798.629 67.044 3 9 -5.017
3 DB07025 386.496 719.51 71.653 3 5 -4.284
4 DB02224 304.256 514.08 52.196 4 7 -2.67
5 DB01094 302.283 541.774 75.467 2 5 -3.782
6 DB08009 414.934 769.425 94.926 2 6 -5.745
7 MDPI15603 311.34 566.422 94.53 2 5 -3.938
8 MDPI15661 311.34 575.036 92.168 2 5 -4.084
9 MDPI15605 311.34 563.412 100 2 5 -3.887
10 MH13092 290.317 556.494 96.106 1 4 -3.922
11 MH10963 302.281 536.82 75.956 2 5 -3.699

Table 3. Drug-like properties of the selected inhibitors from DrugBank, MDPI and MayBridge HItFinder databases.

ADMET prediction

The admetSAR server was used to find out the ADMET properties of the selected drug compounds. HIA probability, Caco-2 probability, AMES test and carcinogenicity and Rat acute toxicity prediction. HIA represent the human intestinal absorption score. The compound was having high score would be absorbed better in the intestinal tract upon oral administration. BBB represents the blood brain barrier which should be high, the higher the BBB better the penetration. AMES test was performed to find out that the compounds were mutagenic or not. We found that all the compounds showed non-toxic properties except one. Carcinogenicity also reveals that all the compounds are non-carcinogens. A compound with a lower LD50 is more lethal than the compound having higher LD50. Predicting the efflux by P-glycoprotein (P-gp) metabolism of the drug compounds is carried out by a family of microsomal enzymes known as cytochrome P450 (CYP450). The two most important members are CYP3A4 and CYP2D6 which are represented in Tables 4 and 5.

S. no. Ligand BBB probability HIA probability Caco-2 probability AMES test Carcinogenicity Rat acute toxicity (LD50: mol/kg)
1 DB07075 0.8541 0.9671 0.6352 Non-toxic Non-carcinogen 2.5786
2 DB01940 0.7306 0.9139 0.6892 Non-toxic Non-carcinogen 2.4425
3 DB07025 0.894 0.9647 0.6532 Non-toxic Non-carcinogen 2.5263
4 DB02224 0.5711 0.965 0.8957 Non-toxic Non-carcinogen 3.02
5 DB01094 0.6591 0.9511 0.8286 Non-toxic Non-carcinogen 3.1455
6 DB08009 0.6782 0.8536 0.59 Non-toxic Non-carcinogen 2.684
7 MDPI15603 0.7797 0.8359 0.7444 Non-toxic Non-carcinogen 2.5097
8 MDPI15661 0.8307 0.8628 0.7785 Toxic Non-carcinogen 2.6928
9 MDPI15605 0.7453 0.9394 0.7199 Non-toxic Non-carcinogen 2.2957
10 MH13092 0.8661 0.9743 0.5332 Non-toxic Non-carcinogen 2.4772
11 MH10963 0.6591 0.9511 0.8286 Non-toxic Non-carcinogen 3.1455

Table 4. In-silico absorption and toxicity profile obtained from admetSAR server.

S. no. Ligand P-gp substrate/inhibitor probability CYP-2C9 substrate/inhibitor CYP-2D6 substrate/inhibitor CYP-3A4 substrate/inhibitor CYP-1A2 substrate/Inhibitor CYP-2C19 inhibitor CYP inhibitory promiscuity
1 DB07075 Substrate/Inhibitor Non-substrate/Non-Inhibitor Non-substrate/Non-Inhibitor Non-substrate/Non-Inhibitor Non-Inhibitor Non-Inhibitor High
2 DB01940 Substrate/Non-Inhibitor Non-substrate/Non-Inhibitor Non-substrate/Non-Inhibitor Non-substrate/Non-Inhibitor Non-Inhibitor Non-Inhibitor Low
3 DB07025 Substrate/Inhibitor Non-substrate/Non-Inhibitor Non-substrate/Inhibitor substrate/Inhibitor Inhibitor Non-Inhibitor High
4 DB02224 Substrate/Non-Inhibitor Non-substrate/Non-Inhibitor Non-substrate/Non-Inhibitor Non-substrate/Inhibitor Inhibitor Non-Inhibitor High
5 DB01094 Substrate/Non-Inhibitor Non-substrate/Inhibitor Non-substrate/Inhibitor Non-substrate/Inhibitor Inhibitor Inhibitor High
6 DB08009 Substrate/Non-Inhibitor Non-substrate/Non-Inhibitor Non-substrate/Non-Inhibitor substrate/Non-Inhibitor Non-Inhibitor Non-Inhibitor Low
7 MDPI15603 Substrate/Non-Inhibitor Non-substrate/Non-Inhibitor Non-substrate/Inhibitor substrate/Inhibitor Inhibitor Inhibitor High
8 MDPI15661 Substrate/Non-Inhibitor Non-substrate/Non-Inhibitor Non-substrate/Inhibitor substrate/Inhibitor Inhibitor Inhibitor High
9 MDPI15605 Substrate/Non-Inhibitor Non-substrate/Non-Inhibitor Non-substrate/Inhibitor Non-substrate/Inhibitor Inhibitor Inhibitor High
10 MH13092 Substrate/Non-Inhibitor Non-substrate/Non-Inhibitor Non-substrate/Non-Inhibitor substrate/Non-Inhibitor Non-Inhibitor Non-Inhibitor Low
11 MH10963 Substrate/Non-Inhibitor Non-substrate/Inhibitor Non-substrate/Inhibitor Non-substrate/Inhibitor Inhibitor Inhibitor High

Table 5. In-silico distribution profile obtained from admetSAR server.

Drug-target binding energy analysis against screened compounds

Prime MM-GBSA (GB stands for Generalized Born) module of the Schrodinger software is very useful for finding out the protein-ligand binding affinity. The selected protein-ligand complexes were subjected to that module. It combines OPLS Molecular Mechanics Energies (EMM), surface generalized born solvation model for polar salvation (GSGB), and a nonpolar solvation term (GNP). The total free energy of binding calculation as:

ΔG bind=G complex-(G protein+G ligand) → (1)

ΔG bind: total binding free energy of complex

G complex: total energy of the complex

G protein: energy of the receptor without ligand

G ligand: energy of the unbound ligand

Where G=EMM+GSGB+GNP → (2)

The binding free energy estimated for Drug Bank, MDPI, MayBridge HitFinder selected compounds as complexes (Table 6).

S. no. Compound Ida ΔG bindb Gevdwc Gcould Gcovalente GsolGBf GsolLipog
1 DB07075 -97.78 -52.35 -78.22 8.56 83.69 -55.98
2 DB01940 -96.74 -55.13 -55.51 10.4 60.24 -53.89
3 DB07025 -91.61 -42.68 -77.7 8.61 82.52 -59.33
4 DB02224 -74.52 -36.8 -18 1.23 18.87 -37.46
5 DB01094 -93.6 -40.84 -31.58 5.15 18.15 -41.49
6 DB08009 -102.46 -49.06 -39.54 7.65 48.33 -67.83
7 MDPI15603 -96.2 -45.22 -17.23 3.56 14.07 -48.48
8 MDPI15661 -104.57 -46.48 -30 4.37 19.7 -48.98
9 MDPI15605 -78.77 -42.3 -11.26 7.27 17.66 -47.99
10 MH13092 -79.61 -45.64 -10.61 11.38 11.24 -44.34
11 MH10963 -80.23 -34.82 -25.33 8.19 16.08 -41.19

Table 6. Prime MM-GBSA energy calculation result of the selected inhibitors from DrugBank, MDPI and MayBridge HItFinder databases.

Superimpose structure

The docked pose of Drug Bank hit, MDPI hits and MayBridge HitFinder hits occupy the active binding site. It was superimposed on crystal structure (PDB ID: 4Y83) that occupy this region, The binding pattern of Drug Bank hits, MDPI hits and MayBridge HitFinder hits were found to be similar to that of the reference binding site. The figure clearly indicates that the proposed binding pose is well occupied in the active site of COT kinase complex and has a binding pattern similar to that of reference ligand (Figure 8).

biomedres-screened-DrugBank

Figure 8: Selected inhibitors after screened DrugBank, MDPI, Maybridge HitFinder database has been superimposed in the active site of the target protein.

Conclusion

Identification of the novel and potential lead compound is step by step procedure. A range of computational tools and techniques were used to discover best inhibitors. A very large number of drugs library were subjected for the screening against the target compound (COT kinase Complex). This structure based virtual screening helped us to reduce the number of compounds which shows the greatest binding affinity to the target and showing the best inhibitory properties. Drug likeliness properties calculated by the Lipinski filter, after using virtual screening and docking using Schrodinger software, the best 11 compounds found promising. Among all compound 3-(5-{[4-(aminomethyl) piperidin-1-yl] methyl}-1hindol- 2-yl)-1h-indazole-6-carbonitrile was found to be more potent and selective. These all compounds showing the binding where the reference co-crystallized ligand bind in the hinge region. It is obvious that these hits could be as potent and selective anticancer agent of Cancer Osaka Thyroid Kinase complex.

Acknowledgements

The Research work was carried out in the Department of Bioinformatics, Maulana Azad National Institute of Technology, India. Authors wish to acknowledge the Schrodinger team for providing software facility.

Funding

Author doesn't have any funding from the institute.

References