How to cite this paper
Yadav, M., Narasimhan, B & Kapoor, A. (2024). Development of 2-dimensional and 3-dimensional QSAR models of Indazole derivatives as TTK inhibitors having Anticancer potential.Current Chemistry Letters, 13(1), 225-240.
Refrences
1. Kula K., and Lapczuk-Krygier A. (2018) A DFT computational study on the [3+2] cycloaddition between parent thionitrone and nitroethene. Current Chem. Lett. 7 (1) 27-34.
2. Harvey J., Himo F., Maseras F., and Perrin L. (2019) Scope and Challenge of Computational Methods for Studying Mechanism and Reactivity in Homogeneous Catalysis ACS Catal. 9 (8) 6803–6813.
3. Deglmann P., Sch€afer A., and Lennartz C. (2015) Application of quantum calculations in the chemical industry—An overview. Inter. J. Quan. Chem. 115 107–136.
4. Neves B. J., Braga R. C., Melo-Filho C. C., Moreira-Filho J. T., Muratov E. N., and Andrade C. H. (2018) QSAR-based Virtual Screening: Advances and Applications in Drug Discovery. Front Pharmacol. 9 1275.
5. Heravi Y. E., Sereshti H., Saboury A. A., Ghasemi J., Amirmostofian M., and Supuran C. T. (2017) 3D QSAR studies, pharmacophore modelling and virtual screening of diarylpyrazole–benzenesulfonamide derivatives as a template to obtain new inhibitors, using human carbonic anhydrase II as a model protein. J. Enzyme Inhib. Med. Chem. 32 (1) 688–700.
6. Akanksha., Mehta V., Dhingra R., Monika., and Dhingra N. (2018) In silico Identification of potential 5α‒reductase inhibitors for prostatic diseases: QSAR modelling, molecular docking, and pre ADME predictions. MOJ. Drug Design Dev. Therapy. 2 (3) 136‒145.
7. Shang C., Hou Y., Meng T., Shi M., and Cui G. (2021) The Anticancer Activity of Indazole Compounds: A Mini Review. Curr. Top. Med. Chem. 21 (5) 363-376.
8. Chaban T., Rotar D., Panasenko N., Skrobala V., Pokhodylo N., and Matiychuk V. (2022) Synthesis, anticancer and antimicrobial properties of some N-aryl-2-(5-aryltetrazol-2-yl) acetamides. Current Chem. Lett. 11(3) 299-308.
9. Rajora, A. M., Ravishankar D., Zhang H., and Rosenholm J. M. (2020) Recent Advances and Impact of Chemotherapeutic and Antiangiogenic Nano formulations for Combination Cancer Therapy. Pharm. 12 (6) 592.
10. Akalu Y. T., Rothlin C. V., and Ghosh S. (2017) TAM receptor tyrosine kinases as emerging targets of innate immune checkpoint blockade for cancer therapy. Immunol Rev. 276 165–177.
11. Zheng L., Chen Z., Kawakami M., Chen Y., Roszik J., Mustachio L. M., Kurie J. M., Villalobos P., Lu W., Behrens C., Mino B., Solis L. M., Silvester J., Thu K. L., Cescon D. W., Rodriguez-Canales J., Wistuba I. I., Mak T. W., Liu X., and Dmitrovsky E. (2019) Tyrosine Threonine Kinase Inhibition Eliminates Lung Cancers by Augmenting Apoptosis and Polyploidy. Mol. Cancer Ther. 18 1775–1786.
12. Stratford J. K., Yan F., Hill R. A., Major M. B., Graves L. M., Der C. J., and Yeh J. J. (2017) Genetic and pharmacological inhibition of TTK impairs pancreatic cancer cell line growth by inducing lethal chromosomal instability. PLoS ONE12: e0174863.
13. Lu N., and Ren L. (2021) TTK (threonine tyrosine kinase) regulates the malignant behaviors of cancer cells and is regulated by microRNA-582-5p in ovarian cancer. Bioengineered. 12 (1) 5759–5768.
14. Thu K. L., Soria-Bretones I., Mak T. W., and Cescon D. W. (2018) Targeting the cell cycle in breast cancer: towards the next phase. Cell Cycle. 17 (15) 1871–1885.
15. Liu Y., Lang Y., Patel N. K., Ng G., Laufer R., Li Szi-W., Edwards L., Forrest B., Sampson P. B., Feher M., Ban F., Awrey D. E., Beletskaya I., Mao G., Hodgson R., Plotnikova O., Qiu W., Chirgadze N. Y., Mason J. M., Wei X., Lin D. C. C., Che Y., Kiarash R., Madeira B., Fletcher G. C., Mak T. W., Bray M. R., and Pauls H. W. (2015) The Discovery of Orally Bioavailable Tyrosine Threonine Kinase (TTK) Inhibitors: 3 (4-(heterocyclyl) phenyl)- 1H- indazole-5-carboxamides as Anticancer Agents. J. Med. Chem. 58 (8) 3366-3392.
16. Laufer R., Ng G., Liu Y., Patel N. K. B., Edwards L. G., Lang Y., Li Sze-W., Feher M., Awrey D. E., Leung G., Beletskaya I., Plotnikova O., Mason J. M., Hodgson R., Wei X., Mao G., Luo X., Huang P., Green E., Kiarash R., Lin D. C. C., Harris-Brandts M., Ban F., Nadeem V., Mak T. W., Pan G. J., Qiu W., Chirgadze N. Y., and Pauls H. W. (2014) Discovery of Inhibitors of the Mitotic Kinase TTK Based on N- (3- (3-Sulfamoylphenyl)-1H-indazol-5-yl)- Acetamides and Carboxamides. Bioorg. Med. Chem. 22 (17) 4968-9714.
17. Martin T. M., Harten P., Young D. M., Muratov E. N., Golbraikh A., Zhu H., and Tropsha A. (2012) Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling?. J. Chem. Inf. Model. 52 (10) 2570-2578.
18. Verma J., Khedkar V. M., and Coutinho E. C. (2010) 3D-QSAR in drug design–a review. Curr. Top. Med. Chem. 10 (1) 95-115.
19. Zhao M., Wang L., Zheng L., Zhang M., Qiu C., Zhang Y., Du D., and Niu B. (2017) 2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors. Hindawi BioMed. Res. Inter. 2017 1-11.
20. Sharma P. K., and Vakil B. V. (2017) Predictive QSAR analysis of flavonoid analogues as antipsoriatic agents. IJPSR. 8 (12) 5146-5160.
21. Gopinath P., and Kathiravan M. K. (2022) molecular field-based qsar studies and docking analysis of mercaptoquinazolinone benzene Sulfonamide derivatives against HCA XII. RASAYAN J. Chem. 15 (1) 686-699.
22. Khedkar S. A., Patil J. S., and Sable P. M. (2017) 3D quantitative structure activity relationship of tetrahydroimidazo [1,2-a] pyrimidine as antimicrobial agents. Marmara Pharm. J. 21 (3) 644-653.
23. Veerasamy R., and Rajak H. (2021) QSAR Studies on Neuraminidase Inhibitors as Anti-influenza Agents. Turk. J. Pharm. Sci. 18 (2) 151-156.
24. Chitre T. S., Kathiravan M. K., Bothara K. G., Bhandari S. V., and Jalnapurkar R. R. (2011) Pharmacophore optimization and design of competitive inhibitors of thymidine monophosphate kinase through molecular modeling studies. Chem. Biol. Drug. Des. 362 (78) 826-34.
25. Bhadoriya K. S., Kumawat N. K., Bhavthankar S. V., Avchara M. H., Dhumal D. M., Patil S. D., and Jain S. V. (2016) Exploring 2D and 3D QSARs of benzimidazole derivatives as transient receptor potential melastatin 8 (TRPM8) antagonists using 347 MLR and kNN-MFA methodology. J. Saud. Chem. Soc. 20 (S) 256–S270.
26. Fadili M. El., Er-Rajy M., Kara M., Assouguem A., Belhassan A., Alotaibi A., Mrabti N. N., Fidan H., Ullah R., Ercisli S., Zarougui S., and Elhallaoui M. (2022) QSAR, ADMET In Silico Pharmacokinetics, Molecular Docking and Molecular Dynamics Studies of Novel Bicyclo (Aryl Methyl) Benzamides as Potent GlyT1 Inhibitors for the treatment of Schizophrenia. Pharmaceuticals (Basel). 15 670.
27. Asati V., Bharti S. K., Rathore A., and Mahapatra D. K. (2017) SWFB and GA Strategies for Variable Selection in QSAR Studies for the Validation of Thiazolidine- 2,4-Dione Derivatives as Promising Antitumor Candidates. Indian J. Pharm. Edu. Res. 51 (3) 436-451.
28. Hunashal R. D., and Palkar M. B. (2017) Rational Design of Antifungal 1,2,4-triazole derivatives by 2D-QSAR Study. Int. J. New. Tech. Res. 3 (4) 88-91.
29. Khan M. S., Ul-Haque Z., Taleuzzaman M., Surana S. S., and Maru A. D. (2022) Development of 2D and 3D Quantitative Structure Activity Relationship Models of Thiazole Derivatives for Antimicrobial Activity. Int. J. Pharm. Sci. Drug. Res. 14 (2) 164-170.
30. Olasupo S. B., Uzairu A., Shallangwa G., and Uba S. (2020) QSAR modelling, molecular docking and ADMET/pharmacokinetic studies: a chemometrics approach to search for novel inhibitors of norepinephrine transporter as potent antipsychotic drugs. J. Iranian Chem. Soc. 17 1953-1966.
31. Panigrahi D., Mishra A., and Sahu S. K. (2015) Rational in silico drug design of HIV-RT inhibitors through G-QSAR and molecular docking study of 4-arylthio and 4-aryloxy-3-iodopyridine-2(1-H)-one derivative. Beni-Suef. Uni. J. Bas. App. Sci. 9 (48) 1-18.
32. Panigrahi D., Mishra A., and Sahu S. K. (2020) Pharmacophore modelling, QSAR study, molecular docking and in-silico ADME prediction of 1,2,3‑triazole and pyrazolopyridones as DprE1 inhibitor antitubercular agents. SN. App. Sci. 2 922.
33. Palkar M. B., Noolvi M. N., Patel H. M., Maddi V. S., and Nargund L. V. G. (2011) 2D-QSAR study of fluoroquinolone derivatives: an approach to design anti-tubercular agents. Inter. J. Drug Desgn. Dis. 3 559-574.
34. Antre R. V., Oswal R. J., Kshirsagar S. S., Kore P. P., and Mutha M. M. (2012) 2D-QSAR studies of substituted pyrazolone derivatives as anti-inflammatory agents. Med. Chem. 2 (6) 126-130.
35. Abdi H. (2010) Partial least squares regression and projection on latent structure regression. Wiley Interdisciplinary Reviews: Computational Statistics. 2 (1) 97-106.
36. Bhatia M. S., Pakhare K. D., Choudhari P. B., Jadhav S. D., Dhavale R. P., and Bhatia N.M. (2017) Pharmacophore modeling and 3D QSAR studies of aryl amine derivatives as potential lumazine synthase inhibitors. Arabian J. Chem. 10 (1) S100-S104.
37. Silva-Junior E. F. D., Aquino T. M. D., and Araujo-Junior J. X. D. (2017) 3D-QSAR and Pharmacophore Identification Studies Applied to Pyridazin-3-one Derivatives as Potent PDE4 Inhibitors. Acta Sci Pharm Sci. 1 (5) 22-27.
38. Gasteiger J., and Marsili M. (1980) Iterative partial equalization of orbital electronegativity-a rapid access to atomic charges. Tetrahedron. 36 3219-28.
39. Suhane S., Nerkar G., Modi K., and Sawant S. D. (2019) 2d and 3d-qsar analysis of amino (3-((3, 5-difluoro-4-methyl-6-phenoxypyridine-2-yl) oxy) phenyl) methaniminium derivatives as factor Xa inhibitor. Int. J. Pharm. Pharm. Sci. 11 (2) 104-114.
40. Al-Attraqchi O. H. A., and Mordi M. N. (2022) 2D- and 3D-QSAR, molecular docking, and virtual screening of pyrido [2, 3-d] pyrimidin-7-one-based CDK4 inhibitors. J. Appl. Pharmaceutical Sci.12 (01) 165–175.
41. ElMchichi L., Belhassan A., Lakhlifi A., and Bouachrine M. (2020) 3D-QSAR study of the chalcone derivatives as anticancer agents. Hindawi J. Chem. 2020 1-12.
42. VLife MDS 4.6 (2018) Molecular design suite. Vlife Sciences Technologies Pvt. Ltd. Pune, India.
43. Khare S., Subramani P., Choudhari S., Phalle S., Kumbhar S., Kadam A., and Choudhari P. B. (2016) k nearest neighbor and 3D QSAR analysis of Thiazolidinone derivatives as antitubercular Agents. J. Pharm. Res. 15 (3) 67-72.
44. Wang J. L., Cheng L. P., Wang T. C., Deng W., and Wu F. H. (2017) Molecular modelling study of CP- 690550 derivatives as JAK3 kinase inhibitors through combined 3D QSAR, molecular docking, and dynamics simulation techniques. J. Mol. Graph Model. 72: 178–186.
45. Bose P., Mishra M., Gajbhiye A., and Kashaw S. K. (2019) QSAR Pharmacophore Mapping and Molecular Docking of 2,4-Diaminoquinazoline as Antitubercular Scaffold: A Computational Hybrid Approach. Indian J. Pharm. Sci. 81 (6) 1078-1088.
2. Harvey J., Himo F., Maseras F., and Perrin L. (2019) Scope and Challenge of Computational Methods for Studying Mechanism and Reactivity in Homogeneous Catalysis ACS Catal. 9 (8) 6803–6813.
3. Deglmann P., Sch€afer A., and Lennartz C. (2015) Application of quantum calculations in the chemical industry—An overview. Inter. J. Quan. Chem. 115 107–136.
4. Neves B. J., Braga R. C., Melo-Filho C. C., Moreira-Filho J. T., Muratov E. N., and Andrade C. H. (2018) QSAR-based Virtual Screening: Advances and Applications in Drug Discovery. Front Pharmacol. 9 1275.
5. Heravi Y. E., Sereshti H., Saboury A. A., Ghasemi J., Amirmostofian M., and Supuran C. T. (2017) 3D QSAR studies, pharmacophore modelling and virtual screening of diarylpyrazole–benzenesulfonamide derivatives as a template to obtain new inhibitors, using human carbonic anhydrase II as a model protein. J. Enzyme Inhib. Med. Chem. 32 (1) 688–700.
6. Akanksha., Mehta V., Dhingra R., Monika., and Dhingra N. (2018) In silico Identification of potential 5α‒reductase inhibitors for prostatic diseases: QSAR modelling, molecular docking, and pre ADME predictions. MOJ. Drug Design Dev. Therapy. 2 (3) 136‒145.
7. Shang C., Hou Y., Meng T., Shi M., and Cui G. (2021) The Anticancer Activity of Indazole Compounds: A Mini Review. Curr. Top. Med. Chem. 21 (5) 363-376.
8. Chaban T., Rotar D., Panasenko N., Skrobala V., Pokhodylo N., and Matiychuk V. (2022) Synthesis, anticancer and antimicrobial properties of some N-aryl-2-(5-aryltetrazol-2-yl) acetamides. Current Chem. Lett. 11(3) 299-308.
9. Rajora, A. M., Ravishankar D., Zhang H., and Rosenholm J. M. (2020) Recent Advances and Impact of Chemotherapeutic and Antiangiogenic Nano formulations for Combination Cancer Therapy. Pharm. 12 (6) 592.
10. Akalu Y. T., Rothlin C. V., and Ghosh S. (2017) TAM receptor tyrosine kinases as emerging targets of innate immune checkpoint blockade for cancer therapy. Immunol Rev. 276 165–177.
11. Zheng L., Chen Z., Kawakami M., Chen Y., Roszik J., Mustachio L. M., Kurie J. M., Villalobos P., Lu W., Behrens C., Mino B., Solis L. M., Silvester J., Thu K. L., Cescon D. W., Rodriguez-Canales J., Wistuba I. I., Mak T. W., Liu X., and Dmitrovsky E. (2019) Tyrosine Threonine Kinase Inhibition Eliminates Lung Cancers by Augmenting Apoptosis and Polyploidy. Mol. Cancer Ther. 18 1775–1786.
12. Stratford J. K., Yan F., Hill R. A., Major M. B., Graves L. M., Der C. J., and Yeh J. J. (2017) Genetic and pharmacological inhibition of TTK impairs pancreatic cancer cell line growth by inducing lethal chromosomal instability. PLoS ONE12: e0174863.
13. Lu N., and Ren L. (2021) TTK (threonine tyrosine kinase) regulates the malignant behaviors of cancer cells and is regulated by microRNA-582-5p in ovarian cancer. Bioengineered. 12 (1) 5759–5768.
14. Thu K. L., Soria-Bretones I., Mak T. W., and Cescon D. W. (2018) Targeting the cell cycle in breast cancer: towards the next phase. Cell Cycle. 17 (15) 1871–1885.
15. Liu Y., Lang Y., Patel N. K., Ng G., Laufer R., Li Szi-W., Edwards L., Forrest B., Sampson P. B., Feher M., Ban F., Awrey D. E., Beletskaya I., Mao G., Hodgson R., Plotnikova O., Qiu W., Chirgadze N. Y., Mason J. M., Wei X., Lin D. C. C., Che Y., Kiarash R., Madeira B., Fletcher G. C., Mak T. W., Bray M. R., and Pauls H. W. (2015) The Discovery of Orally Bioavailable Tyrosine Threonine Kinase (TTK) Inhibitors: 3 (4-(heterocyclyl) phenyl)- 1H- indazole-5-carboxamides as Anticancer Agents. J. Med. Chem. 58 (8) 3366-3392.
16. Laufer R., Ng G., Liu Y., Patel N. K. B., Edwards L. G., Lang Y., Li Sze-W., Feher M., Awrey D. E., Leung G., Beletskaya I., Plotnikova O., Mason J. M., Hodgson R., Wei X., Mao G., Luo X., Huang P., Green E., Kiarash R., Lin D. C. C., Harris-Brandts M., Ban F., Nadeem V., Mak T. W., Pan G. J., Qiu W., Chirgadze N. Y., and Pauls H. W. (2014) Discovery of Inhibitors of the Mitotic Kinase TTK Based on N- (3- (3-Sulfamoylphenyl)-1H-indazol-5-yl)- Acetamides and Carboxamides. Bioorg. Med. Chem. 22 (17) 4968-9714.
17. Martin T. M., Harten P., Young D. M., Muratov E. N., Golbraikh A., Zhu H., and Tropsha A. (2012) Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling?. J. Chem. Inf. Model. 52 (10) 2570-2578.
18. Verma J., Khedkar V. M., and Coutinho E. C. (2010) 3D-QSAR in drug design–a review. Curr. Top. Med. Chem. 10 (1) 95-115.
19. Zhao M., Wang L., Zheng L., Zhang M., Qiu C., Zhang Y., Du D., and Niu B. (2017) 2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors. Hindawi BioMed. Res. Inter. 2017 1-11.
20. Sharma P. K., and Vakil B. V. (2017) Predictive QSAR analysis of flavonoid analogues as antipsoriatic agents. IJPSR. 8 (12) 5146-5160.
21. Gopinath P., and Kathiravan M. K. (2022) molecular field-based qsar studies and docking analysis of mercaptoquinazolinone benzene Sulfonamide derivatives against HCA XII. RASAYAN J. Chem. 15 (1) 686-699.
22. Khedkar S. A., Patil J. S., and Sable P. M. (2017) 3D quantitative structure activity relationship of tetrahydroimidazo [1,2-a] pyrimidine as antimicrobial agents. Marmara Pharm. J. 21 (3) 644-653.
23. Veerasamy R., and Rajak H. (2021) QSAR Studies on Neuraminidase Inhibitors as Anti-influenza Agents. Turk. J. Pharm. Sci. 18 (2) 151-156.
24. Chitre T. S., Kathiravan M. K., Bothara K. G., Bhandari S. V., and Jalnapurkar R. R. (2011) Pharmacophore optimization and design of competitive inhibitors of thymidine monophosphate kinase through molecular modeling studies. Chem. Biol. Drug. Des. 362 (78) 826-34.
25. Bhadoriya K. S., Kumawat N. K., Bhavthankar S. V., Avchara M. H., Dhumal D. M., Patil S. D., and Jain S. V. (2016) Exploring 2D and 3D QSARs of benzimidazole derivatives as transient receptor potential melastatin 8 (TRPM8) antagonists using 347 MLR and kNN-MFA methodology. J. Saud. Chem. Soc. 20 (S) 256–S270.
26. Fadili M. El., Er-Rajy M., Kara M., Assouguem A., Belhassan A., Alotaibi A., Mrabti N. N., Fidan H., Ullah R., Ercisli S., Zarougui S., and Elhallaoui M. (2022) QSAR, ADMET In Silico Pharmacokinetics, Molecular Docking and Molecular Dynamics Studies of Novel Bicyclo (Aryl Methyl) Benzamides as Potent GlyT1 Inhibitors for the treatment of Schizophrenia. Pharmaceuticals (Basel). 15 670.
27. Asati V., Bharti S. K., Rathore A., and Mahapatra D. K. (2017) SWFB and GA Strategies for Variable Selection in QSAR Studies for the Validation of Thiazolidine- 2,4-Dione Derivatives as Promising Antitumor Candidates. Indian J. Pharm. Edu. Res. 51 (3) 436-451.
28. Hunashal R. D., and Palkar M. B. (2017) Rational Design of Antifungal 1,2,4-triazole derivatives by 2D-QSAR Study. Int. J. New. Tech. Res. 3 (4) 88-91.
29. Khan M. S., Ul-Haque Z., Taleuzzaman M., Surana S. S., and Maru A. D. (2022) Development of 2D and 3D Quantitative Structure Activity Relationship Models of Thiazole Derivatives for Antimicrobial Activity. Int. J. Pharm. Sci. Drug. Res. 14 (2) 164-170.
30. Olasupo S. B., Uzairu A., Shallangwa G., and Uba S. (2020) QSAR modelling, molecular docking and ADMET/pharmacokinetic studies: a chemometrics approach to search for novel inhibitors of norepinephrine transporter as potent antipsychotic drugs. J. Iranian Chem. Soc. 17 1953-1966.
31. Panigrahi D., Mishra A., and Sahu S. K. (2015) Rational in silico drug design of HIV-RT inhibitors through G-QSAR and molecular docking study of 4-arylthio and 4-aryloxy-3-iodopyridine-2(1-H)-one derivative. Beni-Suef. Uni. J. Bas. App. Sci. 9 (48) 1-18.
32. Panigrahi D., Mishra A., and Sahu S. K. (2020) Pharmacophore modelling, QSAR study, molecular docking and in-silico ADME prediction of 1,2,3‑triazole and pyrazolopyridones as DprE1 inhibitor antitubercular agents. SN. App. Sci. 2 922.
33. Palkar M. B., Noolvi M. N., Patel H. M., Maddi V. S., and Nargund L. V. G. (2011) 2D-QSAR study of fluoroquinolone derivatives: an approach to design anti-tubercular agents. Inter. J. Drug Desgn. Dis. 3 559-574.
34. Antre R. V., Oswal R. J., Kshirsagar S. S., Kore P. P., and Mutha M. M. (2012) 2D-QSAR studies of substituted pyrazolone derivatives as anti-inflammatory agents. Med. Chem. 2 (6) 126-130.
35. Abdi H. (2010) Partial least squares regression and projection on latent structure regression. Wiley Interdisciplinary Reviews: Computational Statistics. 2 (1) 97-106.
36. Bhatia M. S., Pakhare K. D., Choudhari P. B., Jadhav S. D., Dhavale R. P., and Bhatia N.M. (2017) Pharmacophore modeling and 3D QSAR studies of aryl amine derivatives as potential lumazine synthase inhibitors. Arabian J. Chem. 10 (1) S100-S104.
37. Silva-Junior E. F. D., Aquino T. M. D., and Araujo-Junior J. X. D. (2017) 3D-QSAR and Pharmacophore Identification Studies Applied to Pyridazin-3-one Derivatives as Potent PDE4 Inhibitors. Acta Sci Pharm Sci. 1 (5) 22-27.
38. Gasteiger J., and Marsili M. (1980) Iterative partial equalization of orbital electronegativity-a rapid access to atomic charges. Tetrahedron. 36 3219-28.
39. Suhane S., Nerkar G., Modi K., and Sawant S. D. (2019) 2d and 3d-qsar analysis of amino (3-((3, 5-difluoro-4-methyl-6-phenoxypyridine-2-yl) oxy) phenyl) methaniminium derivatives as factor Xa inhibitor. Int. J. Pharm. Pharm. Sci. 11 (2) 104-114.
40. Al-Attraqchi O. H. A., and Mordi M. N. (2022) 2D- and 3D-QSAR, molecular docking, and virtual screening of pyrido [2, 3-d] pyrimidin-7-one-based CDK4 inhibitors. J. Appl. Pharmaceutical Sci.12 (01) 165–175.
41. ElMchichi L., Belhassan A., Lakhlifi A., and Bouachrine M. (2020) 3D-QSAR study of the chalcone derivatives as anticancer agents. Hindawi J. Chem. 2020 1-12.
42. VLife MDS 4.6 (2018) Molecular design suite. Vlife Sciences Technologies Pvt. Ltd. Pune, India.
43. Khare S., Subramani P., Choudhari S., Phalle S., Kumbhar S., Kadam A., and Choudhari P. B. (2016) k nearest neighbor and 3D QSAR analysis of Thiazolidinone derivatives as antitubercular Agents. J. Pharm. Res. 15 (3) 67-72.
44. Wang J. L., Cheng L. P., Wang T. C., Deng W., and Wu F. H. (2017) Molecular modelling study of CP- 690550 derivatives as JAK3 kinase inhibitors through combined 3D QSAR, molecular docking, and dynamics simulation techniques. J. Mol. Graph Model. 72: 178–186.
45. Bose P., Mishra M., Gajbhiye A., and Kashaw S. K. (2019) QSAR Pharmacophore Mapping and Molecular Docking of 2,4-Diaminoquinazoline as Antitubercular Scaffold: A Computational Hybrid Approach. Indian J. Pharm. Sci. 81 (6) 1078-1088.