Skip to content
2000
image of Investigation of Novel Quinoline Derivatives Targeting Epidermal Growth Factor Receptors as Anticancer Agents a Computational Approach

Abstract

Background

Newer chemical entities are created and synthesis has been made feasible by a variety of computer-aided drug design (CAAD) techniques. In addition to facilitating the visualisation of the ligand-target binding process, the application of in silico methodologies and structure-based drug design (SBDD) allows for the prediction of receptor affinities and significant binding pocket locations.

Objective

The goal of the current study was to identify new quinoline derivatives by computational methods specially designed to bind the EGFR receptor in the treatment of breast cancer.

Materials and Methods

ChemAxon Marvin Sketch 5.11.5 was used to create derivatives of quinolines. The admetSAR online web tools and SwissADME were utilised to forecast the toxicity and pharmacokinetic characteristics of several substances. A multitude of software programmes, such as Autodock 1.1.2, MGL Tools 1.5.6, Procheck, Protparam ExPasy tool, PyMOL, and Biovia Discovery Studio Visualizer v20.1.0.19295 were also employed to ascertain the ligand-receptor interactions between quinoline derivatives and the target receptor (PDB -5GNK).

Result

Almost all components were shown to be less hazardous, orally consumable and to have the appropriate pharmacokinetic characteristics based on in silico study. All newly generated derivative compounds have higher docking scores when compared to the widely used medication sorafenib.

Conclusion

Interactions with quinoline analogues boost binding energy and the number of H-bonds produced, making them a suitable place to start when creating compounds for further exploration. The quinoline moiety increases its potential as a novel therapy alternative for breast cancer and could facilitate more comprehensive in vivo, in vitro, chemical-based, and pharma studies by medicinal chemists.

Loading

Article metrics loading...

/content/journals/ccand/10.2174/012212697X335334241101023647
2024-11-07
2025-01-19
Loading full text...

Full text loading...

References

  1. Zhou F. Zhong Z. Drug design and discovery: Principles and applications. Molecules 2017 22 2 279 10.3390/molecules22020279
    [Google Scholar]
  2. Barret R. Medicinal Chemistry: Fundamentals ISTE Press - Elsevier 2018
    [Google Scholar]
  3. Lombardino J.G. Lowe J.A. The role of the medicinal chemist in drug discovery — Then and now. Nat. Rev. Drug Discov. 2004 3 10 853 862 10.1038/nrd1523 15459676
    [Google Scholar]
  4. Gioiello A. Piccinno A. Lozza A.M. Cerra B. The medicinal chemistry in the era of machines and automation: Recent advances in continuous flow technology. J. Med. Chem. 2020 63 13 6624 6647 10.1021/acs.jmedchem.9b01956 32049517
    [Google Scholar]
  5. Ortega M.P. Gil I.C. New developments in medicinal chemistry. 1st ed USA Nova Science Publishers 2009
    [Google Scholar]
  6. Roughley S.D. Jordan A.M. The medicinal chemist’s toolbox: An analysis of reactions used in the pursuit of drug candidates. J. Med. Chem. 2011 54 10 3451 3479 10.1021/jm200187y 21504168
    [Google Scholar]
  7. GLOBOCAN 2022: Latest global cancer data shows rising incidence and stark inequities. 2024 Available from: https://www.uicc.org/news/globocan-2022-latest-global-cancer-data-shows-rising-incidence-and-stark-inequities
  8. Siegel R. Miller K. Fuchs H.E. Jemal A. Cancer statistics, 2022. CA Cancer J. Clin. 2022 72 1 7 33 10.3322/caac.21708 35020204
    [Google Scholar]
  9. Valcarcel-Jimenez L. Frezza C. Fumarate hydratase (FH) and cancer: A paradigm of oncometabolism. Br. J. Cancer 2023 129 10 1546 1557 10.1038/s41416‑023‑02412‑w 37689804
    [Google Scholar]
  10. Reitman Z.J Yan H. Isocitrate dehydrogenase 1 and 2 mutations in cancer: Alterations at a crossroads of cellular metabolism. J. Natl. Cancer Inst. 102 13 932 941 10.1093/jnci/djq187
    [Google Scholar]
  11. Nia H.T. Munn L.L. Jain R.K. Physical traits of cancer. Science 2020 370 6516 eaaz0868 10.1126/science.aaz0868 33122355
    [Google Scholar]
  12. Jang M. Kim S.S. Lee J. Cancer cell metabolism: Implications for therapeutic targets. Exp. Mol. Med. 2013 45 10 e45 10.1038/emm.2013.85 24091747
    [Google Scholar]
  13. Schiliro C. Firestein B.L. Mechanisms of metabolic reprogramming in cancer cells supporting enhanced growth and proliferation. Cells 2021 10 5 1056 10.3390/cells10051056 33946927
    [Google Scholar]
  14. Wu W. Zhao S. Metabolic changes in cancer: Beyond the Warburg effect. Acta Biochim. Biophys. Sin. (Shanghai) 2013 45 1 18 26 10.1093/abbs/gms104 23257292
    [Google Scholar]
  15. Mattiuzzi C. Lippi G. Current cancer epidemiology. J. Epidemiol. Glob. Health 2019 9 4 217 222 10.2991/jegh.k.191008.001 31854162
    [Google Scholar]
  16. Ferlay J. Colombet M. Soerjomataram I. Parkin D.M. Piñeros M. Znaor A. Bray F. Cancer statistics for the year 2020: An overview. Int. J. Cancer 2021 149 4 778 789 10.1002/ijc.33588 33818764
    [Google Scholar]
  17. Wilkinson L. Gathani T. Understanding breast cancer as a global health concern. Br. J. Radiol. 2022 95 1130 20211033 10.1259/bjr.20211033 34905391
    [Google Scholar]
  18. Zagami P. Carey L.A. Triple negative breast cancer: Pitfalls and progress. NPJ Breast Cancer 2022 8 1 95 10.1038/s41523‑022‑00468‑0 35987766
    [Google Scholar]
  19. Nakai K. Hung M.C. Yamaguchi H. A perspective on anti-EGFR therapies targeting triple-negative breast cancer. Am. J. Cancer Res. 2016 6 8 1609 1623 27648353
    [Google Scholar]
  20. Nielsen T.O. Hsu F.D. Jensen K. Cheang M. Karaca G. Hu Z. Hernandez-Boussard T. Livasy C. Cowan D. Dressler L. Akslen L.A. Ragaz J. Gown A.M. Gilks C.B. van de Rijn M. Perou C.M. Immunohistochemical and clinical characterization of the basal-like subtype of invasive breast carcinoma. Clin. Cancer Res. 2004 10 16 5367 5374 10.1158/1078‑0432.CCR‑04‑0220 15328174
    [Google Scholar]
  21. Laskin J.J. Sandler A.B. Epidermal growth factor receptor: A promising target in solid tumours. Cancer Treat. Rev. 2004 30 1 1 17 10.1016/j.ctrv.2003.10.002 14766123
    [Google Scholar]
  22. Thomas R Weihua Z Rethink of EGFR in cancer with its kinase independent function on board. Front. Oncol. 2019 9 800 10.3389/fonc.2019.00800 31508364
    [Google Scholar]
  23. Citri A. Yarden Y. EGF–ERBB signalling: Towards the systems level. Nat. Rev. Mol. Cell Biol. 2006 7 7 505 516 10.1038/nrm1962 16829981
    [Google Scholar]
  24. Maennling A.E. Tur M.K. Niebert M. Klockenbring T. Zeppernick F. Gattenlöhner S. Meinhold-Heerlein I. Hussain A.F. Molecular targeting therapy against EGFR family in breast cancer: Progress and future potentials. Cancers (Basel) 2019 11 12 1826 10.3390/cancers11121826 31756933
    [Google Scholar]
  25. Hori A. Shimoda M. Naoi Y. Kagara N. Tanei T. Miyake T. Shimazu K. Kim S.J. Noguchi S. Vasculogenic mimicry is associated with trastuzumab resistance of HER2-positive breast cancer. Breast Cancer Res. 2019 21 1 88 10.1186/s13058‑019‑1167‑3 31387614
    [Google Scholar]
  26. Tiwari S.R. Mishra P. Raska P. Calhoun B. Abraham J. Moore H. Budd G.T. Fanning A. Valente S. Stewart R. Grobmyer S.R. Montero A.J. Retrospective study of the efficacy and safety of neoadjuvant docetaxel, carboplatin, trastuzumab/pertuzumab (TCH-P) in nonmetastatic HER2-positive breast cancer. Breast Cancer Res. Treat. 2016 158 1 189 193 10.1007/s10549‑016‑3866‑0 27324504
    [Google Scholar]
  27. Xu Z. Zhang Y. Li N. Liu P. Gao L. Gao X. Tie X. Efficacy and safety of lapatinib and trastuzumab for HER2-positive breast cancer: A systematic review and meta-analysis of randomised controlled trials. BMJ Open 2017 7 3 e013053 10.1136/bmjopen‑2016‑013053 28289045
    [Google Scholar]
  28. Geyer C.E. Forster J. Lindquist D. Chan S. Romieu C.G. Pienkowski T. Jagiello-Gruszfeld A. Crown J. Chan A. Kaufman B. Skarlos D. Campone M. Davidson N. Berger M. Oliva C. Rubin S.D. Stein S. Cameron D. Lapatinib plus capecitabine for HER2-positive advanced breast cancer. N. Engl. J. Med. 2006 355 26 2733 2743 10.1056/NEJMoa064320 17192538
    [Google Scholar]
  29. Baselga J. Bradbury I. Eidtmann H. Di Cosimo S. de Azambuja E. Aura C. Gómez H. Dinh P. Fauria K. Van Dooren V. Aktan G. Goldhirsch A. Chang T.W. Horváth Z. Coccia-Portugal M. Domont J. Tseng L.M. Kunz G. Sohn J.H. Semiglazov V. Lerzo G. Palacova M. Probachai V. Pusztai L. Untch M. Gelber R.D. Piccart-Gebhart M. Lapatinib with trastuzumab for HER2-positive early breast cancer (NeoALTTO): A randomised, open-label, multicentre, phase 3 trial. Lancet 2012 379 9816 633 640 10.1016/S0140‑6736(11)61847‑3 22257673
    [Google Scholar]
  30. Al-Abdulkarim H.A. El-khatib R.M. Aljohani F.S. Mahran A. Alharbi A. Mersal G.A.M. El-Metwaly N.M. Abu-Dief A.M. Optimization for synthesized quinoline-based Cr3+, VO2+, Zn2+ and Pd2+complexes: DNA interaction, biological assay and in-silico treatments for verification. J. Mol. Liq. 2021 339 116797 , 339, 116797 10.1016/j.molliq.2021.116797
    [Google Scholar]
  31. Mohamad A.D.M. Abualreish M.J.A. Abu-Dief A.M. Temperature and salt effects of the kinetic reactions of substituted 2-pyridylmethylene-8-quinolyl iron (II) complexes as antimicrobial, anti-cancer, and antioxidant agents with cyanide ions. Can. J. Chem. 2021 99 9 763 772 10.1139/cjc‑2020‑0412
    [Google Scholar]
  32. El-Remaily M.A.E.A.A.A. Abu-Dief A.M. Elhady O. Green synthesis of TiO 2 nanoparticles as an efficient heterogeneous catalyst with high reusability for synthesis of 1,2‐dihydroquinoline derivatives. Appl. Organomet. Chem. 2019 33 8 e5005 10.1002/aoc.5005
    [Google Scholar]
  33. Ajani O.O. Iyaye K.T. Ademosun O.T. Recent advances in chemistry and therapeutic potential of functionalized quinoline motifs – A review. RSC Advances 2022 12 29 18594 18614 10.1039/D2RA02896D 35873320
    [Google Scholar]
  34. Shiro T. Fukaya T. Tobe M. The chemistry and biological activity of heterocycle-fused quinolinone derivatives: A review. Eur. J. Med. Chem. 2015 97 397 408 10.1016/j.ejmech.2014.12.004 25532473
    [Google Scholar]
  35. Mukherjee S. Pal M. Medicinal chemistry of quinolines as emerging anti-inflammatory agents: An overview. Curr. Med. Chem. 2013 20 35 4386 4410 10.2174/09298673113209990170 23862618
    [Google Scholar]
  36. Prasad M.V.V.V. Rao R.H.R. Veeranna V. Chennupalli V.S. Sathish B. Novel quinolone derivatives: Synthesis and antioxidant activity. Russ. J. Gen. Chem. 2021 91 12 2522 2526 10.1134/S1070363221120239 35068916
    [Google Scholar]
  37. Moodley R. Mashaba C. Rakodi G. Ncube N. Maphoru M. Balogun M. Jordan A. Warner D. Khan R. Tukulula M. New quinoline–urea–benzothiazole hybrids as promising antitubercular agents: Synthesis, in vitro antitubercular activity, cytotoxicity studies, and in silico ADME profiling. Pharmaceuticals (Basel) 2022 15 5 576 10.3390/ph15050576 35631402
    [Google Scholar]
  38. Govindarao K. Sriniwasan N. Suresh R. Quinoline conjugated 2-azetidinone derivatives as anti-breast cancer agents: In vitro antiproliferative and anti-EGFR activities, molecular docking and in-silico drug likeliness studies. J. Saudi Chem. Soc. 2022 26 3 101471 10.1016/j.jscs.2022.101471
    [Google Scholar]
  39. Hamdy R. Elseginy S.A. Ziedan N.I. Jones A.T. Westwell A.D. New quinoline-based heterocycles as anticancer agents targeting bcl-2. Molecules 2019 24 7 1274 10.3390/molecules24071274 30986908
    [Google Scholar]
  40. Abner E. Stoszko M. Zeng L. Chen H.C. Izquierdo-Bouldstridge A. Konuma T. Zorita E. Fanunza E. Zhang Q. Mahmoudi T. Zhou M.M. Filion G.J. Jordan A. A new quinoline BRD4 inhibitor targets a distinct latent HIV-1 reservoir for reactivation from other “shock” drugs. J. Virol. 2018 92 10 e02056-17 10.1128/JVI.02056‑17 29343578
    [Google Scholar]
  41. Afzal O. Kumar S. Haider M.R. Ali M.R. Kumar R. Jaggi M. Bawa S. A review on anticancer potential of bioactive heterocycle quinoline. Eur. J. Med. Chem. 2015 97 871 910 10.1016/j.ejmech.2014.07.044
    [Google Scholar]
  42. Tomasz K. Sylwia F. Adamowicz J. Szeliski K. Quinolones as a potential drug in genitourinary cancer treatment. Front. Oncol. 2022 12 890337 10.3389/fonc.2022.890337 35756639
    [Google Scholar]
  43. Shagufta S. Ahmad I. An insight into the therapeutic potential of quinazoline derivatives as anticancer agents. MedChemComm 2017 8 5 871 885 10.1039/C7MD00097A 30108803
    [Google Scholar]
  44. Marella A. Tanwar O.P. Saha R. Ali M.R. Srivastava S. Akhter M. Shaquiquzzaman M. Alam M.M. Quinoline: A versatile heterocyclic. Saudi Pharm. J. 2013 21 1 1 12 10.1016/j.jsps.2012.03.002 23960814
    [Google Scholar]
  45. Mhaske G.S. Sen A.K. Shah A. Khiste R.H. Dale A.V. Sen D.B. In silico identification of novel quinoline-3-carboxamide derivatives targeting platelet-derived growth factor receptor. Curr. Cancer Ther. Rev. 2022 18 2 131 142 10.2174/1573394718666220421111546
    [Google Scholar]
  46. Verma C. Quraishi M.A. Ebenso E.E. Quinoline and its derivatives as corrosion inhibitors: A review. Surf. Interfaces 2020 21 100634 10.1016/j.surfin.2020.100634
    [Google Scholar]
  47. Ilakiyalakshmi M. Arumugam Napoleon A. Review on recent development of quinoline for anticancer activities. Arab. J. Chem. 2022 15 11 104168 10.1016/j.arabjc.2022.104168
    [Google Scholar]
  48. Bharti A. Bijauliya R.K. Yadav A. Suman The chemical and pharmacological advancements of quinoline: A mini review. J. Drug Deliv. Ther. 2022 12 4 211 215 10.22270/jddt.v12i4.5561
    [Google Scholar]
  49. Elebiju O.F. Ajani O.O. Oduselu G.O. Ogunnupebi T.A. Adebiyi E. Recent advances in functionalized quinoline scaffolds and hybrids - Exceptional pharmacophore in therapeutic medicine. Front Chem. 2023 10 1074331 10.3389/fchem.2022.1074331 36688036
    [Google Scholar]
  50. Ibrahim D.A. Abou El Ella D.A. El-Motwally A.M. Aly R.M. Motwally E. Rasha M.A. Molecular design and synthesis of certain new quinoline derivatives having potential anticancer activity. Eur. J. Med. Chem. 2015 102 115 131 10.1016/j.ejmech.2015.07.030
    [Google Scholar]
  51. Daina A. Michielin O. Zoete V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep. 2017 7 1 42717 10.1038/srep42717 28256516
    [Google Scholar]
  52. Walters W.P. Stahl M.T. Murcko M.A. Virtual screening - An overview. Drug Discov. Today 1998 3 4 160 178 10.1016/S1359‑6446(97)01163‑X
    [Google Scholar]
  53. Sliwoski G. Kothiwale S. Meiler J. Lowe E.W. Computational methods in drug discovery. Pharmacol. Rev. 2014 66 1 334 395 10.1124/pr.112.007336 24381236
    [Google Scholar]
  54. Kapetanovic M. Computer-aided drug discovery and development (CADDD): In silico-chemico-biological approach. Chem. Biol. Interact. 2008 171 2 165-176 176 10.1016/j.cbi.2006.12.006
    [Google Scholar]
  55. Umar A.B. Uzairu A. Shallangwa G.A. Uba S. Design of potential anti-melanoma agents against SK-MEL-5 cell line using QSAR modeling and molecular docking methods. SN Appl. Sci. 2020 2 5 815 10.1007/s42452‑020‑2620‑8
    [Google Scholar]
  56. Das T. Mehta C.H. Nayak U.Y. Multiple approaches for achieving drug solubility: An in silico perspective. Drug Discov. Today 2020 25 7 1206 1212 10.1016/j.drudis.2020.04.016 32353425
    [Google Scholar]
  57. Abdullahi M. Adeniji S.E. In silico molecular docking and ADME/pharmacokinetic prediction studies of some novel carboxamide derivatives as anti-tubercular agents. Chemistry Africa 2020 3 4 989 1000 10.1007/s42250‑020‑00162‑3
    [Google Scholar]
  58. Doman T.N. McGovern S.L. Witherbee B.J. Kasten T.P. Kurumbail R. Stallings W.C. Connolly D.T. Shoichet B.K. Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. J. Med. Chem. 2002 45 11 2213 2221 10.1021/jm010548w 12014959
    [Google Scholar]
  59. Abdel-Rahman L.H. Abu-Dief A.M. Basha M. Hassan Abdel-Mawgoud A.A. Three novel Ni(II), VO(II) and Cr(III) mononuclear complexes encompassing potentially tridentate imine ligand: Synthesis, structural characterization, DNA interaction, antimicrobial evaluation and anticancer activity. Appl. Organomet. Chem. 2017 31 11 e3750 10.1002/aoc.3750
    [Google Scholar]
  60. Abu-Dief A.M. El-Metwaly N.M. Alzahrani S.O. Bawazeer A.M. Shaaban S. Adam M.S.S. Targeting ctDNA binding and elaborated in-vitro assessments concerning novel Schiff base complexes: Synthesis, characterization, DFT and detailed in-silico confirmation. J. Mol. Liq. 2021 322 114977 , 322, 114977 10.1016/j.molliq.2020.114977
    [Google Scholar]
  61. Aljohani F.S. Omran O.A. Ahmed E.A. Al-Farraj E.S. Elkady E.F. Alharbi A. El-Metwaly N.M. Omar Barnawi I. Abu-Dief A.M. Design, structural inspection of new bis(1H-benzo[d]imidazol-2-yl)methanone complexes: Biomedical applications and theoretical implementations via DFT and docking approaches. Inorg. Chem. Commun. 2023 148 110331 10.1016/j.inoche.2022.110331
    [Google Scholar]
  62. Morris G.M. Huey R. Lindstrom W. Sanner M.F. Belew R.K. Goodsell D.S. Olson A.J. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem. 2009 30 16 2785 2791 10.1002/jcc.21256 19399780
    [Google Scholar]
  63. Aljohani F.S. El-Dabea T. El-Khatib R.M. Abdou A. Alzahrani S. Omar Barnawi I. El-Remaily M.A.E.A.A.A. Abu-Dief A.M. Innovation, structural inspection for new mixed complexes: DNA binding, biomedical applications and molecular docking approaches. J. Taibah Univ. Sci. 2024 18 1 2350087 10.1080/16583655.2024.2350087
    [Google Scholar]
  64. Trott O. Olson A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 2010 31 2 455 461 10.1002/jcc.21334 19499576
    [Google Scholar]
  65. Gasteiger E. Hoogland C. Gattiker A. Duvaud S. Wilkins M. Protein identification and analysis tools on the expasy server. The Proteomics Protocols Handbook Humana Press Walker J.M. 2005 571 607 10.1385/1‑59259‑890‑0:571
    [Google Scholar]
  66. Laskowski R.A. Jabłońska J. Pravda L. Vařeková R.S. Thornton J.M. PDBsum: Structural summaries of PDB entries. Protein Sci. 2018 27 1 129 134 10.1002/pro.3289 28875543
    [Google Scholar]
  67. 25.2.6. PROCHECK: Validation of protein-structure coordinates. 2006 Available from: https://onlinelibrary.wiley.com/iucr/itc/Fa/ch25o2v0001/sec25o2o6/
  68. Daina A. Zoete V. A BOILED‐Egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem 2016 11 11 1117 1121 10.1002/cmdc.201600182 27218427
    [Google Scholar]
  69. Colovos C. Yeates T.O. Verification of protein structures: Patterns of nonbonded atomic interactions. Protein Sci. 1993 2 9 1511 1519 10.1002/pro.5560020916 8401235
    [Google Scholar]
  70. Bowie J.U. Lüthy R. Eisenberg D. A method to identify protein sequences that fold into a known three-dimensional structure. Science 1991 253 5016 164 170 10.1126/science.1853201 1853201
    [Google Scholar]
  71. Laskowski R.A. MacArthur M.W. Moss D.S. Thornton J.M. PROCHECK: A program to check the stereochemical quality of protein structures. J. Appl. Cryst. 1993 26 2 283 291 10.1107/S0021889892009944
    [Google Scholar]
  72. Laskowski R. Rullmann J.A.C. MacArthur M. Kaptein R. Thornton J. AQUA and PROCHECK-NMR: Programs for checking the quality of protein structures solved by NMR. J. Biomol. NMR 1996 8 4 477 486 10.1007/BF00228148 9008363
    [Google Scholar]
  73. Morris A.L. MacArthur M.W. Hutchinson E.G. Thornton J.M. Stereochemical quality of protein structure coordinates. Proteins 1992 12 4 345 364 10.1002/prot.340120407 1579569
    [Google Scholar]
  74. Eisenberg D. Bowie J.U. Lüthy R. Choe S. Three-dimensional profiles for analysing protein sequence–structure relationships. Faraday Discuss. 1992 93 93 25 34 10.1039/FD9929300025 1290936
    [Google Scholar]
  75. Yu W. MacKerell A.D. Computer-aided drug design methods. Methods Mol. Biol. 2017 1520 85 106 10.1007/978‑1‑4939‑6634‑9_5 27873247
    [Google Scholar]
  76. Kitchen D.B. Decornez H. Furr J.R. Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov. 2004 3 11 935 949 10.1038/nrd1549 15520816
    [Google Scholar]
  77. Li J. Pan Y.Y. Zhang Y. Synergistic interaction between sorafenib and gemcitabine in EGFR-TKI-sensitive and EGFR-TKI-resistant human lung cancer cell lines. Oncol. Lett. 2013 5 2 440 446 10.3892/ol.2012.1017 23420122
    [Google Scholar]
  78. Iyer R. Fetterly G. Lugade A. Thanavala Y. Sorafenib: A clinical and pharmacologic review. Expert Opin. Pharmacother. 2010 11 11 1943 1955 10.1517/14656566.2010.496453 20586710
    [Google Scholar]
  79. Tian S. Wang J. Li Y. Li D. Xu L. Hou T. The application of in silico drug-likeness predictions in pharmaceutical research. Adv. Drug Deliv. Rev. 2015 86 2 10 10.1016/j.addr.2015.01.009 25666163
    [Google Scholar]
  80. Mhaske G.S. Thorat S.R. Pawar V.S. Pawar R.S. Jambhulkar S.R. Ghumre O.A. Computational molecular docking and in-silico, ADMET prediction studies of quinoline derivatives as EPHB4 inhibitor. Current Indian Science 2024 2 e2210299X265033 10.2174/012210299X265033240116113623
    [Google Scholar]
  81. Daina A. Michielin O. Zoete V. iLOGP: A simple, robust, and efficient description of n-octanol/water partition coefficient for drug design using the GB/SA approach. J. Chem. Inf. Model. 2014 54 12 3284 3301 10.1021/ci500467k 25382374
    [Google Scholar]
  82. Lipinski C.A. Lombardo F. Dominy B.W. Feeney P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 2001 46 1-3 3 26 10.1016/S0169‑409X(00)00129‑0 11259830
    [Google Scholar]
/content/journals/ccand/10.2174/012212697X335334241101023647
Loading
/content/journals/ccand/10.2174/012212697X335334241101023647
Loading

Data & Media loading...


  • Article Type:
    Research Article
Keywords: bioavailability score ; binding affinity ; molecular docking ; pharmacokinetics ; CADD ; C-met ; EGFR
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test