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image of Identification of Leukemia Enzyme Inhibitors by Molecular Modeling and Machine Learning Approaches

Abstract

Introduction

Acute Myeloid Leukemia (AML) is characterized by uncontrolled proliferation of aberrant white blood cells, contributing to high morbidity and mortality rates, particularly among males. Targeting key enzymes involved in AML progression represents a promising therapeutic strategy.

Methods

This study applied computational drug discovery techniques to identify potential AML inhibitors targeting Tyrosine Kinase (TK), Protein Kinase C Beta (PKC), Myeloid Cell Leukemia 1 (MCL-1), and Histone Acetyltransferase (HAT). Inhibitors were retrieved from the ChEMBL database, molecular descriptors computed using PaDEL, and machine learning models evaluated LazyPredict. QSAR modeling with a Random Forest Regressor achieved >90% accuracy in predicting pIC values. Lead compounds underwent molecular docking (PyRx), ADMET profiling (ADMETlab2.0), and molecular dynamics simulations (NMSim) to assess binding affinity, pharmacokinetics, and stability.

Results

The QSAR model showed a strong correlation between predicted and experimental pIC values. Docking studies and ADMET analysis identified a novel compound with favorable pharmacokinetic properties and strong inhibitory potential. MD simulations confirmed stable binding conformations within target enzymes.

Discussion

The integration of QSAR modeling, docking, and ADMET screening efficiently identified promising AML enzyme inhibitors, demonstrating the value of computational pipelines in early-stage drug discovery.

Conclusion

This study highlights a novel lead compound with potential as a multi-target AML therapeutic candidate, warranting further and validation.

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2025-08-07
2025-09-14

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References

  1. Senapati J. Sasaki K. Issa G.C. Lipton J.H. Radich J.P. Jabbour E. Kantarjian H.M. Management of chronic myeloid leukemia in 2023 – Common ground and common sense. Blood Cancer J. 2023 13 1 58 10.1038/s41408‑023‑00823‑9 37088793
    [Google Scholar]
  2. Pelcovits A. Niroula R. Acute myeloid leukemia: A review. RI. Med. J. 2020 103 3 38 40 32236160
    [Google Scholar]
  3. Kayser S. Levis M.J. The clinical impact of the molecular landscape of acute myeloid leukemia. Haematologica 2023 108 2 308 320 10.3324/haematol.2022.280801 36722402
    [Google Scholar]
  4. Stelmach P. Trumpp A. Leukemic stem cells and therapy resistance in acute myeloid leukemia. Haematologica 2023 108 2 353 366 10.3324/haematol.2022.280800 36722405
    [Google Scholar]
  5. Bispo J.A.B. Pinheiro P.S. Kobetz E.K. Epidemiology and etiology of leukemia and lymphoma. Cold Spring Harb. Perspect. Med. 2020 10 6 a034819 10.1101/cshperspect.a034819 31727680
    [Google Scholar]
  6. Lin W.Y. Fordham S.E. Hungate E. Sunter N.J. Elstob C. Xu Y. Park C. Quante A. Strauch K. Gieger C. Skol A. Rahman T. Sucheston-Campbell L. Wang J. Hahn T. Clay-Gilmour A.I. Jones G.L. Marr H.J. Jackson G.H. Menne T. Collin M. Ivey A. Hills R.K. Burnett A.K. Russell N.H. Fitzgibbon J. Larson R.A. Le Beau M.M. Stock W. Heidenreich O. Alharbi A. Allsup D.J. Houlston R.S. Norden J. Dickinson A.M. Douglas E. Lendrem C. Daly A.K. Palm L. Piechocki K. Jeffries S. Bornhäuser M. Röllig C. Altmann H. Ruhnke L. Kunadt D. Wagenführ L. Cordell H.J. Darlay R. Andersen M.K. Fontana M.C. Martinelli G. Marconi G. Sanz M.A. Cervera J. Gómez-Seguí I. Cluzeau T. Moreilhon C. Raynaud S. Sill H. Voso M.T. Lo-Coco F. Dombret H. Cheok M. Preudhomme C. Gale R.E. Linch D. Gaal-Wesinger J. Masszi A. Nowak D. Hofmann W.K. Gilkes A. Porkka K. Milosevic Feenstra J.D. Kralovics R. Grimwade D. Meggendorfer M. Haferlach T. Krizsán S. Bödör C. Stölzel F. Onel K. Allan J.M. Genome-wide association study identifies susceptibility loci for acute myeloid leukemia. Nat. Commun. 2021 12 1 6233 10.1038/s41467‑021‑26551‑x 34716350
    [Google Scholar]
  7. Kantarjian H. Kadia T. DiNardo C. Daver N. Borthakur G. Jabbour E. Garcia-Manero G. Konopleva M. Ravandi F. Acute myeloid leukemia: Current progress and future directions. Blood Cancer J. 2021 11 2 41 10.1038/s41408‑021‑00425‑3 33619261
    [Google Scholar]
  8. Kantarjian H.M. Kadia T.M. DiNardo C.D. Welch M.A. Ravandi F. Acute myeloid leukemia: Treatment and research outlook for 2021 and the MD Anderson approach. Cancer 2021 127 8 1186 1207 10.1002/cncr.33477 33734442
    [Google Scholar]
  9. Hunter T. Signaling - 2000 and Beyond. Cell 2000 100 1 113 127 10.1016/S0092‑8674(00)81688‑8 10647936
    [Google Scholar]
  10. Iqbal A. Dubey M. Randhawa A.S. Khanikar D. Hazarika M. Roy P.S. Dutta C. Barbhuiyan S. Deka R. Improved treatment outcomes with modified induction therapy in Acute Myeloid Leukemia (AML): A retrospective observational study from a regional cancer center. Cureus 2024 16 1 e53303 10.7759/cureus.53303 38435958
    [Google Scholar]
  11. Macaron W. Sargsyan Z. Short N.J. Hyperleukocytosis and leukostasis in acute and chronic leukemias. Leuk. Lymphoma 2022 63 8 1780 1791 10.1080/10428194.2022.2056178 35357988
    [Google Scholar]
  12. Deak D. Gorcea-Andronic N. Sas V. Teodorescu P. Constantinescu C. Iluta S. Pasca S. Hotea I. Turcas C. Moisoiu V. Zimta A.A. Galdean S. Steinheber J. Rus I. Rauch S. Richlitzki C. Munteanu R. Jurj A. Petrushev B. Selicean C. Marian M. Soritau O. Andries A. Roman A. Dima D. Tanase A. Sigurjonsson O. Tomuleasa C. A narrative review of central nervous system involvement in acute leukemias. Ann. Transl. Med. 2021 9 1 68 68 10.21037/atm‑20‑3140 33553361
    [Google Scholar]
  13. Stubbins R.J. Francis A. Kuchenbauer F. Sanford D. Management of acute myeloid leukemia: A review for general practitioners in oncology. Curr. Oncol. 2022 29 9 6245 6259 10.3390/curroncol29090491 36135060
    [Google Scholar]
  14. Attwood M.M. Fabbro D. Sokolov A.V. Knapp S. Schiöth H.B. Trends in kinase drug discovery: Targets, indications and inhibitor design. Nat. Rev. Drug Discov. 2021 20 11 839 861 10.1038/s41573‑021‑00252‑y 34354255
    [Google Scholar]
  15. Adelusi T.I. Oyedele A-Q.K. Boyenle I.D. Ogunlana A.T. Adeyemi R.O. Ukachi C.D. Molecular modeling in drug discovery. Inform. Med. Unlocked 2022 29 100880 10.1016/j.imu.2022.100880
    [Google Scholar]
  16. Keyvanpour M.R. Shirzad M.B. An analysis of QSAR research based on machine learning concepts. Curr. Drug Discov. Technol. 2021 18 1 17 30 10.2174/1570163817666200316104404 32178612
    [Google Scholar]
  17. Muratov E.N. Bajorath J. Sheridan R.P. Tetko I.V. Filimonov D. Poroikov V. Oprea T.I. Baskin I.I. Varnek A. Roitberg A. Isayev O. Curtalolo S. Fourches D. Cohen Y. Aspuru-Guzik A. Winkler D.A. Agrafiotis D. Cherkasov A. Tropsha A. QSAR without borders. Chem. Soc. Rev. 2020 49 11 3525 3564 10.1039/D0CS00098A 32356548
    [Google Scholar]
  18. Saravanan K.M. Balasubramanian H. Nallusamy S. Samuel S. Sequence and structural analysis of two designed proteins with 88% identity adopting different folds. Protein Eng. Des. Sel. 2010 23 12 911 918 10.1093/protein/gzq070 20952437
    [Google Scholar]
  19. Saravanan K.M. Selvaraj S. Search for identical octapeptides in unrelated proteins: Structural plasticity revisited. Pept. Sci. 2012 98 11 26 10.1002/bip.21676
    [Google Scholar]
  20. Saravanan K.M. Selvaraj S. Dihedral angle preferences of amino acid residues forming various non-local interactions in proteins. J. Biol. Phys. 2017 43 2 265 278 10.1007/s10867‑017‑9451‑x 28577238
    [Google Scholar]
  21. Sheng Y. Zhang C. Huang J. Wang D. Xiao Q. Zhang H. Ha X. Comparison of conventional mathematical model and machine learning model based on recent advances in mathematical models for predicting diabetic kidney disease. Digit. Health 2024 10 20552076241238093 10.1177/20552076241238093 38465295
    [Google Scholar]
  22. Zhang H. Saravanan K.M. Advances in Deep Learning Assisted Drug Discovery Methods: A Self-review. Curr. Bioinform. 2024 19 10 891 907 10.2174/0115748936285690240101041704
    [Google Scholar]
  23. Sreeraman S. Kannan M.P. Singh Kushwah R.B. Sundaram V. Veluchamy A. Thirunavukarasou A. Saravanan K.M. Drug Design and Disease Diagnosis: The Potential of Deep Learning Models in Biology. Curr. Bioinform. 2023 18 3 208 220 10.2174/1574893618666230227105703
    [Google Scholar]
  24. Zhang H. Saravanan K.M. Lin J. Liao L. Ng J.T.Y. Zhou J. Wei Y. DeepBindPoc: A deep learning method to rank ligand binding pockets using molecular vector representation. PeerJ 2020 8 e8864 10.7717/peerj.8864 32292649
    [Google Scholar]
  25. Zhang H. Saravanan K.M. Zhang J.Z.H. DeepBindGCN: Integrating molecular vector representation with graph convolutional neural networks for protein–ligand interaction prediction. Molecules 2023 28 12 4691 10.3390/molecules28124691 37375246
    [Google Scholar]
  26. Gramatica P. Principles of QSAR Modeling. Int. J. Quant Struct.-. Prop Relat 2020 5 3 61 97 10.4018/IJQSPR.20200701.oa1
    [Google Scholar]
  27. Saravanan K.M. Wan J.F. Dai L. Zhang J. Zhang J.Z.H. Zhang H. A deep learning based multi-model approach for predicting drug-like chemical compound’s toxicity. Methods 2024 226 164 175 10.1016/j.ymeth.2024.04.020 38702021
    [Google Scholar]
  28. Charbuty B. Abdulazeez A. Classification Based on Decision Tree Algorithm for Machine Learning. J. Appl. Sci. Technol Trends 2021 2 1 20 28 10.38094/jastt20165
    [Google Scholar]
  29. Sheykhmousa M. Mahdianpari M. Ghanbari H. Mohammadimanesh F. Ghamisi P. Homayouni S. Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2020 13 6308 6325 10.1109/JSTARS.2020.3026724
    [Google Scholar]
  30. Maulud D. Abdulazeez A.M. A Review on Linear Regression Comprehensive in Machine Learning. J. Appl. Sci. Technol Trends 2020 1 2 140 147 10.38094/jastt1457
    [Google Scholar]
  31. Shah K. Patel H. Sanghvi D. Shah M. A comparative analysis of logistic regression, random forest and KNN models for the text classification. Aug Human Res. 2020 5 1 12 10.1007/s41133‑020‑00032‑0
    [Google Scholar]
  32. Cervantes J. Garcia-Lamont F. Rodríguez-Mazahua L. Lopez A. A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing 2020 408 189 215 10.1016/j.neucom.2019.10.118
    [Google Scholar]
  33. Jackins V. Vimal S. Kaliappan M. Lee M.Y. AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes. J. Supercomput. 2021 77 5 5198 5219 10.1007/s11227‑020‑03481‑x
    [Google Scholar]
  34. Raza B. Aslam A. Sher A. Malik A.K. Faheem M. Autonomic performance prediction framework for data warehouse queries using lazy learning approach. Appl. Soft Comput. 2020 91 106216 10.1016/j.asoc.2020.106216
    [Google Scholar]
  35. Mahmood A. Sandali Y. Wang J.L. Easy and fast prediction of green solvents for small molecule donor-based organic solar cells through machine learning. Phys. Chem. Chem. Phys. 2023 25 15 10417 10426 10.1039/D3CP00177F 36987914
    [Google Scholar]
  36. Tang R. Kong D. Huang L. Xue H. Large language models can be lazy learners: Analyze shortcuts in in-context learning. arXiv 2023 4645 4657 10.18653/v1/2023.findings‑acl.284
    [Google Scholar]
  37. Bhanumathy K.K. Balagopal A. Vizeacoumar F.S. Protein tyrosine kinases: Their roles and their targeting in leukemia. Cancers 2021 13 184 10.3390/cancers13020184
    [Google Scholar]
  38. Megías-Vericat J.E. Ballesta-López O. Barragán E. Martínez-Cuadrón D. Montesinos P. Tyrosine kinase inhibitors for acute myeloid leukemia: A step toward disease control? Blood Rev. 2020 44 100675 10.1016/j.blre.2020.100675 32147087
    [Google Scholar]
  39. Roskoski R. Properties of FDA-approved small molecule protein kinase inhibitors: A 2024 update. Pharmacol. Res. 2024 200 107059 10.1016/j.phrs.2024.107059 38216005
    [Google Scholar]
  40. Thomas L.W. Lam C. Edwards S.W. Mcl‐1; The molecular regulation of protein function. FEBS Lett. 2010 584 14 2981 2989 10.1016/j.febslet.2010.05.061 20540941
    [Google Scholar]
  41. Glaser S.P. Lee E.F. Trounson E. Bouillet P. Wei A. Fairlie W.D. Izon D.J. Zuber J. Rappaport A.R. Herold M.J. Alexander W.S. Lowe S.W. Robb L. Strasser A. Anti-apoptotic Mcl-1 is essential for the development and sustained growth of acute myeloid leukemia. Genes Dev. 2012 26 2 120 125 10.1101/gad.182980.111 22279045
    [Google Scholar]
  42. Gao X. Lin J. Ning Q. Gao L. Yao Y. Zhou J. Li Y. Wang L. Yu L. A histone acetyltransferase p300 inhibitor C646 induces cell cycle arrest and apoptosis selectively in AML1-ETO-positive AML cells. PLoS One 2013 8 2 e55481 10.1371/journal.pone.0055481 23390536
    [Google Scholar]
  43. McKnight P.E. Najab J. Mann‐Whitney U test. The Corsini Encyclopedia of Psychology. Wiley 2010 1 1 10.1002/9780470479216.corpsy0524
    [Google Scholar]
  44. Zdrazil B. Felix E. Hunter F. Manners E.J. Blackshaw J. Corbett S. de Veij M. Ioannidis H. Lopez D.M. Mosquera J.F. Magarinos M.P. Bosc N. Arcila R. Kizilören T. Gaulton A. Bento A.P. Adasme M.F. Monecke P. Landrum G.A. Leach A.R. The ChEMBL Database in 2023: A drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic Acids Res. 2024 52 D1 D1180 D1192 10.1093/nar/gkad1004 37933841
    [Google Scholar]
  45. PyMOL by Schrödinger Available from:https://www.pymol.org/
  46. Wallace A.C. Laskowski R.A. Thornton J.M. LIGPLOT: A program to generate schematic diagrams of protein-ligand interactions. Protein Eng. Des. Sel. 1995 8 2 127 134 10.1093/protein/8.2.127 7630882
    [Google Scholar]
  47. Xiong G. Wu Z. Yi J. Fu L. Yang Z. Hsieh C. Yin M. Zeng X. Wu C. Lu A. Chen X. Hou T. Cao D. ADMETlab 2.0: An integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res. 2021 49 W1 W5 W14 10.1093/nar/gkab255 33893803
    [Google Scholar]
  48. Pettersen E.F. Goddard T.D. Huang C.C. Couch G.S. Greenblatt D.M. Meng E.C. Ferrin T.E. UCSF Chimera - A visualization system for exploratory research and analysis. J. Comput. Chem. 2004 25 13 1605 1612 10.1002/jcc.20084 15264254
    [Google Scholar]
  49. Kumar S.U. Rajan B. Kumar D.T. Cathryn R.H. Das S. Zayed H. Emmanuel Jebaraj Walter C. Ramanathan G. Priya Doss C. G. Comparison of potential inhibitors and targeting fat mass and obesity-associated protein causing diabesity through docking and molecular dynamics strategies. J. Cell. Biochem. 2021 122 11 1625 1638 10.1002/jcb.30109 34289159
    [Google Scholar]
  50. Dallakyan S. Olson A.J. Small-molecule library screening by docking with PyRx BT - chemical biology: Methods and protocols. Chemical Biology. Hempel J.E. Williams C.H. Hong C.C. New York, NY Springer New York 2015 243 250 10.1007/978‑1‑4939‑2269‑7_19
    [Google Scholar]
  51. Krüger D.M. Ahmed A. Gohlke H. NMSim Web Server: Integrated approach for normal mode-based geometric simulations of biologically relevant conformational transitions in proteins. Nucleic Acids Res. 2012 40 W1 W310 W316 10.1093/nar/gks478 22669906
    [Google Scholar]
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Identification of Leukemia Enzyme Inhibitors by Molecular Modeling and Machine Learning Approaches
Bentham Science Publishers ; https://doi.org/10.2174/0122127968399382250722063606
/content/journals/ccb/10.2174/0122127968399382250722063606
/content/journals/ccb/10.2174/0122127968399382250722063606
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  • Article Type:     Research Article
Keywords: machine learning ; quantitative structure-activity relationship ; enzyme inhibitors ; Leukemia therapy ; molecular docking simulation ; drug screening

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