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Volume 31, Issue 16
  • ISSN: 1381-6128
  • E-ISSN: 1873-4286

Abstract

Introduction

The COVID-19 pandemic has necessitated rapid advancements in therapeutic discovery. This study presents an integrated approach combining machine learning (ML) and network pharmacology to identify potential non-covalent inhibitors against pivotal proteins in COVID-19 pathogenesis, specifically B-cell lymphoma 2 (BCL2) and Epidermal Growth Factor Receptor (EGFR).

Methods

Employing a dataset of 13,107 compounds, ML algorithms such as k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes (NB) were utilized for screening and predicting active inhibitors based on molecular features. Molecular docking and molecular dynamics simulations, conducted over a 100 nanosecond period, enhanced the ML-based screening by providing insights into the binding affinities and interaction dynamics with BCL2 and EGFR. Network pharmacology analysis identified these proteins as hub targets within the COVID-19 protein-protein interaction network, highlighting their roles in apoptosis regulation and cellular signaling.

Results

The identified inhibitors exhibited strong binding affinities, suggesting potential efficacy in disrupting viral life cycles and impeding disease progression. Comparative analysis with existing literature affirmed the relevance of BCL2 and EGFR in COVID-19 therapy and underscored the novelty of integrating network pharmacology with ML. This multidisciplinary approach establishes a framework for emerging pathogen treatments and advocates for subsequent and validation, emphasizing a multi-targeted drug design strategy against viral adaptability.

Conclusion

This study's findings are crucial for the ongoing development of therapeutic agents against COVID-19, leveraging computational and network-based strategies.

© 2025 Bentham Science Publishers
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2025-01-16
2025-05-23

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References

  1. WaqasM. HaiderA. RehmanA. QasimM. UmarA. SufyanM. AkramH.N. MirA. RazzaqR. RasoolD. TahirR.A. SehgalS.A. Immunoinformatics and molecular docking studies predicted potential multiepitope-based peptide vaccine and novel compounds against novel SARS-CoV-2 through virtual screening.BioMed Res. Int.20212021112010.1155/2021/159683433728324
     [Google Scholar]
  2. ul QamarTahir Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico approaches.PLoS One20201512e0244176
     [Google Scholar]
  3. MoazzamM. SajidM.I. ShahidH. ButtJ. BashirI. JamshaidM. ShiraziA.N. TiwariR.K. Understanding COVID-19: From origin to potential therapeutics.Int. J. Environ. Res. Public Health20201716590410.3390/ijerph1716590432823901
     [Google Scholar]
  4. YadavR. ChaudharyJ.K. JainN. ChaudharyP.K. KhanraS. DhamijaP. SharmaA. KumarA. HanduS. Role of structural and non-structural proteins and therapeutic targets of SARS-CoV-2 for COVID-19.Cells202110482110.3390/cells1004082133917481
     [Google Scholar]
  5. Ortiz-PradoE. Simbaña-RiveraK. Gómez- BarrenoL. Rubio-NeiraM. GuamanL.P. KyriakidisN.C. MuslinC. JaramilloA.M.G. Barba-OstriaC. Cevallos-RobalinoD. Sanches-SanMiguelH. UnigarroL. ZalakeviciuteR. GadianN. López-CortésA. Clinical, molecular, and epidemiological characterization of the SARS-CoV-2 virus and the coronavirus disease 2019 (COVID-19), a comprehensive literature review.Diagn. Microbiol. Infect. Dis.202098111509410.1016/j.diagmicrobio.2020.11509432623267
     [Google Scholar]
  6. YanF. GaoF. An overview of potential inhibitors targeting non-structural proteins 3 (PLpro and Mac1) and 5 (3CLpro/Mpro) of SARS-CoV-2.Comput. Struct. Biotechnol. J.2021194868488310.1016/j.csbj.2021.08.03634457214
     [Google Scholar]
  7. LowZ.Y. ZabidiN.Z. YipA.J.W. PuniyamurtiA. ChowV.T.K. LalS.K. SARS-CoV-2 non-structural proteins and their roles in host immune evasion.Viruses2022149199110.3390/v1409199136146796
     [Google Scholar]
  8. BorkotokyS. DeyD. HazarikaZ. Interactions of angiotensin-converting enzyme-2 (ACE2) and SARS-CoV-2 spike receptor-binding domain (RBD): A structural perspective.Mol. Biol. Rep.20235032713272110.1007/s11033‑022‑08193‑436562937
     [Google Scholar]
  9. SeyranM. TakayamaK. UverskyV.N. LundstromK. PalùG. SherchanS.P. AttrishD. RezaeiN. AljabaliA.A.A. GhoshS. PizzolD. ChauhanG. AdadiP. Mohamed Abd El-AzizT. SoaresA.G. KandimallaR. TambuwalaM. HassanS.S. AzadG.K. Pal ChoudhuryP. Baetas-da-CruzW. Serrano-ArocaÁ. BrufskyA.M. UhalB.D. The structural basis of accelerated host cell entry by SARS‐CoV‐2.FEBS J.2021288175010502010.1111/febs.1565133264497
     [Google Scholar]
  10. KimC.H. SARS-CoV-2 evolutionary adaptation toward host entry and recognition of receptor O-Acetyl sialylation in virus–host interaction.Int. J. Mol. Sci.20202112454910.3390/ijms2112454932604730
     [Google Scholar]
  11. WuW. ChengY. ZhouH. SunC. ZhangS. The SARS-CoV-2 nucleocapsid protein: Its role in the viral life cycle, structure and functions, and use as a potential target in the development of vaccines and diagnostics.Virol. J.2023201610.1186/s12985‑023‑01968‑636627683
     [Google Scholar]
  12. RajarshiK. KhanR. SinghM.K. RanjanT. RayS. RayS. Essential functional molecules associated with SARS-CoV-2 infection: Potential therapeutic targets for COVID-19.Gene202176814531310.1016/j.gene.2020.14531333220345
     [Google Scholar]
  13. ZhouY. LiQ. PanR. WangQ. ZhuX. YuanC. CaiF. GaoY. CuiY. Regulatory roles of three miRNAs on allergen mRNA expression in Tyrophagus putrescentiae.Allergy202277246948210.1111/all.1511134570913
     [Google Scholar]
  14. ZhouY. LiL. YuZ. GuX. PanR. LiQ. YuanC. CaiF. ZhuY. CuiY. Dermatophagoides pteronyssinus allergen Der p 22: Cloning, expression, IgE ‐binding in asthmatic children, and immunogenicity.Pediatr. Allergy Immunol.2022338e1383510.1111/pai.1383536003049
     [Google Scholar]
  15. HuS. JiangS. QiX. BaiR. YeX.Y. XieT. Races of small molecule clinical trials for the treatment of COVID‐19: An up‐to‐date comprehensive review.Drug Dev. Res.2022831165410.1002/ddr.2189534762760
     [Google Scholar]
  16. LiX. LiangJ. HuJ. MaL. YangJ. ZhangA. JingY. SongY. YangY. FengZ. DuZ. WangY. LuoT. HeW. ShuX. YangS. LiQ. MeiM. LuoS. LiaoK. ZhangY. HeY. HeY. XiaoM. PengB. Screening for primary aldosteronism on and off interfering medications.Endocrine202383117818710.1007/s12020‑023‑03520‑637796417
     [Google Scholar]
  17. LiW. WuJ. ZhangJ. WangJ. XiangD. LuoS. LiJ. LiuX. Puerarin-loaded PEG-PE micelles with enhanced anti-apoptotic effect and better pharmacokinetic profile.Drug Deliv.201825182783710.1080/10717544.2018.145576329587545
     [Google Scholar]
  18. KangL. GaoX.H. LiuH.R. MenX. WuH.N. CuiP.W. OldfieldE. YanJ.Y. Structure–activity relationship investigation of coumarin–chalcone hybrids with diverse side-chains as acetylcholinesterase and butyrylcholinesterase inhibitors.Mol. Divers.201822489390610.1007/s11030‑018‑9839‑y29934672
     [Google Scholar]
  19. WangY. LiH. FanR. LvH. HuaS. XieH. TangT. LuoJ. XiaZ. The effects of ferulic acid on the pharmacokinetics of warfarin in rats after biliary drainage.Drug Des. Devel. Ther.2016102173218010.2147/DDDT.S10791727462142
     [Google Scholar]
  20. ZengM. GuoD. Fernández-VaroG. ZhangX. FuS. JuS. YangH. LiuX. WangY.C. ZengY. CasalsG. CasalsE. The integration of nanomedicine with traditional chinese medicine: Drug delivery of natural products and other opportunities.Mol. Pharm.202320288690410.1021/acs.molpharmaceut.2c0088236563052
     [Google Scholar]
  21. WangK. YinJ. ChenJ. MaJ. SiH. XiaD. Inhibition of inflammation by berberine: Molecular mechanism and network pharmacology analysis.Phytomedicine202412815525810.1016/j.phymed.2023.15525838522318
     [Google Scholar]
  22. HeJ. FengX. LiuY. WangY. GeC. LiuS. JiangY. Graveoline attenuates D-GalN/LPS-induced acute liver injury via inhibition of JAK1/STAT3 signaling pathway.Biomed. Pharmacother.202417711716310.1016/j.biopha.2024.11716339018876
     [Google Scholar]
  23. AloufiB. AlshabrmiF.M. SreeharshaN. RehmanA. Exploring therapeutic targets and drug candidates for obesity: A combined network pharmacology, bioinformatics approach.J. Biomol. Struct. Dyn.202312237811763
     [Google Scholar]
  24. ZhuQ. GaoY. HuQ. HuD. WuX. A study on the factors influencing the intention to receive booster shots of the COVID-19 vaccine in China based on the information frame effect.Front. Public Health202412125818810.3389/fpubh.2024.125818838444439
     [Google Scholar]
  25. ZhangY. RuanL.L. LiM. Palmitic acid impairs human and mouse placental function by inhibiting trophoblast autophagy through induction of acyl-coenzyme A-binding protein (ACBP) upregulation.In: Hum Reprod2024deae091
     [Google Scholar]
  26. LiX.-F. ZhangY.J. YaoY.L. The association of post-embryo transfer SARS-CoV-2 infection with early pregnancy outcomes in in vitro fertilization: A prospective cohort study.Am J Obstet Gynecol20242304436.e1436.e1210.1016/j.ajog.2023.12.022
     [Google Scholar]
  27. RayhanA. Accelerating drug discovery and material design: Unleashing AI's potential for optimizing molecular structures and properties.Preprint2023 https://www.researchgate.net/publication/372960857_Accelerating_Drug_Discovery_and_Material_Design_Unleashing_AI%27s_Potential_for_Optimizing_Molecular_Structures_and_Properties?channel=doi&linkId=64d1ea2e806a9e4e5cf77e88&showFulltext=true
    [Google Scholar]
  28. NaikK. GoyalR.K. FoschiniL. ChakC.W. ThielscherC. ZhuH. LuJ. LehárJ. PacanoswkiM.A. TerranovaN. MehtaN. KorsboN. FakhouriT. LiuQ. GobburuJ. Current status and future directions: The application of artificial intelligence/machine learning for precision medicine.Clin. Pharmacol. Ther.2024115467368610.1002/cpt.315238103204
     [Google Scholar]
  29. RevanthB. The role of artificial intelligence and machine learning in drug discovery and development.Asian J. Adv. Res.202471133140
     [Google Scholar]
  30. LiuT. LinY. WenX. JorissenR.N. GilsonM.K. BindingDB: A web-accessible database of experimentally determined protein-ligand binding affinities.Nucleic Acids Res.200735DatabaseSuppl. 1D198D20110.1093/nar/gkl99917145705
     [Google Scholar]
  31. DraisbachU. NaumannF. DuDe: The duplicate detection toolkit.Proceedings of the international workshop on quality in databases (QDB).Citeseer, 2010.
     [Google Scholar]
  32. BentoA.P. HerseyA. FélixE. LandrumG. GaultonA. AtkinsonF. BellisL.J. De VeijM. LeachA.R. An open source chemical structure curation pipeline using RDKit.J. Cheminform.20201215110.1186/s13321‑020‑00456‑133431044
     [Google Scholar]
  33. Le FevreR.J.W. Molecular refractivity and polarizability.In: Advances in Physical Organic Chemistry.Elsevier1965190
     [Google Scholar]
  34. PrasannaS. DoerksenR. Topological polar surface area: A useful descriptor in 2D-QSAR.Curr. Med. Chem.2009161214110.2174/09298670978700281719149561
     [Google Scholar]
  35. RitchieT.J. MacdonaldS.J.F. YoungR.J. PickettS.D. The impact of aromatic ring count on compound developability: Further insights by examining carbo- and hetero-aromatic and -aliphatic ring types.Drug Discov. Today2011163-416417110.1016/j.drudis.2010.11.01421129497
     [Google Scholar]
  36. WalachJ. FilzmoserP. HronK. Data normalization and scaling: Consequences for the analysis in omics sciences.Comprehensive analytical chemistry.Elsevier2018165196
     [Google Scholar]
  37. PearsR. FinlayJ. ConnorA.M. Synthetic Minority over-sampling technique (SMOTE) for predicting software build outcomes.arXiv:1407.23302014
     [Google Scholar]
  38. PetersonL. K-nearest neighbor.Scholarpedia J.200942188310.4249/scholarpedia.1883
     [Google Scholar]
  39. Jakkula, V. Tutorial on support vector machine (svm).School of EECS, Washington State University200637(2.5)3
     [Google Scholar]
  40. QiY. Random forest for bioinformatics. Ensemble machine learning: Methods and applications.2012307323
     [Google Scholar]
  41. LiangxiaoJ. ZhangH. ZhihuaC. A novel Bayes model: Hidden naive Bayes.IEEE Trans. Knowl. Data Eng.200921101361137110.1109/TKDE.2008.234
     [Google Scholar]
  42. GuelmanL. Gradient boosting trees for auto insurance loss cost modeling and prediction.Expert Syst. Appl.20123933659366710.1016/j.eswa.2011.09.058
     [Google Scholar]
  43. SwetsJ.A.J.S. Measuring the accuracy of diagnostic systems.Science198824048571285129310.1126/science.3287615
     [Google Scholar]
  44. YaoJ. ShepperdM. Assessing software defection prediction performance: Why using the Matthews correlation coefficient matters.Proceedings of the 24th International Conference on Evaluation and Assessment in Software Engineering202010.1145/3383219.3383232
     [Google Scholar]
  45. CarringtonA.M. ManuelD.G. FieguthP.W. Deep ROC analysis and AUC as balanced average accuracy to improve model selection, understanding and interpretation.arXiv:2103.113572021
     [Google Scholar]
  46. ChenX. LiH. TianL. LiQ. LuoJ. ZhangY. Analysis of the physicochemical properties of acaricides based on Lipinski’s rule of five.J. Comput. Biol.20202791397140610.1089/cmb.2019.032332031890
     [Google Scholar]
  47. GuanL. YangH. CaiY. SunL. DiP. LiW. LiuG. TangY. ADMET-score – a comprehensive scoring function for evaluation of chemical drug-likeness.MedChemComm201910114815710.1039/C8MD00472B30774861
     [Google Scholar]
  48. GfellerD. GrosdidierA. WirthM. DainaA. MichielinO. ZoeteV. SwissTargetPrediction: A web server for target prediction of bioactive small molecules.Nucleic Acids Res.201442W1W32W3810.1093/nar/gku29324792161
     [Google Scholar]
  49. KuhnM. von MeringC. CampillosM. JensenL.J. BorkP. STITCH: Interaction networks of chemicals and proteins.Nucleic Acids Res.200836Database issueSuppl. 1D684D68818084021
     [Google Scholar]
  50. BarrettT. WilhiteS.E. LedouxP. EvangelistaC. KimI.F. TomashevskyM. MarshallK.A. PhillippyK.H. ShermanP.M. HolkoM. YefanovA. LeeH. ZhangN. RobertsonC.L. SerovaN. DavisS. SobolevaA. NCBI GEO: Archive for functional genomics data sets-update.Nucleic Acids Res.201341Database issueD991D99523193258
     [Google Scholar]
  51. NiaidN. Database for Annotation, Visualization and Integrated Discovery.DAVID2006
     [Google Scholar]
  52. AlshabrmiF.M. AlkhaylF.F.A. RehmanA.J.E.J.P. Novel drug discovery: Advancing Alzheimer’s therapy through machine learning and network pharmacology.Eur J Pharm2024976176661
     [Google Scholar]
  53. SmootM.E. OnoK. RuscheinskiJ. WangP.L. IdekerT. Cytoscape 2.8: New features for data integration and network visualization.Bioinformatics201127343143210.1093/bioinformatics/btq67521149340
     [Google Scholar]
  54. SzklarczykD. FranceschiniA. WyderS. ForslundK. HellerD. Huerta-CepasJ. SimonovicM. RothA. SantosA. TsafouK.P. KuhnM. BorkP. JensenL.J. von MeringC. STRING v10: Protein–protein interaction networks, integrated over the tree of life.Nucleic Acids Res.201543D1D447D45210.1093/nar/gku100325352553
     [Google Scholar]
  55. TianW. ChenC. LeiX. ZhaoJ. LiangJ. CASTp 3.0: Computed atlas of surface topography of proteins.Nucleic Acids Res.201846W1W363W36710.1093/nar/gky47329860391
     [Google Scholar]
  56. PawarR.P. RohaneS.H. Role of autodock vina in PyRx molecular docking.Asian J. Research Chem.2021142132134
     [Google Scholar]
  57. PettersenE.F. GoddardT.D. HuangC.C. CouchG.S. GreenblattD.M. MengE.C. FerrinT.E. UCSF Chimera—A visualization system for exploratory research and analysis.J. Comput. Chem.200425131605161210.1002/jcc.2008415264254
     [Google Scholar]
  58. Jejurikar BhagyashreeL. and SachinH. Rohane. Drug designing in discovery studio.2021135138
     [Google Scholar]
  59. BergdorfM. Robinson-MosherA. GuoX. LawK.H. ShawD.E. Desmond/GPU performance as of April 2021.DE Shaw Research, Tech. Rep. DESRES/TR–2021-012021
     [Google Scholar]
  60. NoorF. Tahir ul QamarM. AshfaqU.A. AlbuttiA. AlwashmiA.S.S. AljasirM.A. Network pharmacology approach for medicinal plants: Review and assessment.Pharmaceuticals (Basel)202215557210.3390/ph1505057235631398
     [Google Scholar]
  61. ZhangR. ZhuX. BaiH. NingK. Network pharmacology databases for traditional Chinese medicine: Review and assessment.20191012310.3389/fphar.2019.0012330846939
     [Google Scholar]
  62. BatoolS. JavedM.R. AslamS. NoorF. JavedH.M.F. SeemabR. RehmanA. AslamM.F. ParayB.A. GulnazA. Network pharmacology and bioinformatics approach reveals the multi-target pharmacological mechanism of Fumaria indica in the treatment of liver cancer.Pharmaceuticals (Basel)202215665410.3390/ph1506065435745580
     [Google Scholar]
  63. RehmanA. FatimaI. NoorF. Role of small molecules as drug candidates for reprogramming somatic cells into induced pluripotent stem cells: A Comprehensive Review.2024108661
     [Google Scholar]
  64. IslamM.M. SreeharshaN. AlshabrmiF.M. AsifA.H. AldhubiabB. AnwerM.K. KrishnasamyR. RehmanA. From seeds to survival rates: Investigating Linum usitatissimum’s potential against ovarian cancer through network pharmacology.202314128525810.3389/fphar.2023.128525837964873
     [Google Scholar]
  65. RehmanA. NoorF. FatimaI. QasimM. LiaoM. Identification of molecular mechanisms underlying the therapeutic effects of Celosia Cristata on immunoglobulin nephropathy.Comput. Biol. Med.2022151Pt A10629010.1016/j.compbiomed.2022.10629036379189
     [Google Scholar]
  66. NoorF. RehmanA. AshfaqU.A. SaleemM.H. OklaM.K. Al-HashimiA. AbdElgawadH. AslamS. Integrating network pharmacology and molecular docking approaches to decipher the multi-target pharmacological mechanism of Abrus precatorius L. acting on diabetes.Pharmaceuticals (Basel)202215441410.3390/ph1504041435455411
     [Google Scholar]
  67. ShahA. PatelV. JainM. ParmarG. Network Pharmacology and Systems Biology in Drug DiscoveryIn: CADD and Informatics in Drug Discovery231-52.Springer202323125210.1007/978‑981‑99‑1316‑9_10
     [Google Scholar]
  68. Hernández-RodríguezM. Rosales-HernándezM. Mendieta-WejebeJ. Current tools and methods in molecular dynamics (MD) simulations for drug design.Curr Med Chem2016233439093924
     [Google Scholar]
  69. HospitalA. GoñiJ.R. OrozcoM. GelpíJ.L. Molecular dynamics simulations: Advances and applications.Adv. Appl. Bioinform. Chem.20158374726604800
     [Google Scholar]
  70. NaikR.R. ShakyaA.K. AladwanS.M. El-TananiM. Kinase inhibitors as potential therapeutic agents in the treatment of COVID-19. Frontiers in pharmacology.20221380656810.3389/fphar.2022.80656835444538
     [Google Scholar]
  71. LondresH.D. ArmadaJ.J. MartínezA.H. Abdo CuzaA.A. SánchezY.H. RodríguezA.G. FigueroaS.S. Llanez GregorichE.M. Torres LaheraM.L. PeireF.G. GonzálezT.M. GonzálezY.Z. Añé KouriA.L. PalomoA.G. ConcepciónM.T. PérezL.M. Luaces-AlvarezP.L. IglesiasD.E. HernándezD.S. SuzarteM.R. RamosT.C. Blocking EGFR with nimotuzumab: A novel strategy for COVID-19 treatment.Immunotherapy202214752153010.2217/imt‑2022‑002735306855
     [Google Scholar]
/content/journals/cpd/10.2174/0113816128342951241210175314
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Advancing COVID-19 Treatment: The Role of Non-covalent Inhibitors Unveiled by Integrated Machine Learning and Network Pharmacology
Curr. Pharm. Des. 31, 1307 (2025); https://doi.org/10.2174/0113816128342951241210175314
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  • Article Type:     Research Article
Keyword(s): BCL2; COVID-19; EGFR; machine learning; network pharmacology; non-covalent inhibitors

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