Skip to main content
SpringerLink
Log in

Hybrid deep learning technique for COX-2 inhibition bioactivity detection against breast cancer disease

  • Original Article
  • Published:
Biomedical Engineering Letters Aims and scope Submit manuscript

Abstract

This study addresses detecting COX-2 inhibition in breast cancer, targeting its role in tumor growth. The primary goal is to develop an efficient technique for precise COX-2 inhibition bioactivity detection, with implications for identifying anti-cancer compounds and advancing breast cancer therapies. The proposed methodology uses the UNet architecture for feature extraction, enhancing accuracy. A modified chicken swarm optimization (MCSO) algorithm addresses data dimensionality, optimizing features. An improved Laguerre neural network (ILNN) classifies COX-2 inhibition bioactivity. Validation is performed using the ChEMBL database. The research evaluates the accuracy, precision, recall, F-measure, Matthews' correlation coefficient (MCC), and Dice coefficient of the proposed method. These metrics are compared against those of contemporary methods to assess the efficiency and effectiveness of the developed technique. The study underscores the hybrid deep learning method's significance in accurately detecting COX-2 inhibition bioactivity against breast cancer. Results highlight its potential as a valuable tool in breast cancer drug discovery.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Algorithm 1
Algorithm 2
Algorithm 3
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Aibe N, Karasawa K, Aoki M, Akahane K, Ogawa Y, Ogo E, Kanamori S, Kawamori J, Saito AI, Shiraishi K, Sekine H. Results of a nationwide survey on Japanese clinical practice in breast-conserving radiotherapy for breast cancer. J Radiat Res. 2019;60(1):142–9.

    Article  Google Scholar 

  2. Wu N, Phang J, Park J, Shen Y, Huang Z, Zorin M, Jastrzębski S, Févry T, Katsnelson J, Kim E, Wolfson S. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans Med Imaging. 2019;39(4):1184–94.

    Article  Google Scholar 

  3. Kalathiya U, Padariya M, Baginski M. Molecular modeling and evaluation of novel dibenzopyrrole derivatives as telomerase inhibitors and potential drug for cancer therapy. IEEE/ACM Trans Comput Biol Bioinf. 2014;11(6):1196–207.

    Article  Google Scholar 

  4. Sengupta S, Bandyopadhyay S. De novo design of potential reca inhibitors using multiobjective optimization. IEEE/ACM Trans Comput Biol Bioinf. 2012;9(4):1139–54.

    Article  Google Scholar 

  5. Seniya C, Yadav A, Khan GJ, Sah NK. In-silico studies show potent inhibition of HIV-1 reverse transcriptase activity by a herbal drug. IEEE/ACM Trans Comput Biol Bioinf. 2015;12(6):1355–64.

    Article  Google Scholar 

  6. Javadi A, Keighobadi F, Nekoukar V, Ebrahimi M. Finite-set model predictive control of melanoma cancer treatment using signaling pathway inhibitor of cancer stem cell. IEEE/ACM Trans Comput Biol Bioinf. 2019;18(4):1504–11.

    Article  Google Scholar 

  7. Kadhim M, Thacker M, Kadhim A, Holmes L Jr. Treatment of unicameral bone cyst: systematic review and meta analysis. J Child Orthop. 2014;8(2):171–91.

    Article  Google Scholar 

  8. Duan W, Xu Y, Dong Y, Cao L, Tong J, Zhou X. Ectopic expression of miR-34a enhances radiosensitivity of non-small cell lung cancer cells, partly by suppressing the LyGDI signaling pathway. J Radiat Res. 2013;54(4):611–9.

    Article  Google Scholar 

  9. Rodler D, Sinowatz F. Expression of prostaglandin synthesizing enzymes (cyclooxygenase 1 and cyclooxygenase 2) in the ovary of the ostrich (Struthio camelus). Acta Histochem. 2015;117(1):69–75.

    Article  Google Scholar 

  10. Xu HB, Shen FM, Lv QZ. Celecoxib enhanced the cytotoxic effect of cisplatin in drug-resistant human gastric cancer cells by inhibition of cyclooxygenase-2. Eur J Pharmacol. 2015;769:1–7.

    Article  Google Scholar 

  11. Nile SH, Ko EY, Kim DH, Keum YS. Screening of ferulic acid related compounds as inhibitors of xanthine oxidase and cyclooxygenase-2 with anti-inflammatory activity. RevistaBrasileira de Farmacognosia. 2016;26:50–5.

    Article  Google Scholar 

  12. Nørregaard R, Kwon TH, Frøkiær J. Physiology and pathophysiology of cyclooxygenase-2 and prostaglandin E2 in the kidney. Kidney Res Clinic Pract. 2015;34(4):194–200.

    Article  Google Scholar 

  13. Wildenhain J, Spitzer M, Dolma S, Jarvik N, White R, Roy M, Griffiths E, Bellows DS, Wright GD, Tyers M. Prediction of synergism from chemical-genetic interactions by machine learning. Cell Syst. 2015;1(6):383–95.

    Article  Google Scholar 

  14. Deeb SJ, Tyanova S, Hummel M, Schmidt-Supprian M, Cox J, Mann M. Machine learning-based classification of diffuse large B-cell lymphoma patients by their protein expression profiles. Mol Cell Proteomics. 2015;14(11):2947–60.

    Article  Google Scholar 

  15. Wassermann AM, Lounkine E, Davies JW, Glick M, Camargo LM. The opportunities of mining historical and collective data in drug discovery. Drug Discov Today. 2015;20(4):422–34.

    Article  Google Scholar 

  16. Ngo TD, Tran TD, Le MT, Thai KM. Computational predictive models for P-glycoprotein inhibition of in-house chalcone derivatives and drug-bank compounds. Mol Divers. 2016;20:945–61.

    Article  Google Scholar 

  17. Koutsoukas A, Monaghan KJ, Li X, Huan J. Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data. J Cheminform. 2017;9(1):1–13.

    Article  Google Scholar 

  18. Ali M, Aittokallio T. Machine learning and feature selection for drug response prediction in precision oncology applications. Biophys Rev. 2019;11(1):31–9.

    Article  Google Scholar 

  19. Bolgár B, Antal P. VB-MK-LMF: fusion of drugs, targets and interactions using variational Bayesian multiple kernel logistic matrix factorization. BMC Bioinformatics. 2017;18(1):1–18.

    Article  Google Scholar 

  20. Scheeder C, Heigwer F, Boutros M. Machine learning and image-based profiling in drug discovery. Curr Opin Syst Biol. 2018;10:43–52.

    Article  Google Scholar 

  21. Qin Z, Xi Y, Zhang S, Tu G, Yan A. Classification of cyclooxygenase-2 inhibitors using support vector machine and random forest methods. J Chem Inf Model. 2019;59(5):1988–2008.

    Article  Google Scholar 

  22. Ruano-Ordás D, Burggraaff L, Liu R, van der Horst C, Heitman LH, Emmerich MT, Mendez JR, Yevseyeva I, van Westen GJ. A multiple classifier system identifies novel cannabinoid CB2 receptor ligands. J Cheminform. 2019;11:1–14.

    Article  Google Scholar 

  23. Elbadawi M, Gaisford S, Basit AW. Advanced machine-learning techniques in drug discovery. Drug Discov Today. 2021;26(3):769–77.

    Article  Google Scholar 

  24. Raschka S, Kaufman B. Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition. Methods. 2020;180:89–110.

    Article  Google Scholar 

  25. Bian Y, Xie XQ. Generative chemistry: drug discovery with deep learning generative models. J Mol Model. 2021;27:1–18.

    Article  Google Scholar 

  26. Periwal V, Bassler S, Andrejev S, Gabrielli N, Patil KR, Typas A, Patil KR. Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs. PLoS Comput Biol. 2022;18(4):e1010029.

    Article  Google Scholar 

  27. Santana MV, Silva-Jr FP. De novo design and bioactivity prediction of SARS-CoV-2 main protease inhibitors using recurrent neural network-based transfer learning. BMC Chem. 2021;15(1):8.

    Article  Google Scholar 

  28. Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers. 2021;25:1315–60.

    Article  Google Scholar 

  29. Hentabli H, Bengherbia B, Saeed F, Salim N, Nafea I, Toubal A, Nasser M. Convolutional neural network model based on 2D fingerprint for bioactivity prediction. Int J Mol Sci. 2022;23(21):13230.

    Article  Google Scholar 

  30. An T, Chen Y, Chen Y, Ma L, Wang J, Zhao J. A machine learning-based approach to ERα bioactivity and drug ADMET prediction. Front Genet. 2022;13:1087273.

    Article  Google Scholar 

  31. Dibia KT, Igbokwe PK, Ezemagu GI, Asadu CO. Exploration of the quantitative structure-activity relationships for predicting cyclooxygenase-2 inhibition bioactivity by machine learning approaches. Res Chem. 2022;4:100272.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computational Sciences, Swami Ramanand Teerth, Marathvada University, Nanded, India

    Sahebrao B. Pawar, N. K. Deshmukh & Sharad B. Jadhav

Authors
  1. Sahebrao B. Pawar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. K. Deshmukh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sharad B. Jadhav
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sahebrao B. Pawar.

Ethics declarations

Conflict of interest

I have no conflict of interest.

Ethics approval

This article does not contain any studies with human or animals subjects performed by any of the authors.

Consent to participate

Not Applicable.

Consent for publication

Not Applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pawar, S.B., Deshmukh, N.K. & Jadhav, S.B. Hybrid deep learning technique for COX-2 inhibition bioactivity detection against breast cancer disease. Biomed. Eng. Lett. (2024). https://doi.org/10.1007/s13534-024-00355-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13534-024-00355-6

Keywords

Search

Navigation