DeepInsulin-Net: A Deep Learning Model for Identifying Drug Interactions Leading to Specific Insulin-Related Adverse Events

Muhammed Ali Pala

doi:10.35377/saucis...1646658

EN

DeepInsulin-Net: A Deep Learning Model for Identifying Drug Interactions Leading to Specific Insulin-Related Adverse Events

Abstract

Predicting clinical adverse effects resulting from drug-drug interactions is a critical research area for drug safety and patient health. Specifically, predicting adverse effects associated with insulin is crucial for clinical decision support systems and pharmacovigilance applications. This study proposes a deep learning-based model with high accuracy to predict adverse effects caused by drug interactions. In the literature, 17 different clinical side effects commonly associated with the hormone insulin have been identified. The properties of the drug molecules causing these interactions were calculated through MACCS, Morgan fingerprints and RDKit descriptors. These features are filtered by the variance thresholding method and optimized to improve classification performance. The model is built on a 1D CNN architecture that handles drug pairs as parallel inputs and a class weighting technique is used to eliminate class imbalance. Experimental results show that the model achieves 99.66% accuracy in training and 94.03% in validation, with training loss decreasing to 0.01 and validation loss stabilizing at 0.22. The ROC-AUC metric is above 0.99, indicating that the model can predict infrequent adverse events. The developed model provides a scalable, computationally efficient and highly reliable approach to predict the clinical consequences of drug interactions.

Keywords

Supporting Institution

Author declare that this study received no specific funding or external support.

Ethical Statement

It is declared that during the preparation process of this study, scientific and ethical principles were followed, and all the studies benefited from are stated in the bibliography.

References

A. Agustí, E. Melén, D. L. DeMeo, R. Breyer-Kohansal, and R. Faner, “Pathogenesis of chronic obstructive pulmonary disease: understanding the contributions of gene–environment interactions across the lifespan,” lancet Respir. Med., vol. 10, no. 5, pp. 512–524, 2022.
S. R. Chowdhury, D. C. Das, T. C. Sunna, J. Beyene, and A. Hossain, “Global and regional prevalence of multimorbidity in the adult population in community settings: a systematic review and meta-analysis,” EClinicalMedicine, vol. 57, 2023.
R. W. Hoel, R. M. G. Connolly, and P. Y. Takahashi, “Polypharmacy management in older patients,” in Mayo Clinic Proceedings, Elsevier, 2021, pp. 242–256.
F. Pazan and M. Wehling, “Polypharmacy in older adults: a narrative review of definitions, epidemiology and consequences,” Eur. Geriatr. Med., vol. 12, pp. 443–452, 2021.
J. Wolff et al., “Polypharmacy and the risk of drug–drug interactions and potentially inappropriate medications in hospital psychiatry,” Pharmacoepidemiol. Drug Saf., vol. 30, no. 9, pp. 1258–1268, 2021.
M. R. Mohamed et al., “Association of polypharmacy and potential drug‐drug interactions with adverse treatment outcomes in older adults with advanced cancer,” Cancer, vol. 129, no. 7, pp. 1096–1104, 2023.
M. Zitnik, M. Agrawal, and J. Leskovec, “Modeling polypharmacy side effects with graph convolutional networks,” Bioinformatics, vol. 34, no. 13, pp. i457–i466, 2018.
H. Askr, E. Elgeldawi, H. Aboul Ella, Y. A. M. M. Elshaier, M. M. Gomaa, and A. E. Hassanien, “Deep learning in drug discovery: an integrative review and future challenges,” Artif. Intell. Rev., vol. 56, no. 7, pp. 5975–6037, 2023.

M. F. Rasool et al., “Assessment of risk factors associated with potential drug-drug interactions among patients suffering from chronic disorders,” PLoS One, vol. 18, no. 1, p. e0276277, 2023.
P. Rorsman and E. Renström, “Insulin granule dynamics in pancreatic beta cells,” Diabetologia, vol. 46, pp. 1029–1045, 2003.
N. J. Hogrebe, K. G. Maxwell, P. Augsornworawat, and J. R. Millman, “Generation of insulin-producing pancreatic β cells from multiple human stem cell lines,” Nat. Protoc., vol. 16, no. 9, pp. 4109–4143, 2021.
M. A. Hill et al., “Insulin resistance, cardiovascular stiffening and cardiovascular disease,” Metabolism, vol. 119, p. 154766, 2021.
M. S. Rahman et al., “Role of insulin in health and disease: an update,” Int. J. Mol. Sci., vol. 22, no. 12, p. 6403, 2021.
A. Kleinridders, H. A. Ferris, W. Cai, and C. R. Kahn, “Insulin action in brain regulates systemic metabolism and brain function,” Diabetes, vol. 63, no. 7, pp. 2232–2243, 2014.
J. J. Holst, L. S. Gasbjerg, and M. M. Rosenkilde, “The role of incretins on insulin function and glucose homeostasis,” Endocrinology, vol. 162, no. 7, p. bqab065, 2021.
J. Blahova, M. Martiniakova, M. Babikova, V. Kovacova, V. Mondockova, and R. Omelka, “Pharmaceutical drugs and natural therapeutic products for the treatment of type 2 diabetes mellitus,” Pharmaceuticals, vol. 14, no. 8, p. 806, 2021.
R. A. Haeusler, T. E. McGraw, and D. Accili, “Biochemical and cellular properties of insulin receptor signalling,” Nat. Rev. Mol. cell Biol., vol. 19, no. 1, pp. 31–44, 2018.
G. Wilcox, “Insulin and insulin resistance,” Clin. Biochem. Rev., vol. 26, no. 2, p. 19, 2005.
D. Soumya and B. Srilatha, “Late stage complications of diabetes and insulin resistance,” J Diabetes Metab, vol. 2, no. 9, p. 1000167, 2011.
O. I. Aruoma, V. S. Neergheen, T. Bahorun, and L.-S. Jen, “Free radicals, antioxidants and diabetes: embryopathy, retinopathy, neuropathy, nephropathy and cardiovascular complications,” Neuroembryology Aging, vol. 4, no. 3, pp. 117–137, 2007.
C. Argano, L. Mirarchi, S. Amodeo, V. Orlando, A. Torres, and S. Corrao, “The role of vitamin D and its molecular bases in insulin resistance, diabetes, metabolic syndrome, and cardiovascular disease: state of the art,” Int. J. Mol. Sci., vol. 24, no. 20, p. 15485, 2023.
J.-M. Guettier and P. Gorden, “Hypoglycemia,” Endocrinol. Metab. Clin., vol. 35, no. 4, pp. 753–766, 2006.
L. Sacchetta et al., “Synergistic effect of chronic kidney disease, neuropathy, and retinopathy on all-cause mortality in type 1 and type 2 diabetes: a 21-year longitudinal study,” Cardiovasc. Diabetol., vol. 21, no. 1, p. 233, 2022.
B. T. Alemu, O. Olayinka, H. A. Baydoun, M. Hoch, and M. Akpinar-Elci, “Neonatal hypoglycemia in diabetic mothers: A systematic review,” Curr. Pediatr. Res., vol. 21, no. 1, 2017.
T. Zhang, J. Leng, and Y. Liu, “Deep learning for drug–drug interaction extraction from the literature: a review,” Brief. Bioinform., vol. 21, no. 5, pp. 1609–1627, 2020.
Y. Deng, X. Xu, Y. Qiu, J. Xia, W. Zhang, and S. Liu, “A multimodal deep learning framework for predicting drug–drug interaction events,” Bioinformatics, vol. 36, no. 15, pp. 4316–4322, 2020.
K. Han et al., “A review of approaches for predicting drug–drug interactions based on machine learning,” Front. Pharmacol., vol. 12, p. 814858, 2022.
N.-N. Wang, B. Zhu, X.-L. Li, S. Liu, J.-Y. Shi, and D.-S. Cao, “Comprehensive Review of Drug–Drug Interaction Prediction Based on Machine Learning: Current Status, Challenges, and Opportunities,” J. Chem. Inf. Model., vol. 64, no. 1, pp. 96–109, 2023.
A. Akgul, Y. Karaca, M. A. Pala, M. E. Çimen, A. F. Boz, and M. Z. Yildiz, “Chaos Theory, Advanced Metaheuristic Algorithms And Their Newfangled Deep Learning Architecture Optimization Applications: A Review,” Fractals, vol. 32, no. 03, p. 2430001, 2024.
M. A. Pala and M. Z. Yıldız, “Improving cellular analysis throughput of lens-free holographic microscopy with circular Hough transform and convolutional neural networks,” Opt. Laser Technol., vol. 176, p. 110920, 2024.
M. A. Pala, “Graph-Aware AURALSTM: An Attentive Unified Representation Architecture with BiLSTM for Enhanced Molecular Property Prediction,” Mol. Divers., Apr. 2025, doi: 10.1007/s11030-025-11197-4.
X. Li, Z. Xiong, W. Zhang, and S. Liu, “Deep learning for drug‐drug interaction prediction: A comprehensive review,” Quant. Biol., vol. 12, no. 1, pp. 30–52, 2024.
E. Kim and H. Nam, “DeSIDE-DDI: interpretable prediction of drug-drug interactions using drug-induced gene expressions,” J. Cheminform., vol. 14, no. 1, p. 9, 2022.
Z. Li, X. Tu, Y. Chen, and W. Lin, “HetDDI: a pre-trained heterogeneous graph neural network model for drug–drug interaction prediction,” Brief. Bioinform., vol. 24, no. 6, p. bbad385, Nov. 2023, doi: 10.1093/bib/bbad385.
M. Asfand-E-Yar, Q. Hashir, A. A. Shah, H. A. M. Malik, A. Alourani, and W. Khalil, “Multimodal cnn-ddi: using multimodal cnn for drug to drug interaction associated events,” Sci. Rep., vol. 14, no. 1, p. 4076, 2024.
N. P. Tatonetti, P. P. Ye, R. Daneshjou, and R. B. Altman, “Data-driven prediction of drug effects and interactions.,” Sci. Transl. Med., vol. 4, no. 125, p. 125ra31, Mar. 2012, doi: 10.1126/scitranslmed.3003377.
A. Cereto-Massagué, M. J. Ojeda, C. Valls, M. Mulero, S. Garcia-Vallvé, and G. Pujadas, “Molecular fingerprint similarity search in virtual screening,” Methods, vol. 71, pp. 58–63, 2015.
S. Zhong and X. Guan, “Count-based morgan fingerprint: A more efficient and interpretable molecular representation in developing machine learning-based predictive regression models for water contaminants’ activities and properties,” Environ. Sci. Technol., vol. 57, no. 46, pp. 18193–18202, 2023.
G. Landrum et al., “rdkit/rdkit: 2024\_09\_5 (Q3 2024) Release.” Zenodo, 2025. doi: 10.5281/zenodo.14779836.
X. Ying, “An overview of overfitting and its solutions,” in Journal of physics: Conference series, IOP Publishing, 2019, p. 22022.
M. A. F. A. Fida, T. Ahmad, and M. Ntahobari, “Variance threshold as early screening to Boruta feature selection for intrusion detection system,” in 2021 13th International Conference on Information & Communication Technology and System (ICTS), IEEE, 2021, pp. 46–50.
M. A. PALA, M. E. ÇİMEN, M. Z. YILDIZ, G. ÇETİNEL, E. AVCIOĞLU, and Y. ALACA, “CNN-Based Approach for Overlapping Erythrocyte Counting and Cell Type Classification in Peripheral Blood Images,” Chaos Theory Appl., 2022, doi: 10.51537/chaos.1114878.

Details

Primary Language

English

Subjects

Software Engineering (Other)

Journal Section

Research Article

Authors

Muhammed Ali Pala ^*
0000-0002-8153-7971
Türkiye

Early Pub Date

June 16, 2025

Publication Date

June 30, 2025

Submission Date

February 25, 2025

Acceptance Date

June 11, 2025

Published in Issue

Year 2025 Volume: 8 Number: 2

DOI

https://doi.org/10.35377/saucis...1646658

IZ

https://izlik.org/JA89MP93TL

Cite

RIS / Bibtex

APA

Pala, M. A. (2025). DeepInsulin-Net: A Deep Learning Model for Identifying Drug Interactions Leading to Specific Insulin-Related Adverse Events. Sakarya University Journal of Computer and Information Sciences, 8(2), 245-259. https://doi.org/10.35377/saucis...1646658

AMA

1.Pala MA. DeepInsulin-Net: A Deep Learning Model for Identifying Drug Interactions Leading to Specific Insulin-Related Adverse Events. SAUCIS. 2025;8(2):245-259. doi:10.35377/saucis.1646658

Chicago

Pala, Muhammed Ali. 2025. “DeepInsulin-Net: A Deep Learning Model for Identifying Drug Interactions Leading to Specific Insulin-Related Adverse Events”. Sakarya University Journal of Computer and Information Sciences 8 (2): 245-59. https://doi.org/10.35377/saucis. 1646658.

EndNote

Pala MA (June 1, 2025) DeepInsulin-Net: A Deep Learning Model for Identifying Drug Interactions Leading to Specific Insulin-Related Adverse Events. Sakarya University Journal of Computer and Information Sciences 8 2 245–259.

IEEE

[1]M. A. Pala, “DeepInsulin-Net: A Deep Learning Model for Identifying Drug Interactions Leading to Specific Insulin-Related Adverse Events”, SAUCIS, vol. 8, no. 2, pp. 245–259, June 2025, doi: 10.35377/saucis...1646658.

ISNAD

Pala, Muhammed Ali. “DeepInsulin-Net: A Deep Learning Model for Identifying Drug Interactions Leading to Specific Insulin-Related Adverse Events”. Sakarya University Journal of Computer and Information Sciences 8/2 (June 1, 2025): 245-259. https://doi.org/10.35377/saucis. 1646658.

JAMA

1.Pala MA. DeepInsulin-Net: A Deep Learning Model for Identifying Drug Interactions Leading to Specific Insulin-Related Adverse Events. SAUCIS. 2025;8:245–259.

MLA

Pala, Muhammed Ali. “DeepInsulin-Net: A Deep Learning Model for Identifying Drug Interactions Leading to Specific Insulin-Related Adverse Events”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 2, June 2025, pp. 245-59, doi:10.35377/saucis. 1646658.

Vancouver

1.Muhammed Ali Pala. DeepInsulin-Net: A Deep Learning Model for Identifying Drug Interactions Leading to Specific Insulin-Related Adverse Events. SAUCIS. 2025 Jun. 1;8(2):245-59. doi:10.35377/saucis. 1646658