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.

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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 1970 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