Advanced classification of drug-drug interactions for assessing adverse effect risks of Fluvoxamine and Curcumin using deep learning in COVID-19

Maryam Abdollahi Shamami ,Helia Farahzadi ,Leila Amini ,Mohsen Asghari Ilani ,Yaser Mike Banad

doi:10.24294/jipd9734

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Advanced classification of drug-drug interactions for assessing adverse effect risks of Fluvoxamine and Curcumin using deep learning in COVID-19

Maryam Abdollahi Shamami

Helia Farahzadi

Leila Amini

Mohsen Asghari Ilani

Yaser Mike Banad

Journal of Infrastructure Policy and Development 2024, 8(15), 9734; https://doi.org/10.24294/jipd9734

Submitted：18 Oct 2024

Accepted：18 Oct 2024

Published：17 Dec 2024

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Abstract

Accurate drug-drug interaction (DDI) prediction is essential to prevent adverse effects, especially with the increased use of multiple medications during the COVID-19 pandemic. Traditional machine learning methods often miss the complex relationships necessary for effective DDI prediction. This study introduces a deep learning-based classification framework to assess adverse effects from interactions between Fluvoxamine and Curcumin. Our model integrates a wide range of drug-related data (e.g., molecular structures, targets, side effects) and synthesizes them into high-level features through a specialized deep neural network (DNN). This approach significantly outperforms traditional classifiers in accuracy, precision, recall, and F1-score. Additionally, our framework enables real-time DDI monitoring, which is particularly valuable in COVID-19 patient care. The model’s success in accurately predicting adverse effects demonstrates the potential of deep learning to enhance drug safety and support personalized medicine, paving the way for safer, data-driven treatment strategies.

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