A comprehensive survey explores Drug-Drug interaction prediction using Machine-Learning techniques
radwan, Y., gouda, K., Abdelbaky, I., arafa, M. (2024). A comprehensive survey explores Drug-Drug interaction prediction using Machine-Learning techniques. EKB Journal Management System, 9(5), 13-21. doi: 10.21608/bjas.2024.274193.1343
yasmin radwan; karam abdelghany gouda; Ibrahim Zaghloul Abdelbaky; mona mohamed arafa. "A comprehensive survey explores Drug-Drug interaction prediction using Machine-Learning techniques". EKB Journal Management System, 9, 5, 2024, 13-21. doi: 10.21608/bjas.2024.274193.1343
radwan, Y., gouda, K., Abdelbaky, I., arafa, M. (2024). 'A comprehensive survey explores Drug-Drug interaction prediction using Machine-Learning techniques', EKB Journal Management System, 9(5), pp. 13-21. doi: 10.21608/bjas.2024.274193.1343
radwan, Y., gouda, K., Abdelbaky, I., arafa, M. A comprehensive survey explores Drug-Drug interaction prediction using Machine-Learning techniques. EKB Journal Management System, 2024; 9(5): 13-21. doi: 10.21608/bjas.2024.274193.1343

A comprehensive survey explores Drug-Drug interaction prediction using Machine-Learning techniques

Benha Journal of Applied Sciences
Article 2, Volume 9, Issue 5, May 2024, Page 13-21  XML PDF (409.34 K)
Document Type: Original Research Papers
DOI: 10.21608/bjas.2024.274193.1343
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Authors
yasmin radwan email 1; karam abdelghany gouda2; Ibrahim Zaghloul Abdelbakyorcid 1; mona mohamed arafa1
1faculty of Computers and Artificial Intelligence banha univeristy
2Faculty of Computers and Artificial Intelligence, Benha University
Abstract
Drug-Drug Interactions is a critical health and safety concern that receives a lot of attention from both academia and business. Polypharmacy is often employed as a strategy to manage complex diseases such as cancer, diabetes, and age-related ailments. However, combining medications with other drugs can lead to unintended adverse reactions. Interactions between drugs may increase the chance of unanticipated negative effects and even unknown toxicity, putting patients at risk. Detecting and identifying Interactions not only helps clinicians avoid chronic but will also encourage the co-prescription of safe drugs for more effective therapies. It is expensive and time-consuming to identify drug-drug interactions and Adverse Reactions among several medication pairings, both in vivo and in vitro. Recent advancements in computer science, specifically in the field of Artificial Intelligence, have yielded techniques that enable researchers to identify drug-drug interactions. We present comprehensive approaches that enable in-depth analysis of potential interactions by taking into account various factors, including molecular structure, clinical data, network relationships, and existing literature. This paper offers an all-encompassing survey of research studies that utilize Machine Learning and Deep Learning algorithms for the prediction of Drug-Drug interactions.
Keywords
Drug-Drug interactions; Adverse Reactions; artificial intelligence
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