Published 02-12-2023
Keywords
- deep learning,
- drug interaction prediction
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
Abstract
This paper discusses the application of deep learning models for predicting drug interactions, a critical aspect of personalized medicine. As personalized healthcare advances, the ability to predict how different drugs interact with one another becomes vital in designing optimal treatment regimens. Adverse drug reactions (ADRs) and drug-drug interactions (DDIs) pose significant risks to patients, particularly those undergoing complex therapies involving multiple medications. Deep learning methods, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), provide tools for analyzing complex biomedical data and predicting these interactions with high accuracy. This paper examines various deep learning techniques employed in drug interaction prediction, focusing on their potential to improve patient outcomes by minimizing adverse effects. It also discusses challenges, such as data heterogeneity and model interpretability, and explores future directions in this evolving field.
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