Deep Learning in Drug Repurposing: A Review of the CoV-DrugX Module within the CoV-DrugX Pipeline

A comprehensive overview of the integration of deep learning techniques in drug repurposing, particularly focusing on the CoV-DrugX module within the CoV-DrugX Pipeline. The paper highlights the significance of drug repurposing in accelerating treatment discovery, especially in the context of the COVID-19 pandemic. It discusses the emergence of deep learning methods, such as recurrent neural networks (RNNs), graph convolutional networks (GCNs), and long short-term memory (LSTM) networks, in predicting drug-target interactions and identifying repurposable drugs. The review emphasizes the role of deep learning in extracting informative features, improving drug discovery, enhancing drug repositioning, and handling large-scale data effectively. Additionally, it explores the applications and advantages of deep learning in drug repurposing, showcasing its potential to revolutionize the field by learning complex relationships from extensive datasets. The abstract sets the stage for a detailed examination of the DrugX module's capabilities within the CoV-DrugX Pipeline, shedding light on its contributions to drug discovery and repurposing efforts, particularly in the fight against COVID-19.