AI-Assisted Drug Repurposing Pipelines for Viral Diseases
DOI:
https://doi.org/10.15662/IJRAI.2020.0303001Keywords:
AI-assisted drug repurposing, Viral diseases, Machine learning, Deep learning, Network-based approaches Drug discovery, COVID-19Abstract
The rapid emergence of novel viral infections, such as COVID-19, has underscored the urgent need for expedited therapeutic interventions. Traditional drug discovery processes are time-consuming and costly, often taking over a decade and billions of dollars to develop new drugs. In contrast, drug repurposing offers a promising alternative by identifying existing, approved drugs that can be repurposed for new therapeutic indications. Artificial Intelligence (AI) has emerged as a pivotal tool in this domain, facilitating the identification of potential drug candidates through advanced computational methods. AI-assisted drug repurposing pipelines leverage various techniques, including machine learning, deep learning, and network-based approaches, to analyze vast biological datasets. These pipelines integrate data from multiple sources, such as gene expression profiles, protein-protein interaction networks, and drug-target databases, to predict novel drug-disease associations. By employing algorithms that can process and interpret complex biological data, AI models can identify potential therapeutic candidates more efficiently than traditional methods. In the context of viral diseases, AI has been utilized to predict existing drugs that may inhibit viral replication or modulate host immune responses. For instance, during the early stages of the COVID-19 pandemic, AI models were employed to identify potential treatments by analyzing the virus's genetic sequence and comparing it with existing drug databases. Such approaches have led to the identification of promising candidates that are currently undergoing clinical trials. This paper provides an overview of AI-assisted drug repurposing pipelines, highlighting their methodologies, applications in viral diseases, and the challenges associated with their implementation. By examining case studies and recent advancements, we aim to underscore the potential of AI in accelerating the development of therapeutic agents for viral infections.
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