<![CDATA[Despite substantial pharmaceutical investments of approximately $50 billion annually, modern drug discovery yields only 20-25 new approvals, with traditional development requiring 12-15 years and success rates below 10%. Contemporary challenges, including high clinical failure rates, prolonged timelines, and limited preclinical predictive capacity, represent the current therapeutic debacle of de novo drug development. To address this critical scenario, drug repurposing is an appealing strategy for identifying novel therapeutic applications from existing approved drugs. However, traditional repurposing relies on serendipitous observations or resource-intensive screenings. In contrast, in silico drug repurposing is an emerging, hypothesis-driven approach leveraging big data, artificial intelligence, machine learning, multi-omics analysis, and network pharmacology to predict drug-target interactions and therapeutic efficacy cost-effectively. Additionally, repurposing approaches, including in silico techniques, reduce development timelines to 3-12 years with enhanced success rates of approximately 25%, with 30% of FDA-approved drugs originating from repurposing initiatives. Therefore, computational drug repurposing substantially improves therapeutic development efficiency while requiring rigorous experimental validation for clinical translation. Here, we will review in silico methodologies exploited for drug repurposing across oncology, infectious diseases, neurodegenerative disorders, metabolic disorders, and pandemic threats, alongside computational pharmacology assessment tools to address how the implementation of current in silico options can accelerate the robust drug repurposing opportunities.]]>
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