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@article{190850,
        author = {Ahmed Alshahab and Dr. Vaishali A. Chavan},
        title = {A Lightweight Web Application for Genome-Based Drug Repurposing Using Machine Learning Models},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {4804-4807},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190850},
        abstract = {Drug repurposing reduces the cost, time, and risk associated with conventional drug discovery by identifying new therapeutic uses for existing drugs. With the rapid growth of genomic data, machine learning models provide effective tools for genome similarity analysis and drug target identification. This paper presents a lightweight web-based application for genome- based drug repurposing that predicts similar viral genomes using a Random Forest machine learning model and recommends associated antiviral drug molecules. The system accepts viral genome FASTA protein sequences, validates input data, performs real-time prediction via a FastAPI backend, and presents results through a ReactJS frontend. Approved and experimental drug references are linked to PubChem for validation. Experimental evaluation demonstrates accurate genome similarity prediction with low latency, highlighting the feasibility of deploying ma- chine learning-driven bioinformatics solutions as accessible web applications.},
        keywords = {Drug Repurposing, Genomics, Machine Learn- ing, Random Forest, FASTA, Web Application, Bioinformatics},
        month = {January},
        }