Genome-Based Drug Repurposing: Identifying Potential Targets Using FASTA Sequences and Machine Learning

Author(s)	Mr. Ahmed Abdallah Alshahab, Dr. Vaishali A. Chavan
Country	India
Abstract	This study uses genomic data to explore machine learning techniques for drug repurposing in viral diseases. This research aims to develop classification models that utilize FASTA protein sequence data to find similar genomes and, based on identified similar genome could help in drug repurposing for viral diseases. To develop this model, we explored three machine learning models Decision Tree, Random Forest , and K-Nearest Neighbours. These models were implemented and assessed. The study used genetic information from seven viral disease families obtained from the National Center for Biotechnology Information (NCBI). Data preprocessing involved cleaning and encoding the FASTA protein sequences. These three models were implemented to identify similar genomes for targeted viral disease, and tested targeted HMPV Viral disease on all three models and found Rotavirus as the closest match. The Random Forest model shows the best performance with an accuracy of 98.79\% and F1 Score of 0.988.
Keywords	Machine Learning, Genome Predictor, FASTA sequence, Drug Repurposing
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 16, Issue 4, October-December 2025
Published On	2025-12-31
DOI	https://doi.org/10.71097/IJSAT.v16.i4.10034
Short DOI	https://doi.org/hbjmqd

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