Pattern Recognition Algorithms in Pharmacogenomics and Drug Repurposing-Case Study: Ribavirin and Lopinavir

Pattern recognition and machine learning algorithms have become integral to modern drug discovery, offering powerful tools to uncover complex patterns in biomedical data. This article provides a comprehensive review of state-of-the-art pattern recognition techniques-including traditional machine learning (e.g., support vector machines), deep learning approaches, genome-wide association studies (GWAS), and biomarker discovery methods-as applied in pharmacogenomics and computational drug repurposing. We discuss how these methods facilitate the identification of genetic factors that influence drug response, as well as the in silico screening of existing drugs for new therapeutic uses. Two antiviral agents, ribavirin and lopinavir, are examined as extended case studies in the context of COVID-19, illustrating practical applications of pattern recognition algorithms in analyzing pharmacogenomic data and guiding drug repurposing efforts during a pandemic. We highlight successful approaches such as the machine learning-driven prediction of responders and the AI-assisted identification of repurposed drugs (exemplified by the case of baricitinib for COVID-19), alongside current limitations, including data scarcity, model interpretability, and translational gaps. Finally, we outline future directions for integrating multi-omics data, improving algorithmic interpretability, and enhancing the synergy between computational predictions and experimental validation. The insights presented highlight the promising role of pattern recognition algorithms in advancing precision medicine and accelerating drug discovery, while recognizing the challenges that must be addressed to fully realize their potential.