APPLICATION OF ARTIFICIAL INTELLIGENCE IN PRECISION BIOTECHNOLOGY AND DRUG DISCOVERY

Abstract

Artificial Intelligence (AI) has emerged as a transformative technology in precision biotechnology and drug discovery by enabling faster, more accurate, and cost-effective approaches for understanding biological systems and developing therapeutic solutions. Traditional drug discovery processes often involve extensive time, high costs, and significant failure rates; however, AI-driven methods such as machine learning, deep learning, and predictive modeling have introduced new opportunities for target identification, molecular design, biomarker discovery, and personalized medicine. This research explores the applications of AI in precision biotechnology, focusing on its role in genomic analysis, protein structure prediction, drug–target interaction prediction, and optimization of therapeutic development. The study reviews current AI-based approaches, evaluates their contributions to improving drug discovery pipelines, and analyzes the challenges associated with data quality, model interpretability, ethical concerns, and clinical implementation. The findings highlight that AI technologies can significantly enhance precision medicine by integrating complex biological datasets and generating predictive insights for individualized treatments. Despite existing limitations, continuous advancements in computational methods and biomedical data integration are expected to strengthen the role of AI as a key driver of next-generation biotechnology and pharmaceutical innovation. This research provides an overview of the current landscape of AI applications and discusses future opportunities for improving healthcare outcomes through intelligent, data-driven approaches.