Matrix Science Pharma (MSP)

ABSTRACT

The pharmaceutical landscape has been continually evolving, adapting to the myriad technological advancements of the 21st century. Among these innovations, artificial intelligence (AI) and big data have emerged as transformative tools, altering the trajectory of drug discovery processes. This review elucidates the symbiotic relationship between AI and big data in modern drug discovery, highlighting the profound enhancements in efficiency, precision, and innovation. Drawing from current research trends, we delve into the potent capabilities of AI‑powered algorithms to process, analyze, and glean insights from massive, multifaceted datasets. Furthermore, we explore the tangible impacts on drug repurposing, predictive modeling, molecular simulation, and clinical trials. Yet, this merger is not devoid of challenges. The review also critically examines inherent obstacles such as data integrity, the interpretability of AI models, and the evolving regulatory frameworks. As we gaze into the future of drug discovery, this intersection of AI and big data offers promising horizons, with implications not only for faster, more precise drug development but also for personalized, efficient patient care. This article aims to provide a comprehensive perspective on the current landscape while forecasting the transformative potential in the upcoming decades.