Abstract

Vivek Jain*, Yogesh Soni, Yogendra Kurmi, Yuvraj Singh Rajpoot and Rupesh K. Jain

ABSTRACT

The integration of artificial intelligence (AI) and big data analytics has catalyzed a paradigm shift in drug discovery and delivery, offering innovative solutions to accelerate the development of novel therapeutics and optimize patient care. In drug discovery, AI algorithms analyze vast amounts of biological, chemical, and clinical data to identify potential drug targets, predict compound activities, and optimize lead candidates with improved efficacy and safety profiles. Moreover, big data analytics enable the integration of diverse data sources, including genomic, proteomic, and clinical data, to uncover disease mechanisms, identify biomarkers, and stratify patient populations, facilitating personalized treatment approaches. In drug delivery, AI-driven technologies enable the design of targeted drug delivery systems, personalized dosing regimens, and predictive modeling of drug pharmacokinetics and pharmacodynamics, enhancing therapeutic efficacy while minimizing adverse effects. Furthermore, real-time monitoring and analysis of patient data enable proactive intervention and personalized healthcare delivery, improving patient outcomes and reducing healthcare costs. However, challenges such as data quality, bias, and interpretability remain significant barriers to the widespread adoption of AI and big data analytics in healthcare. Addressing these challenges requires interdisciplinary collaboration, robust data governance frameworks, and ethical considerations to ensure the reliability, transparency, and fairness of AI-driven approaches. Despite these challenges, the synergy between AI and big data analytics holds immense promise for transforming drug discovery and delivery, revolutionizing the healthcare industry, and ultimately improving patient outcomes and quality of life.