The Synergy Of Artificial Intelligence And Big Data Analytics In Accelerating Drug Discovery: From Target Identification To Clinical Trials

Abstract

The traditional drug discovery pipeline is lengthy, costly, and marked by high attrition rates, with over 90% of candidates failing in clinical trials. The convergence of Artificial Intelligence (AI) and Big Data analytics is revolutionizing this paradigm, offering transformative potential across all stages of drug development. This review explores how AI—encompassing machine learning, deep learning, and natural language processing—leverages vast datasets such as genomic, proteomic, clinical, and real-world evidence to enhance drug discovery. AI-driven techniques accelerate target identification, optimize lead compound generation, and enable virtual screening through predictive modeling of drug-target interactions. Notably, platforms like AlphaFold have redefined protein structure prediction, while generative AI models design novel molecules with enhanced pharmacological profiles. Furthermore, AI expedites drug repurposing, accurately predicts ADME/Toxicity properties, and enhances clinical trial efficiency by improving patient stratification and adaptive trial design. The synergy between AI and Big Data not only reduces costs and timelines but also increases the probability of clinical success. However, significant challenges remain, including data heterogeneity, model interpretability, regulatory hurdles, and ethical concerns related to bias and data privacy. Emerging solutions such as hybrid AI-physics models, federated learning, and autonomous robotic labs show promise in overcoming these limitations. With several AI-generated compounds advancing to clinical trials, the integration of AI and Big Data is shifting drug discovery from an empirical to a predictive science. This review underscores their transformative role in advancing personalized and precision medicine, marking a new era of efficient and intelligent therapeutic development.