Applications of Artificial Intelligence, Machine Learning, and Big Data Analytics in Drug Discovery and Pharmacovigilance: Current Advances and Future Perspectives

Background: Artificial intelligence (AI), machine learning (ML), and big data analytics are increasingly influencing pharmaceutical research by enabling faster and more accurate decision-making. Conventional drug discovery and pharmacovigilance processes are time-consuming, resource-intensive, and often limited by manual workflows and delayed detection of adverse drug reactions (ADRs). Objective: This review aims to summarize recent advances in the application of AI, ML, and big data in drug discovery and pharmacovigilance. Methods: A narrative review of published literature was conducted focusing on AI- and ML-driven approaches in target identification, virtual screening, lead optimization, ADMET prediction, drug repurposing, and pharmacovigilance activities. Results: AI- and ML-based models have demonstrated significant potential in accelerating drug discovery, improving prediction of pharmacokinetic and toxicity profiles, enabling efficient drug repurposing, and enhancing pharmacovigilance through automated case processing, advanced signal detection, and knowledge-graph-based analyses. Conclusion: The integration of AI, ML, and big data across the drug development lifecycle offers substantial benefits in terms of efficiency and safety monitoring. However, challenges related to data quality, interpretability, and regulatory acceptance must be addressed to ensure responsible and effective implementation.