Explainable AI in Drug Discovery and Clinical Trials: Bridging Prediction, Interpretation, and Ethics
Arjun Anand · International Journal for Research in Applied Science and Engineering Technology · 2025
Integrating Artificial Intelligence (AI) into drug discovery and clinical trials marks a transformative evolution in pharmaceutical research and healthcare innovation. This study explores how AI technologies such as machine learning (ML), deep learning (DL), and natural language processing (NLP) are reshaping the landscape of drug development by accelerating target identification, optimizing molecule screening, and enhancing patient recruitment strategies. By reviewing key advancements from 2020 to 2025, the paper evaluates both the potential benefits and critical limitations of AI, including challenges related to data privacy, interpretability, and regulatory compliance. Furthermore, the research highlights real-world applications and ethical implications, emphasizing the necessity for transparent, explainable, and clinically validated AI systems. Through a multidisciplinary lens, this paper contributes to the ongoing conversation around responsible AI adoption, proposing frameworks for safer, more effective, and equitable integration of AI in the pharmaceutical industry.