Explainability, Regulatory Compliance, and Ethical Dimensions of Artificial Intelligence in Pharmaceutical Research and Development

In recent years, Artificial Intelligence (AI) and Machine Learning (ML) have become transformative tools across pharmaceutical research and development spanning from target discovery, drug design, formulation, to clinical decision support and post-marketing surveillance. However, the widespread adoption of AI in regulated pharmaceutical settings is constrained by critical issues of explainability, regulatory compliance, and ethical governance. Without interpretability or transparency, AI “black boxes” risk undermining trust, raising safety concerns, and impeding regulatory acceptance. Regulators such as the U.S. Food and Drug Administration (FDA) are evolving guidance frameworks (e.g., for AI/ML in drug development and Medical Device Software) that emphasize credibility, life-cycle management, and documentation. Meanwhile, ethical dimensions such as bias, accountability, data privacy, and informed consent require careful integration into AI deployment strategies. In this review, we synthesize the state of explainable AI (XAI) methods tailored to pharmaceutical applications, map current regulatory landscapes (with emphasis on FDA, EMA, and emerging Indian regulation), and discuss ethical considerations unique to drug development. We propose a unified framework to guide researchers and industry in deploying AI systems that are interpretable, compliant, and ethical thereby enabling safer, trustworthy adoption of AI in pharmaceutical R&D.