What does this research mean for the field?

AI-driven drug discovery presents unprecedented opportunities for pharmaceutical innovation, but requires strong regulatory harmonization and ethical governance for responsible adoption. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.CHALLENGES_CONSENSUS.

What question did this study set out to answer?

The research aims to assess the implications of AI in drug discovery, focusing on regulatory and ethical challenges.

March 1, 20260 citationsOpen Access

AI-Driven Drug Discovery: The Role of Regulatory Frameworks, Compliance, and Ethical Governance

RYRitesh Chaudhari*1, Dr. Jay Prakash Thakur2, Neha Yadav3

Key Points

The research aims to assess the implications of AI in drug discovery, focusing on regulatory and ethical challenges.
Comparative qualitative analysis of international regulatory frameworks
Examination of ethical issues including justice, transparency, and patient safety
Assessment of compliance challenges related to data privacy and intellectual property rights
AI offers unprecedented opportunities for drug innovation and design
Effective governance requires robust regulatory harmonization and compliance systems
The study advocates for global efforts and adaptable models to balance innovation and responsibility

Abstract

Pharmaceutical research is changing as a result of artificial intelligence (AI), which speeds up drug discovery, improves molecular design, and makes it possible to predict treatment effects. But the quick adoption of AI in pharmaceutical sciences presents important concerns regarding ethical governance, compliance systems, and regulatory sufficiency. In order to determine the advantages and disadvantages of regulating AI-driven drug discovery, this study looks at international regulatory frameworks, such as those of the European Medicines Agency (EMA), the Central Drugs Standard Control Organization (CDSCO), and the U.S. Food and Drug Administration (FDA). Ethical problems of justice, openness, and patient safety are examined with compliance issues such data privacy, intellectual property rights, and reproducibility standards. The research presents new developments in adaptive regulatory and governance models using a comparative qualitative methodology. The results indicate that although AI presents previously unheard-of possibilities for pharmaceutical innovation, responsible and equitable adoption requires strong regulatory harmonization, improved compliance procedures, and multi-stakeholder ethical governance. In order to strike a balance between innovation and responsibility, the study's conclusion suggests global harmonization efforts, blockchain-enabled compliance, and flexible regulatory models.

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