Artificial intelligence is fundamentally altering the way pharmaceutical research is conducted globally. Machine learning algorithms, deep neural networks, and natural language processing tools now assist scientists in identifying molecular targets, predicting clinical outcomes, and optimising drug candidates with a speed and accuracy that surpasses conventional approaches. The adoption of these technologies promises to reduce the time and financial burden associated with drug development, which historically has taken over a decade and cost billions of dollars for a single approved product. Nevertheless, the deployment of artificial intelligence in drug discovery and regulatory approval raises profound legal, ethical, and institutional questions. Questions of algorithmic accountability, data integrity, intellectual property ownership, liability for AI-generated errors, and patient safety have not yet been comprehensively resolved within existing regulatory frameworks. Most national drug regulatory authorities continue to rely upon approval processes designed for traditionally developed pharmaceutical products and are ill-equipped to evaluate AI-driven submissions. This paper critically analyses the current legal landscape governing the use of artificial intelligence in drug discovery and approval. It examines regulatory approaches in the United States, the European Union, and India, identifies key legal challenges, and proposes a framework for coherent and forward-looking regulation that balances innovation with patient protection.
Amanjot Singh Mann* (Mon,) studied this question.
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