Can agentic AI dent Eroom's Law? Large reasoning models across the drug discovery and development pipeline

Productivity in pharmaceutical R&D continues to fall despite deeper biological insight and steady gains in clinical development operations - a phenomenon termed Eroom's Law. Agentic AI workflows powered by reasoning-trained large language models (LLMs), increasingly described as large reasoning models (LRMs), could potentially dent this trend. Unlike earlier task-specific models, these systems couple multi-step reasoning with the ability to plan, invoke external tools and retrieve authoritative information, enabling them to decompose and execute complex scientific and operational tasks. This review discusses agentic AI applications across the drug development continuum, from target discovery to post-market surveillance, and highlights three near-term use cases: algorithmic drug repurposing, informed consent support and automated drafting of regulatory documents. For each, we outline plausible architectures, the current level of supporting evidence and the principal failure modes that constrain deployment. Realizing these gains, however, requires prospective validation, rigorous human oversight and governance frameworks that align algorithmic outputs with clinical, ethical, legal and regulatory standards. When implemented responsibly, agentic AI could transform human-AI collaboration in biopharma, improving R&D efficiency and accelerating delivery of safer, more effective therapies.