Owkin CEO Thomas Clozel highlights five key areas where AI can address core challenges in drug R&D, focusing on medicine discovery and development.6
AI accelerates clinical development more than discovery due to better data availability in later stages, with tools like AlphaFold3 aiding structure determination and experimental science.1
Traditional drug development costs $2.6 billion with 90% failure rate; AI improves target identification, molecule design, clinical trial recruitment (30-50% faster), and extends to manufacturing and regulatory processes.2
AI enhances preclinical research via protein structure prediction and virtual compound screening, while revolutionizing patient recruitment using NLP on health records.3
AI market in pharma projected to grow from $4B to $25.7B by 2030, aiding formulation challenges like solubility and bioavailability with data-driven insights.4
Dozens of AI-driven drug candidates have entered trials after $18–30B investment by 2024, with companies like Insitro and Recursion partnering with big pharma.5
Sources:
1. https://www.inflexion.com/news-and-insights/insights/2026/healthcare-spotlight-ai-drug-discovery-and-development/
2. https://newyork.theaisummit.com/latest-news/ai-in-drug-discovery-and-development-accelerating-pharmaceutical-innovation/
3. https://www.ey.com/en_us/insights/strategy/how-ai-in-biopharma-can-drive-mission-focused-growth
4. https://drug-dev.com/special-feature-artificial-intelligence-in-drug-discovery-development-delivery/
5. https://intuitionlabs.ai/articles/ai-pharma-it-architecture-rd-manufacturing