This edition examines AI’s evolution from a discovery tool into a decision-making layer across the drug-development lifecycle—from target identification and molecule design to clinical trials, regulatory submissions, and post-market surveillance. By integrating multi-omics, clinical, and real-world data, AI is enabling faster and more data-driven R&D decisions, while generative AI, foundation models, quantum computing, and lab automation are broadening its applications.

Regulators are also establishing frameworks for responsible adoption. FDA and EMA initiatives around Good AI Practice, risk-based validation, transparency, human oversight, and lifecycle management are making regulatory readiness essential for AI-generated evidence.

Investment remains strong, with approximately $29.2B deployed across 437 AI-enabled drug-development funding deals since 2021, particularly in oncology and AI/ML platforms. Although the market is growing rapidly, it remains fragmented.

Ultimately, leaders will be companies that combine proprietary data, validated AI models, experimental and clinical evidence, automated infrastructure, and robust governance—demonstrating tangible improvements in development timelines, decision quality, and patient outcomes.