Artificial Intelligence (AI) has transitioned from an experimental tool to foundational infrastructure in pharmaceutical R&D, shifting the industry from empirical, trial-and-error methods to predictive, data-centric models. This transformation addresses the unsustainable economics of traditional drug discovery, which typically costs over $2 billion and takes more than a decade.

Discovery and Preclinical Acceleration AI dramatically compresses early research timelines. Generative AI and deep learning models allow for de novo molecule design and multi-parameter optimization, reducing target-to-preclinical cycles from 4–6 years to approximately 18 months. For example, Insilico Medicine’s AI-generated drug for idiopathic pulmonary fibrosis, Rentosertib, advanced to Phase II trials rapidly using generative platforms ...