The role of AI for drug discovery in healthcare

Page date: Published 13 Feb 2025; updated 25 Mar 2026. 2 min read.

The article describes artificial intelligence and machine learning tools for analyzing biological data and predicting how candidate compounds may interact with human targets. It lists potential applications in identifying targets and compounds, forecasting safety and efficacy, selecting clinical-trial participants, and checking regulatory documentation. The page presents these as uses and expected advantages; they do not remove the need for laboratory, clinical and regulatory evaluation.

The article cites a study reporting 80–90% success rates for Phase I trials of AI-discovered drugs, compared with historical industry averages of 40–65%. It does not explain the study’s sample selection or methods in the page, so the comparison should be treated cautiously and does not establish later-stage success or clinical benefit. It also quotes a forecast that AI could save more than USD 70 billion in drug discovery by 2028; this is a projection, not a realized savings figure.

Other areas discussed include data-driven target discovery, work on rare-disease medicines and partnerships between pharmaceutical and AI companies. Related links: Health and life science; AI in clinical trials.