The Great AI Land Grab in Pharma

Drugmakers race to lock in AI partners, cut risk, and secure a lead in data-driven discovery

5 Feb 2026

Artificial intelligence is moving from the margins of pharmaceutical research to a central role in drug discovery, prompting a rise in partnerships, acquisitions and consolidation across the industry.

Large drugmakers are increasingly turning to AI as they seek to cut the cost and uncertainty of developing new medicines, while facing pressure from patent expiries on blockbuster drugs. Drug development typically takes more than a decade and costs billions of dollars, with high failure rates. AI systems that can analyse large biological datasets are seen as a way to identify promising compounds earlier and improve decision-making in the early stages of research.

This shift is changing the structure of industry deals. Instead of short-term contracts focused on single projects, pharmaceutical groups are pursuing long-term partnerships or buying AI-focused platforms outright. Analysts say many companies now believe AI delivers the most value when it is embedded across research and development operations, rather than used as an external service.

Several large drugmakers have signalled this approach through recent transactions. AstraZeneca has expanded its use of AI through collaborations and targeted acquisitions aimed at strengthening its discovery and research capabilities. Such moves reflect a broader effort by large pharmaceutical companies to secure in-house access to advanced analytics and data-driven research tools.

Smaller biotechnology groups are adapting to this demand. Companies such as Recursion and Insilico Medicine have built AI-based discovery platforms supported by large datasets of biological and chemical information. High-profile partnerships, including Eli Lilly’s collaboration with Insilico Medicine, underline growing interest in AI-enabled platforms that can generate multiple drug candidates, rather than single experimental assets.

Competition is intensifying as a result. Companies with strong data assets and proven AI systems are gaining an advantage in negotiations, while those without access to high-quality data face increasing pressure. Traditional contract research organisations are also being challenged as pharmaceutical groups bring more discovery and analytics capabilities in-house.

Obstacles remain. Integrating AI into established research environments requires changes to internal processes, careful validation of data and regulatory scrutiny over how automated tools influence development decisions. Still, industry executives and investors largely view these issues as manageable.

Many expect consolidation around AI-driven drug discovery to accelerate. For pharmaceutical companies, securing the right technology, data and partners is increasingly seen as critical to shaping future drug pipelines.