Nelson Advisors interviewed by Mergermarket: Big Pharma, Big Tech target genomics players to feed AI models with healthcare data

Nelson Advisors
11 minutes ago
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Nelson Advisors partners Lloyd Price and Paul Hemings were interviewed by Mergermarket for their 'Big Pharma, Big Tech target genomics players to feed AI models with healthcare data' story.

Source: https://mergermarket.ionanalytics.com/content/1004461385?source=news

Interest in genetics datasets is growing as Big Pharma and Big Tech alike construct data-hungry AI models to power drug development.

"Pharma’s key challenge of finding the next blockbuster drug hasn’t changed," Stephen Reese, partner and co‑chair of Clifford Chance's Healthcare and Life Sciences sector group, told Mergermarket.

"The field of genomics is hugely valuable to tackling some of those really big killers, including cancer," he said.

Big Pharma companies are turning to AI models to drive drug discovery technology that speeds up thepathway of a molecule to clinical trials, Reese said. At the same time, drug developers hope to use other AI models such as digital twins to make clinical development faster and cheaper, he said.

For example, GSK sealed a 5‑year USD 50M deal with US player Noetik for access to both data and its training models earlier this year focused on oncology R&D and colorectal cancer, noted Lloyd Price, partner at Nelson Advisors.

Meanwhile, AstraZeneca paid USD 12M for the assets of UK-based Achilles Therapeutics in 2024, and theUS company Recursion Pharmaceuticals merged with UK-based Exscientia in a USD 790m deal in 2024.

According to Mergermarket data, other major global deals in this space include the USD 1Bn launch of US-based AI player Xaira Therapeutics in 2024 and the pending USD 1.5Bn listing of Singapore‑based NanyangBiologics via the Nasdaq‑listed SPAC RF Acquisition Corp II, announced in October 2025.

Biological data quest

The cost of sequencing the human genome has plummeted since the USD 2.7bn Human Genome Projectcompleted its mission in 2003. Nowadays, this can fall to as little as USD 200 per sequence, leading to fast-growing data troves worldwide.

At the same time, demand is also growing for other forms of structured biological data, including proteomics and transcriptomics, Price said.

Increasing usage of precision medicine and tools for handling large datasets are contributing to a soaring global market for AI in genomics services, which is expected to grow by 38% per year from USD 1.5 Bn in 2026 to approximately USD 20 Bn by 2034, according to a report by Precedence Research.

However, a key bottleneck is finding good‑quality proprietary data to feed AI models, Price said. The main AI models draw their data from open source datasets designed for approved researchers under controlled conditions, such as Genomics England's 100,000 Genomes Project, making it hard to stand out, he said.

Companies with privately owned, structured, and coded datasets are the most sought-after targets, Price said. The holy grail is longitudinal data that combines medical history, genomics, and lifestyle information, he added.

One interesting case is US‑based 23andMe, whose customers voluntarily contributed genomic data in return for health and ancestry reports, effectively subsidising collection. 23andMe declared bankruptcy inMarch 2025, received a USD 256M takeover bid from Regeneron in May, but was acquired by the co‑founder's nonprofit, TTAM Research Institute, for USD 305M in July.

In a sign of the many challenges facing the industry, there have been a series of claims against 23andMe for data breaches.

Go big or go home

Big Tech is not letting Big Pharma run free in this environment.

Major tech names are also showing interest in the overlap between AI and genomics, with examples including Google parent Alphabet's participation in UK‑based Isomorphic Labs' USD 600M funding round last year and NVIDIA's investment in UK company Basecamp Research earlier this year.

Big Tech companies bring infrastructure, scale, and computing power to the field, and need new markets because consumer tech is saturated, Price said.

However, downstream expansions by the likes of NVIDIA into healthcare bring with them a risk of antitrust issues, said Adrian Toutoungi, partner at Taylor Wessing.

On the pharmaceutical side, Big Pharma holds large troves of sequencing data from clinical trials that they can harness for training AI models, Toutoungi said.

Big Pharma is also keen to access new AI tools via partnering with tech firms, such as Eli Lilly andNVIDIA's Co-Innovation lab project announced earlier this year, he said.

Additionally, applying general tech to healthcare does not always make for a natural fit, Price said, adding that the winners will be specialised players who deeply understand the vertical.

M&A, licensing challenges

One challenge of the field is knowing when to license in, or acquire, data and AI models, Price said.Licensing allows access without full ownership but lacks exclusivity, meaning competitors can gain access to the same datasets, he said.

Data sovereignty is another factor, where questions arise over the ownership of the data, and whether itcan cross borders to be stored in international data centres – a key restraint in the EU, Price said.

Meanwhile, buyers may not know how good the data and AI outputs are until later in the process, so they resist large upfront payments and prefer risk‑sharing, Reese said.

Monetising the technology is also tough for data and AI providers, since AI drug discovery collaborations tend to yield the most benefit when the drug enters the market many years away, Reese said. Monetising AI models for clinical trials, such as digital twins, can also create regulatory issues, he said.

In spite of the challenges, M&A interest in genomics data and AI tools is likely to continue growing, Reese and Price said.

The pace of the growth depends on how quickly genomics models and datasets can prove reliably that they improve outcomes and justify the investment, Reese said.

Notably, the surge in AI use is likely to further drive the uptake of sequencing machines, which then produce more biological data to train AI in a reinforcing loop, Price said.

And as a principal driver, the potential upside of AI and genomics in drug discovery, particularly for cancer, is too important to ignore, Reese said.

by Jonathan Smith and Christel Thunell, with analytics by Akshaya Hari

Source: https://mergermarket.ionanalytics.com/content/1004461385?source=news

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