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A team at Stanford recently posted a preprint describing a system they called “Virtual Biotech”: a coordinated squad of AI agents organized to mirror a real drug discovery company, complete with a virtual Chief Scientific Officer, specialized scientist agents, and over 100 tools for querying biomedical databases.

For their headline demonstration, they deployed over 37,000 agents in parallel, each one tasked with annotating a single clinical trial, linking therapeutic targets to genomic and single-cell transcriptomic features. The resulting dataset spans 55,984 trials. The analysis turned up what the authors call previously unreported associations: drugs targeting cell-type-specific genes were 40% more likely to advance from Phase I to Phase II, 48% more likely to reach market, and showed 32% lower adverse event rates.

In another case study, the system pulled together genetics, transcriptomics, and clinical data on B7-H3 in lung cancer and landed on an antibody-drug conjugate strategy, the same bet several pharma companies are already running in the clinic. It also flagged liabilities and differentiation angles. The whole thing reportedly cost $46 in API credits and took less than a day.

The Virtual Biotech is one lab’s preprint, but it lands in what we recently likened to a “gold rush”—agents are being deployed across clinical operations, translational biology, antibody design, and regulatory workflows. Major pharma companies are in an apparent compute arms race, stacking GPU clusters and billion-dollar AI partnerships within months of each other. Startups backed by hundreds of millions are launching agent-focused platforms. NVIDIA’s Jensen Huang even went so far as to declare agentic AI “the new computer” at this year’s GTC.

Whether the implementations match is another question. In a recent experiment, researcher Liang Chang asked—”Can AI make better decisions than pharma executives?” and sent AI agent teams back to a pivotal 2012 decision in oncology, the BMS vs. Merck biomarker strategy that ultimately decided the Keytruda-Opdivo war, and found that both Claude and GPT independently recommended the same path BMS took. The path that lost.

The agents produced rigorous analysis, identified the exact competitive threat, and still followed the consensus. As Chang put it: “AI can give you the best possible analysis. It can’t give you the courage to go against it.”

What can AI agents do today, where are they falling short, and why is everyone building them?

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🤖 Why agents, and why now?

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GTC 2026 brought that energy straight into life sciences. NVIDIA CEO Jense…

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⚠️ The most immediate one is patient recruitment. Far back in 1979, the father of clinical pharmacology Louis Lasagna observed that the pool of eligible patients shrinks by 90% the moment a trial opens, only to reappear once it closes. Lasagna’s Law remains as relevant as ever: according to a 2022 article, 11% of trial sites enrol zero participants and nearly 90% of trials face meaningful delays. With Phase II and III trials costing roughly $40,000 per day, the financial toll is brutal.

⚠️ Another issue is clinical attrition. Research from VU Amsterdam found that between 2012 and 2019, the share of trials successfully completing each phase declined steadily—particularly at Phase II. In the first half of 2024, nearly a third (32%) of trials were discontinued at Phase II—a 56% rise compared to pre-pandemic levels. Combined with stagnant rates of Phase III initiation over that same decade, the picture is one of a systemic bottleneck: trials that begin are increasingly unlikely to see the finish line.

⚠️ Rare disease research presents its own distinct challenge. As the FDA’s Rare Disease Evidence Principles note, shrinking patient populations make it progressively harder to generate reliable efficacy data through conventional designs—especially placebo-controlled trials, where enrolling enough participants to reach statistical significance can be close to impossible.

⚠️ Apart from operational intricacies, there is the ethical dilemma. Randomized controlled trials remain the gold standard for evaluating new therapies, but randomization isn’t always defensible. When an effective treatment already exists, assigning patients to a placebo raises serious moral questions, e.g. HIV cure trials with the antiretroviral treatment interruption.

The question, then, is whether parts of the control process can be simulated rather than physically recruited.

Digital twins are emerging as a compelling response. Last October, Sanofi Ventures invested in a digital twin platform developer QuantHealth, bringing its total funding to $30M. In 2025, the FDA announced plans to phase out animal testing requirements for monoclonal antibodies in favor of human-relevant methods, including AI-driven computational models—with the EMA moving in the same direction. Both industry and regulators, it seems, are taking this technology seriously.

👥 How it Works

A digital twin is a virtual replica of a physical object, continuously updated with real-world data so it mirrors the original’s behavior in real time. The concept, first applied by NASA in the 1960s, has since migrated from engineering into healthcare.

The applications are wide-ranging: optimizing industrial processes as Eli Lilly did to boost production of their GLP-1 drugs, predicting equipment failures, streamlining supply chains, and accelerating product development.

In a clinical trial patients are generally divided into two groups, also known as arms. The intervention arm receives the experimental treatment; the control arm receives a placebo, standard-of-care treatment or sham, serving as the baseline against which results are measured. Randomized controlled trials (RCTs) are the gold standard because randomization minimizes bias, but that randomization isn’t always flawless.

The traditional workaround of external controls drawn from historical trials, health records, or registries all carry their own limitations. For instance, data like those don’t include underrepresented groups or don’t account for placebo effect due to their observational nature.

Digital twins go a step further: using AI models augmented with historical data, they generate individualized predictions of how a patient might respond under different treatment scenarios. When used to simulate outcomes for patients who do not receive the experimental therapy, these models can produce a synthetic control arm.

These trial-level twins build on a foundation of patient-specific digital twin modeling (virtual replicas of individual physiology shaped by genomics, imaging, and clinical history) which we covered in our earlier overview of biological and patient-specific twins.

In clinical trials, an AI-powered digital twin typically operates in three steps:

• Build virtual patients — AI integrates biomarkers, imaging, genetics, and real-world evidence to generate synthetic profiles capturing the full variability of real populations.

• Run simulated trials — virtual cohorts replace placebo groups or test experimental therapies in silico, probing efficacy and safety without exposing patients to unnecessary risk.

• Optimize continuously — trial parameters like dosing and sample size are continuously refined in real time, anchored by validation against real-world data.

A less computationally demanding synthetic control arm approach uses AI-generated patient data based on registries, and real-world evidence but unlike DTs not modelling it on a particular individual. The appeal is sharpest in rare diseases, where finding enough eligible control patients is often impractical. The FDA, EMA, and NICE have all endorsed the approach, and it’s gaining traction: recent Phase II/III myeloma and lymphoma trials have leaned on external control data, and in at least one case (blinatumomab for acute lymphoblastic leukemia), a synthetic control arm helped support accelerated regulatory approval. AstraZeneca used over 300M synthetic patient records to advance its clinical trials, allegedly saving up to $100M per drug in development.

Synthetic control arms built from historical data have already supported label expansions and accelerated approvals with alectinib, blinatumomab, palbociclib among them. AI-generated individualized digital twins, however, have not yet served as primary evidence in a completed approval.

🦾 An Industry Arm

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Shortly prior to last Christmas the European Commission published a proposal of a European Biotech Act, a strategic initiative aimed at setting up a regulatory framework to strengthen the life sciences sector across the EU. The document has been mostly positively received by the sector leaders as a needed step towards fostering local biotech innovation. Brussels isn’t stopping there. Commission President Ursula von der Leyen pitched EU-Inc, a pan-European company structure meant to solve what many see as the EU’s core startup problem of navigating 27 different bureaucratic regimes. The proposal would let one register in 48 hours, fully online and in English.

The Case for Urgency

Why does it matter? Europe gave the world its first blockbuster pharmaceutical (Aspirin in 1899) and just 30 years ago produced half of all new treatments globally. Today, that share has fallen to just one in five. Even though the EU biotech industry has grown twice as fast as the overall union’s economy over the last decade, it struggles to convert the world’s top science into commercially viable products.

Europe holds a comparable share of the top 10% most-cited biomedical research to the US and China, yet lags significantly behind in venture investment — a gap caused by underdeveloped private equity markets and fragmented, complex regulatory frameworks. The disparity is also visible in listing trends, with 66 of the 67 EU companies that went public over the past six years choosing foreign stock exchanges.

To address this, the Biotech Act includes measures like:

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A newly launched U.S. bra…

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Transformer architectures trained on biological data are beginning to predict drug response, generate therapeutic hypotheses, and identify which patients are likely to benefit from which treatments (part of a broader push that includes early attempts at virtual cell models) tasks that previously required years of wet-lab iteration. Some of that work is still early, though a handful of results have made it far enough through validation to be worth paying attention to.

• Google Research, Google DeepMind, and Yale spent much of 2025 scaling C2S-Scale, a language model that reads single-cell RNA data as text; the 27-billion-parameter version, released in April, came in October with wet-lab validation of a model-generated hypothesis about making immune-”cold” tumors visible to T cells.

• A collaboration between Microsoft Research, Providence Health, and the University of Washington took a complementary approach: GigaTIME, published in Cell in December, routinely converts pathology slides into virtual immune-protein maps, surfacing over 1,200 significant associations across 14,256 patients.

• At Davos in January, Demis Hassabis now put Isomorphic Labs‘ first trials, primarily oncology candidates, at end of 2026; the company followed this month with IsoDDE, a general-purpose drug design engine that reportedly doubles AlphaFold 3’s accuracy, already deployed across its oncology programs.

Not all of it is language-model work.

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Separately, a new benchtop sequencer announcement from San Diego kept the “falling sequencing …

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Just a day earlier, MIT Technology Review published its annual 10 Breakthrough Technologies list. This year, three of the highlighted technologies were in genomics: personalized gene editing, embryo scoring, and gene resurrection. From there, it seems like genomic applications are moving more into the mainstream technology discourse.

Another just-in data point from a few days ago is a report out of San Diego, where Element Biosciences says its newly announced VITARI benchtop sequencer can deliver a whole genome for $100, positioning it as a lower-cost alternative to Illumina’s high-throughput systems.

Looking at these and many of last year’s developments, genomics come into view as an integrated technology wave that extends from data generation to interpretation, intervention, and biological reconstruction.

With those early-2026 pings as a starting point, let’s do a selective pass through a few genomics patterns that seem to be carrying momentum into 2026.

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At first glance, these headlines span different companies and medical areas. But they share a common thread: each centers on advanced therapeutic modalities (ATMs)—a new generation of medicines that go beyond the limits of conventional drugs.

According to BCG, eight of the top ten best-selling biopharma products in 2025 are new-modality drugs, and the global pipeline value for these therapies has reached $197B. ATMs are becoming a more established part of the industry and are noticeably contributing to its growth.

Reject Tradition, Embrace Modernity

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In DealForma’s figures cited by CEO Chris Dokomajilar, deal flow between large-cap biopharma and Chinese biopharma accelerated in 2024-2025. In 2025, big pharma completed 18 in-licensing and asset purchase deals (just one in 2020) from Chinese companies with $50M+ upfronts, totaling $57.3B in deal value and $3.9B in upfront cash and equity. By 2026, China continues to emerge as a major source of globally licensable, clinical-stage biotech assets, backed by an increasingly complete innovation stack, even as new policy constraints complicate cross-border data flows and outsourcing.

In late January, speaking at the Asian Financial Forum in Hong Kong, executives from Merck and Amgen pointed to China as a likely early approval market for fully AI-designed drugs. Merck China president Marc Horn suggested that 2026 could mark the shift from AI-assisted discovery to compounds designed end-to-end by AI entering regulatory pipelines, citing China’s patient datasets, clinical execution, and the government’s recent “AI Plus” policy push. Amgen’s chief medical officer Paul Burton pointed to a similar timeline, seeing 2026 as a year when AI-driven and human genetics–led discovery could begin translating more directly into drug candidates.

For perspective, among recent big pharma deals involving Chinese companies, this year’s JPM week had AbbVie’s $5.6B partnership with RemeGen around a bispecific oncology asset. Looking back at just 2025, Pfizer licensed a bispecific from 3SBio with $1.25B upfront, AstraZeneca entered a multi-year $5.3B AI-enabled small-molecule discovery collaboration with CSPC Pharmaceuticals, and GSK’s x Jiangsu Hengrui agreements included $500M upfront and up to about $12B in potential milestones.

From Generics to Innovation

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On the tech side, DeepMind’s AlphaGe…

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