If it worked reliably, it would turn much of early drug discovery from trial and error into simulation. That promise has pulled in nonprofits, big tech, pharma and a wave of startups.

However, the field’s own benchmarks show that today’s models are still far from that goal.

The Virtual Cell Tracker follows the field as it moves: the models, the datasets that feed them, the deals that fund them, and the evidence on whether any of it works. It is updated in place, so the tracker is always the current picture. Every figure links to its source. The tracker is free to read with a sign-up on BiopharmaTrend.

The latest big story: a $1.8 billion bet that data, not models, is the bottleneck

In October, Biohub (the Chan Zuckerberg science organization) expanded its Virtual Biology Initiative to $1.8 billion. The US Department of Energy, NIH, Google DeepMind, Isomorphic Labs and Meta joined.

The goal is open, standardized data for training virtual cells. There is a catch. Biohub’s head of science, Alex Rives, told Reuters that commercial funders get an embargo period with the data before it becomes public, and, as reported by The Next Web, he told Axios the embargo lasts one year. Rives expects a first dataset in about a year and accurate predictive models within five.

This is the clearest sign yet that the field sees data as its bottleneck. Academic groups and startups gain in the long run, because they could never produce perturbation data at this scale themselves. In the short run, DeepMind, Isomorphic and Meta get a hand in shaping the data and a year’s head start on training with it.

What the money can’t buy is proof that more data solves generalization. This year’s independent benchmarks and follow-up studies show that models still struggle once they’re tested on cell types and perturbations they haven’t seen.

How we got here

The idea of simulating a whole cell goes back to Masaru Tomita’s 2001 call to treat whole-cell simulation as “a grand challenge of the 21st century.”

The landmark came in 2012, when Markus Covert’s group at Stanford published a model of Mycoplasma genitalium covering all 525 of its known genes. Mechanistic platforms such as VCell (launched in 1999), CompuCell3D, Morpheus, CHASTE and BioDynaMo let researchers simulate reaction kinetics, diffusion and tissue growth from hand-built equations. Those approaches broke down on real complexity: processes spanning atomic to cellular scales, highly nonlinear dynamics, and too many unknown parameters. After 2012 the field went largely quiet for a decade.

The revival is data-driven rather than equation-driven. Cheap single-cell sequencing, pooled CRISPR screens (Perturb-seq) and transformer models made it possible to learn cell behavior directly from hundreds of millions of measured cells.

A December 2024 Cell perspective led by Charlotte Bunne, with co-authors from Stanford, the Chan Zuckerberg Initiative, Genentech, Google and others, named the goal: the AI Virtual Cell.

It is a set of linked foundation models that represent cells across scales, predict their response to perturbations, and run experiments in silico.

Since then, the race has split into three parts: who has the data, whose models generalize, and who can prove either.

Access The Virtual Cell Tracker for free at BiopharmaTrend.

One recent sign: in October 2026, Iambic raised $150 million in a Nasdaq IPO, saying in its filing that its AI approach can “disrupt legacy drug discovery paradigms.” Investors and pharma partners increasingly judge companies like Iambic on clinical data rather than on technology alone.

Since the previous update of BiopharmaTrend company tracker back in 2024, the evidence has cut both ways. Generate:Biomedicines raised $400 million on Nasdaq, and Insilico Medicine completed Hong Kong’s largest biotech IPO of 2025. Insilico’s rentosertib became the first generative-AI-discovered drug to enter Phase 3, and Generate’s lead antibody is in pivotal asthma trials. Meanwhile, there are setbacks, including Neumora’s lead drug failed two Phase 3 studies, a reminder that machine-learning/datascience driven discovery does not remove clinical risk.

These companies are a small slice of a much larger field. According to BiopharmaTrend’s internal database, more than 500 startups and scaleups use machine learning and generative AI in drug discovery, from point tools for target finding or drug repurposing to end-to-end platforms that run from hypothesis to clinical trial design. In earlier work we reviewed a decade of AI implementation and the novelty of targets pursued by AI-discovered molecules. Our conclusion then still holds: the field is early, with setbacks and inflated expectations alongside real gains.

In a new article update, we profile 13 illustrative AI-enabled drug discovery/development players, most of them clinical-stage and publicly traded, with the latest milestones for each.

Disclaimer: This post is not investment advice of any kind; it is for informational purposes only. Neither WTMB Research & Media, S.L. nor the authors of this post endorse or recommend any particular stocks or investment opportunities. Consult relevant professional service providers for any investment decisions at all times.

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Quick hits from across the techbio space:

AI agents built by PerturbAI and OpenAI analyzed a whole-brain genomic dataset and synthesized insights in 32 minutes.🧠 read here.

Astellas links up with AI Proteins in $400M computational drug discovery deal

Eli Lilly and Company commits up to $1.1B to tackle extrahepatic delivery, partnering with Hong Kong-listed Metis TechBio on tissue-specific LNPs. read here.

Albert Invent raised $55M in Series B funding for chemistry-focused AI platform to be used in R&D work. read here.

Xaira Therapeutics introduced agentic AI platform for drug discovery, bringing together frontier language models, its biological models and datasets, and adaptive research workflows. read here.

TechBio 3.0 Is Closing the Loop. Is “Prompt to Drug” Next?

But despite the obvious impressiveness and importance of this move, the part that Anthropic is addressing is the one that was never the most expensive/complex part of the drug development business, in my opinion.

Designing a promising molecule has been getting cheaper for a decade, as well as target selection. That is not where drug discovery programs fail, generally speaking. Drug programs fail in toxicity and unexpected systemic effects on body, lack of efficacy, in manufacturing, in failing to enroll enough/right patients, in trials that fail because the biology was misjudged or the population was selected in a sloppy trial design (lack of biomarkers, etc.).

As far as I can tell, Anthropic has limited clinical apparatus, no manufacturing, no regulatory track record, and, likely, no answer yet to “what do you do if you find something that works?” Yes, sure, they can design drugs really fast, but then what? How does their model differentiate against later stages, where the role of AI is surprisingly small at the moment?

Claude Science can probably move “target to shortlist in minutes,” and so it compresses the cheapest, fastest, most-solved stage of the pipeline. It is certainly a milestone, certainly impressive, but have they solved drug discovery with it? Probably not yet.

Now, what does it mean for the AI-native biotechs, the likes of Iambic, Recursion, Insilico Medicine, OWKIN, Isomorphic Labs, Cradle, NOETIK, Xaira, SandboxAQ, etc.)?

Well, their moat was never the model alone, I think. It is this:

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I spent last week in Amsterdam, attending HLTH Europe 2026, arguably Europe’s flagship healthcare innovation event, hosting more than 5,000 attendees. It is my second HLTH experience, and two additions to this year’s program stood out: last year’s Pharma & Life Sciences Spotlight grew into a full two-day Global Pharma Summit aimed at C-suite from 30-plus pharma and biotech organizations across 20-plus countries, and the floor gained a dedicated AI @ HLTH zone with its own stage dedicated completely to the progress of artificial intelligence in healthcare and clinical research.

The organizers framed the agenda around whether healthcare AI has broken the Gartner hype cycle, skipping the trough of disillusionment and landing straight into clinical workflows, patient portals, and everyday tools, or not…? The reality is, as always, nuanced and ambiguous, but whatever it is, I have gathered several observations from various interviews and discussions, the HLTH Europe show floor, and the announcements timed to the event, that might be shaping this area in the second half of 2026 and beyond.

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Everyone is talking about frontier AI models and agents. But the most interesting conversations I had in Barcelona earlier this month during two cutting-edge bioinformatics events pointed in a different direction.

I attended two events back-to-back: the Alchemistry Workshop on Free Energy Methods (May 4–6) and the inaugural CoFold Summit (May 6).

The first is the established annual conference for physics-based drug design, with participation and sponsorships from companies like Schrödinger, AstraZeneca, Cresset, OpenBioSim, etc. The second brought together the teams building deep learning co-folding models — Isomorphic Labs, Boltz, OpenFold, RoseTTAFold, SandboxAQ, and others. Same city, same week, overlapping audiences.

Frontier AI models alone won’t get you far in biology. We need to invest in physics-grounded tools and methods.

Anyway, speaking about free energy perturbation methods, they have become the industrial workhorse for binding affinity prediction, and GPU acceleration has moved them from supercomputers to everyday pharma workflows. But FEP needs good starting structures, and it struggles with structurally diverse compounds coming out of generative AI pipelines. On the co-folding side, models like Boltz-1x are making real progress on the chemical validity of predicted poses. But they still default to well-represented binding sites from training data and can’t reliably score what they generate. Allosteric pockets, novel targets, anything underrepresented, still a major challenge.

The pattern kept coming up in different sessions and hallway conversations.

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Their thesis is a bit different from what most AI biopharma companies are doing. Instead of designing new molecules, Biossil uses AI to dig through late-stage clinical failures and figure out which patient subgroups those drugs should have actually been tested on. Ten molecules were acquired while in stealth mode over three years. Trials running in everything from glioblastoma to Alzheimer’s.

That’s not drug repurposing in the classic sense — taking an approved drug and finding it a new indication, like thalidomide going from its original (disastrous) use to multiple myeloma, or metformin being studied in cancer.

Biossil is doing something more subtle: same molecule, same disease, just a more precisely defined subset of patients. The argument is that many drugs “failed” trials only in the “aggregate”, averaged across a heterogeneous population where a real signal got buried.

And they’re not alone. A cluster of companies, each with different technical approaches and varying levels of clinical evidence, is converging on a shared conviction: the pharma industry’s 90%+ clinical failure rate isn’t just a scientific problem. It’s partly an analytical one. The tools to find the right patients simply weren’t good enough, until now.

This is a piece about that convergence. We’ll map who’s doing what, how the approaches differ, what’s actually been validated, and whether the thesis holds up under scrutiny.

In this issue: The Logic of Drug Rescue — The Landscape: Who’s Doing What — A Closer Look at the Frontrunners — The Roivant Precedent — What Doesn’t Work (Yet) — Looking Ahead

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Recent developments suggest that the longevity industry is beginning to outgrow its infancy and move into a more mature formative stage. What started with academic aging research, biohacking, mouse studies, and occasional n=1 self-experiments is increasingly evolving into a well-funded biotech ecosystem focused on translating aging-related mechanisms into clinical development. A prominent example is Life BioSciences, the cell-rejuvenation company co-founded by aging researcher David Sinclair, which recently raised an $80 million Series D round to advance its first-in-human clinical program in optic neuropathies. Beyond cellular rejuvenation, a growing number of biotechs are developing therapies aimed at other hallmarks of aging, from senescence to metabolic dysregulation.

At the same time, despite these encouraging signs of momentum, the longevity industry still faces fundamental challenges, including the absence of a unified hypothesis of aging and the lack of robust proxy markers that can reliably measure biological aging and therapeutic impact.

The promise and challenges of the budding longevity industry

For most of human history, living longer mainly meant not dying early. Over the past two centuries, particularly in the 20th century, clean water and sanitation, improved nutrition, vaccines and antibiotics, and safer maternal, neonatal, and emergency care helped push average life expectancy up by decades.

In many high-income settings, however, the pace of improvement has slowed since the 1990s, while at the same time the burden of chronic disease has increased significantly. This raises a new question: how can we live longer, individually and as a society, without simply shifting the burden into more years lived with chronic disease?

Researchers are increasingly reframing the problem away from purely disease-by-disease fixes and toward the underlying biology of aging that contributes to multiple conditions, such as cancer, cardiovascular, neurodegenerative, and metabolic disease. While the field is only beginning to untangle the complex, and likely multifactorial process of aging, a fledgling longevity industry has emerged around various hypotheses of aging mechanisms. This longevity stack spans different layers from biomarkers and aging clocks that aim to measure biological aging, to therapeutics that target aging-linked mechanisms, as well as care and delivery models (clinics, digital platforms, consulting) that package measurement and interventions into services (Figure 1).

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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.

Virtual Biotech workflow. User query > CSO clarification and briefing preparation > specialized scientist agents > scientific reviewer > synthesis or revision. Source: Zhang et al., bioRxiv, Feb. 23, 2026.

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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⚠️ 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:

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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.

Comparison of the global shares of elite biomedical scientific output and global shares of biotech VC investment between EU, China and US. Source of the data: European Biotech Act

To address this, the Biotech Act includes measures like:

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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.

Role of artificial intelligence in the cancer treatment continuum. Source: Current AI technologies in cancer diagnostics and treatment

Not all of it is language-model work…

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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 in 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.

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This article is brought to you by our writer Anastasiia Rohozianska; also, check out our regular BiopharmaTrend.com contributor, Dr. Louise von Stechow, who writes on AI, biotech strategy, rare disease therapeutics, and emerging modalities (will also be publishing here soon!).

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Each year, the day has a different theme. This year it’s “Synergizing AI, Social Science, STEM and Finance: Building Inclusive Futures for Women and Girls,” which makes it natural to first ask where women generally stand in science, AI and finance today before looking at those who are now shaping these systems.

The numbers show that progress has been slow: women still account for roughly one-third of researchers worldwide and about 35% of STEM graduates, a proportion UNESCO data indicate has barely shifted in a decade. Inside the AI pillar that is rapidly reshaping drug discovery, healthcare and finance, women are estimated to hold around a quarter of AI jobs and less than 15% of senior roles, while a global synthesis of usage studies finds women about 20% less likely than men to engage with generative AI tools in the first place. At the same time, a UN analyses suggest a higher share of women’s jobs than men’s are exposed to AI-enabled automation, particularly in clerical and administrative roles, making it more likely that women experience AI as something that reshapes or displaces their work rather than an arena where they set agendas and build systems.

We usually cover the intersection of advanced biology, digital technologies, and emerging therapeutics—so for this occasion, we focus on STEM through the lens of life sciences.

Today, genomics, cell signalling, neurotrophic pathways and infectious-disease therapeutics are standard components of drug discovery pipelines, but much of this infrastructure traces back to scientists whose careers were shaped by structural barriers and delayed recognition, which is why an international observance focused on women and girls in science is not just symbolic context for an AI-era discussion, but part of the story: modern biomedicine is built on work that women often completed without full recognition.

• Rosalind Franklin’s X-ray crystallography on DNA and viruses was central to solving the double helix and later structural virology, but her role was widely recognized only decades later.

• Barbara McClintock’s maize genetics revealed transposable elements and genes as mobile (“jumping genes”), now fundamental to genomics and epigenetics, yet her Nobel Prize came more than 30 years after her first reports.

• Working under fascist racial laws that barred Jewish scientists from universities, Rita Levi-Montalcini identified nerve growth factor, the first growth factor and a basis of modern cell and neurobiology.

• Tu Youyou’s isolation of artemisinin created a new class of antimalarial drugs that have saved millions of lives and remain standard malaria treatment, while her contribution stayed largely unknown outside China for many years.

Below, we turn to women who are defining research, product, and investment priorities across AI and life sciences, rather than appearing only as those whose jobs, data, or care are shaped by these systems.

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Efforts to amass massive “virtual biology” datasets also made news with Tahoe Therapeutics, Arc Institute, and Chan Zuckerberg Biohub combined roughly 120M single‑cell profiles and 225k perturbations into what they position as the largest open resource for virtual cell modeling. Illumina unveiled a Billion Cell Atlas, built with AstraZeneca, Merck, and Lilly, to train models on standardized cell-line experiments. Observers, though, kept a critical eye on translation, noting that cell lines are not patients and these curated perturbation signals may not cleanly map to real clinical biology. A smaller but distinctive example of the “data and population specificity” theme was BioMed X and the Barbados government’s project to model early diabetic kidney disease using deep molecular profiling and AI, framed as a population-specific “digital African twin.”

Pharma interest in virtual-cell-style modeling showed up in two directions: GSK signed a five-year, non-exclusive license and collaboration with Noetik, committing $50M to use its virtual cell foundation models and bespoke spatial oncology datasets in non-small cell lung cancer and colorectal cancer, while Bristol Myers Squibb entered a strategic clinical-development agreement with Immunai to apply its immune “operating system” to high-dimensional trial data to decode patient immune responses/variability in treatment outcomes.

Another clear theme was the continued movement of AI from proofs-of-concept into everyday workflows. Pharmaceutical players started bringing AI in-house and embedding it into R&D operations – AstraZeneca’s mid-month acquisition of Modella AI was explicitly to fold foundation models and “agentic” AI workflows throughout its oncology pipeline. On the tools side, MIT spinout Boltz (the founder trio is behind the well-known BoltzGen model) launched as a public benefit corporation with $28M seed round, and a multi-year collaboration with Pfizer.

Below is a selective rundown of the early-year signals we tracked most closely.

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There are other speakers highlighting the promises of AI for solving aging. Anthropic CEO Dario Amodei said at 2025 WEF that if AI dramatically accelerates biological research, doubling the human lifespan by around 2030 isn’t unrealistic because it could compress “100 years of progress” into 5–10 years. Such claims are controversial, but they reflect a real trend: AI is impacting both basic geroscience and emerging longevity medicine. Before delving deeper into the intersection of AI and longevity, let’s overview the history of this field before machines came.

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Terray’s work in diffusion methods prompted a broader reflection on generative AI in biology. Today, most conversations and publications center on Transformer-based systems, especially large language models (LLMs) and other foundation models (FMs). LLMs make up a major subset of FMs, but whereas language models are trained primarily on textual data like natural language, code, or biological sequences, foundation models extend the paradigm to additional modalities, including images, audio, video, and even multimodal combinations.

Recent meta-reviews in biomedical NLP collectively catalog nearly 300 LLM instances across hundreds of studies. Foundation models are also proliferating, with over 200 tools developed since 2022 in drug discovery alone. In contrast, the literature on diffusion models for biological and chemical applications remains comparatively modest. So far, there have been only a handful of reviews capturing the diffusion generators. Yet despite lower popularity, diffusion architectures are carving out a meaningful and distinctive role in biotech research and industry.

Before diving deeper into their role in biomedicine, let’s briefly review how diffusion models work in general.

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This transformation is happening thanks to numerous advances in the biology of aging, including the emergence of age-specific biomarkers or “aging clocks,” senolytics, cell reprogramming, and many other promising directions.

But it is also enabled by the rapid advent of foundational technologies, such as artificial intelligence (AI), which has opened doors for more sophisticated biology modeling, better understanding of aging processes, and potentially new creative ideas for addressing humanity’s biggest current limitation: the inevitable decline and end of life.

Below, I would like to highlight several recent advances in aging research that can redefine the landscape of therapeutics development in this area, both R&D and business-wise. This is based on learnings from this year’s 12th Aging Research and Drug Discovery Meeting (ARDD2025) in Copenhagen, and my coverage of last year’s ARDD2024 event…

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