This analysis is now published in full on BioPharmaTrend.

What’s inside:

• Where general models already match domain-specific tools, and where they don’t

• What Anthropic’s recent releases change for companies with proprietary biological data

• The three things that actually constitute a TechBio moat — and why renting data isn’t the same as generating it

• Which companies face real pressure from this, and which don’t

• ALSO — The numbers behind China’s biopharma ascent: human trial approvals down from 501 days to 87, out-licensing deal value up 54x to $135.7B, and the limits the new ITIF report is careful to flag.

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(For BioPharmaTrend subscribers · €8,99/month or €89,90/year).

My field notes from conversations and sessions during HLTH Europe 2026, the leading healthtech event in Europe.

Published in full on BioPharmaTrend.

What’s inside:

• The verification layer: aiomics running on top of hospital IT to structure faxes, referrals and dictation; Guideways AI launching EU MDR Reviewer and QMS Reviewer to catch CE-submission problems before they cause delays

• “Human-in-the-loop” as a product category — Roche’s Kimberly Noel pointing to Human-In-the-Loop GmbH, a company built entirely around it

• EU sovereignty sold as a feature: Datum Agent as an EU-sovereign platform, iCure’s Cardinal v2 prepared for NIS-2, the AI Act and the European Health Data Space

• Triage results with numbers behind them: Ada Health’s MomConnect study in South Africa, where care-seeking went from 17% to 43% across 968 participants; Tucuvi’s voice agent associated with 43.7% fewer urgent COPD admissions; Infermedica’s 1.55M-interaction analysis in Mayo Clinic Proceedings

• Adherence: BrightInsight and Sanofi tracking 6,000+ specialty patients against the fact that 71% abandon therapy within a year; PACE Clinical citing 200,000 premature EU deaths and €125B in avoidable costs

• Why data infrastructure, not AI, was the biggest theme on the floor

→ Read the full DeepDive
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The one insight: neither side works alone. Co-folding generates structural hypotheses from sequence; physics-based methods validate them. The teams investing in both are the ones to watch.

Published in full on BioPharmaTrend.

What’s inside:

• Where free energy perturbation methods actually stand, and why pharma can’t scale them

• Where co-folding models are making real progress, and where they still fail

• What’s solved in computational drug discovery, and what isn’t

• Company picks from both events.

→ Read the full DeepDive
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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

💊 The Logic of Drug Rescue

Before we profile the companies, it’s worth understanding why this thesis is surfacing now and why it’s distinct from what came before.

Drug repurposing has a long history. Sildenafil started as a cardiovascular drug before becoming Viagra. Thalidomide was rehabilitated remarkably, decades after its teratogenic disaster in the 1960s, as a treatment for multiple myeloma. These are cases where an approved (or previously studied) molecule found a genuinely new indication.

What companies like a newcover Biossil, as well as more established players like Lantern Pharma, Pathos AI, and BPGbio, are doing is different. They are not necessarily changing the target disease, but the target patient. The hypothesis: within a trial population that produced a negative aggregate result, there are subgroups of patients who responded, and whose response was masked by the statistical noise of everyone who didn’t.

This isn’t a new idea conceptually. Post-hoc subgroup analysis has been part of clinical trials for decades. What’s new is the scale and sophistication of the AI being applied: multimodal foundation models trained on hundreds of petabytes of data, causal inference engines, spatial transcriptomics paired with pathology imaging, and multi-agent systems reasoning across publications and biomarker data.

The question is whether the analytical tools have finally caught up to the biological complexity… or whether we’re just building fancier ways to p-hack.

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🗺️ The Landscape: Who’s Doing What

The companies working in this space share a thesis but diverge significantly in their technical approaches, therapeutic focus, and maturity. Here’s how the landscape breaks down.

⭐ Biossil

The freshest entrant. Biossil emerged from stealth with $70M co-led by Founders Fund and OpenAI, and a portfolio of ten molecules acquired quietly over three years. Their approach centers on reanalyzing late-stage clinical failures to identify patient subgroups where a meaningful treatment signal was hidden by population heterogeneity. Trials are running across glioblastoma, Alzheimer’s, and other indications.

What makes Biossil notable is the breadth of their bet: ten molecules across multiple therapeutic areas, funded by investors who have not traditionally played in biopharma. The OpenAI connection signals a belief that general-purpose AI capabilities, not just domain-specific biostatistics, can crack the patient stratification problem. That’s an interesting bet, though one that remains unproven clinically.

Details on their technical platform are still limited. We’ll be watching for specifics on what data they’re training on, how their models identify subgroups, and, most critically, whether their approach produces prospectively validated biomarkers or just retrospective correlations.

⭐ Lantern Pharma (Nasdaq: LTRN)

Lantern has been working on a related playbook for years, making it one of the most useful reference points for whether the thesis actually holds up in the clinic. Their RADR AI platform identifies abandoned clinical-stage drugs and matches them to patient subgroups most likely to respond. The focus is oncology.

The most tangible proof point right now is LP-300, a candidate in development for never-smoker non-small cell lung cancer (NSCLC) — a molecularly distinct type of the disease with poor outcomes and no approved therapies focused on this specific population. Lantern has just announced that the FDA raised no objections to key proposed protocol amendments for the Phase 2 HARMONIC trial, a meaningful de-risking step.

Three protocol changes are worth noting:

• Focused enrollment on the EGFR exon 21 L858R subgroup — the molecular subset, accounting for roughly 40% of EGFR-mutant NSCLC globally, where current therapies leave the largest unmet need, according to the company, and where LP-300’s preliminary data have been most differentiated.

• Extended dosing from a maximum of 6 to 8 cycles.

• Transition from a randomized to a single-arm design — intended to accelerate enrollment and sharpen the clinical signal in a genomically defined subgroup.

On the AI side, Lantern has been developing withZeta.ai, a multi-agentic system derived from RADR that extends the platform’s mechanistic modeling capabilities. For LP-300, withZeta has been used to interrogate the drug’s mechanistic potential in the L858R setting — reasoning across publications and biomarker observations to surface insights that informed the development strategy. It’s an interesting example of AI agents being used not just for patient selection but for mechanistic hypothesis generation.

⭐ Pathos AI

Pathos is arguably the company with the deepest data moat in this space, thanks to its roots in the Tempus ecosystem. Founded by executives from Tempus (Eric Lefkofsky’s healthcare AI company), Pathos claims to have access to over 200 petabytes of multimodal oncology data linked to patient outcomes, reportedly 50 times the size of The Cancer Genome Atlas, the largest public genomic dataset in oncology.

Their thesis mirrors Biossil’s: drugs fail because they were tested in the wrong patients, with the wrong assumptions, in trials that couldn’t answer the real question “who benefits, and why?” But Pathos is focused exclusively on oncology and is building what it describes as the largest foundation model in the field.

In April 2025, Pathos entered a major three-way collaboration with AstraZeneca and Tempus to build a multimodal oncology foundation model, with $200 million in data licensing and model development fees flowing to Tempus. The foundation model is being built on Tempus's repository, which includes 7.3 million de-identified patient records, including 1.4 million with imaging data, 1.3 million with genomic information, and 260,000 with full transcriptomics profiles.

Pathos is also running its own clinical programs. In March 2025, they dosed the first patient in a Phase 1b/2a trial of pocenbrodib (a CBP/p300 inhibitor) in metastatic castration-resistant prostate cancer. They also acquired Known Medicine, which builds patient-specific 3D cell cultures and uses AI to predict drug responses prospectively — an attempt to close the loop between computational prediction and wet-lab validation.

Funding: $365 million in a Series D (May 2025), at a $1.6 billion valuation.

⭐ BPGbio

BPGbio takes a different technical angle: Bayesian causal AI, built on their NAi Interrogative Biology platform. Rather than relying on pattern recognition across large datasets, their approach aims to infer causal relationships, not just correlations, between patient characteristics and treatment response.

The platform integrates one of the largest non-governmental biobanks (over 100,000 clinically annotated patient samples) with deep multi-omic and clinical data, running on the Frontier exascale supercomputer at Oak Ridge National Labs.

The clearest demonstration of the approach in the drug rescue context comes from a multi-arm Phase Ib oncology study involving 104 patients across multiple tumor types. NAi’s models, trained on tissue and blood-derived multi-omic data, identified biological signatures predicting response to BPM31510 — and BPGbio used those insights to prioritize glioblastoma multiforme (GBM) and pancreatic cancer as the most compelling indications. The causal framing is that if the model can distinguish “patients who happened to respond” from “patients who responded because of a specific biological mechanism,” the resulting biomarkers should be more robust in prospective validation.

BPGbio is further along clinically than many AI-native biotechs. The company has completed enrollment in a Phase 2b GBM trial, with topline results expected in Q3 2026, and sought FDA guidance in late 2025 for a potential expedited regulatory path in GBM. They have multiple Phase 2 clinical trials underway — making them, by their own account, one of the first companies worldwide to advance multiple Phase 2 programs developed using causal Bayesian AI.

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🔹 Others in the Neighborhood

The companies above aren’t the only ones in this territory. Several others touch the thesis from adjacent angles:

🔹 NOETIK trains AI models on massive datasets of paired pathology images and spatial transcriptomics to find hidden biological subtypes among trial participants and predict which patients will respond. The pairing of imaging and spatial transcriptomics is technically ambitious and could surface subgroups invisible to genomics-only approaches.

🔹 Ignota Labs (London) takes a complementary but distinct approach. As CEO Sam Windsor has noted, sometimes the drug just isn’t good enough and Ignota focuses on fixing fundamental safety issues in the chemistry itself to give failed drugs a second chance. Patient stratification and molecular optimization are different interventions for the same problem (the 90%+ failure rate), and both are needed.

🔹 Formation Bio (New York, valued at ~$1.7 billion) acquires stalled clinical-stage drugs and uses AI to run trials more efficiently, optimizing patient recruitment, protocol design, and site management. In November 2024, they launched Muse, an AI tool for clinical trial recruitment, in partnership with OpenAI and Sanofi. Formation’s overlap with the “drug rescue” thesis is real but less precise: they’re improving trial execution, not fundamentally reanalyzing who should be in the trial. Closer to operational arbitrage than to computational patient stratification.

🔹 Origent Data Sciences uses machine learning to build patient-level predictive models for disease progression, then identifies cohorts within failed trials where treatment effects can be demonstrated. Their ForecastOne platform is specifically designed for drug rescue in neurodegenerative diseases — a space where population heterogeneity is especially pronounced.

📜 The Roivant Precedent

The idea of finding value in pharma's abandoned assets isn't new.

Roivant Sciences, founded in 2014 by Vivek Ramaswamy, was built on the thesis that the pharmaceutical industry was full of abandoned assets that failed not because of efficacy problems but because of strategic deprioritization. They licensed shelved drugs, housed them in independent subsidiaries (”Vants”), and pushed them through development.

The model worked, sometimes. The most famous case, buying GSK’s Alzheimer’s candidate intepirdine for $5 million via Axovant, failed in Phase 3. But Roivant learned and evolved. The company has since grown to a ~$20 billion market cap by pivoting to a precision focus on immunology and inflammation, and its AI story now lives in VantAI (a spinout building the Neo-1 model for molecular glue design), not in clinical failure analysis.

Roivant’s original model was a financial and operational bet, spot undervalued assets, give them focused management, and move fast. It wasn’t a computational bet on finding hidden responder subgroups in trial data per se. The companies profiled above are making a different bet: that AI can extract signal from noise in ways that traditional biostatistics couldn’t.

Roivant CEO Matt Gline has been candid about his skepticism of AI drug discovery, arguing it faces a fundamental problem by solving only one or two of the roughly 150 hard problems in preclinical development. But it was aimed at de novo drug discovery, not at the more focused application of AI for patient stratification in existing clinical data.

⚠️ Still Open Questions

We need to be honest about the risks and limitations.

The p-hacking problem is real. Post-hoc subgroup analysis is one of the oldest and most dangerous tools in clinical research. If you slice a trial population enough ways, you will find a subgroup that responded, by chance. The key question for every company in this space is: can your AI-identified subgroups be validated prospectively? Retrospective signal discovery is table stakes. Prospective confirmation is where most of these approaches will succeed or fail.

Regulatory uncertainty. The FDA has frameworks for enrichment strategies and biomarker-driven trial designs, but there’s no well-trodden regulatory path for “we reanalyzed a failed trial with AI and found a responding subgroup, now we want to run a new trial in just those patients.” Each company is navigating this largely ad hoc. Lantern’s FDA interaction on the HARMONIC trial amendments is a positive signal, but one data point doesn’t make a precedent.

Small subgroups, small markets. Patient stratification is a precision medicine play. By definition, you’re narrowing the addressable population. Some subgroups will be commercially viable (Lantern’s L858R NSCLC population of 65,000–80,000 relapsed patients per year is meaningful). Others may be too small to justify the cost of a dedicated clinical program. The economics of drug rescue only work if the subgroup is big enough, or if the development cost is low enough, to justify the investment.

Data access and quality. These approaches are only as good as the data they’re trained on. Pathos has an enormous advantage through the Tempus relationship (200+ petabytes), but most failed trials sit in corporate vaults, and the patient-level data needed for subgroup reanalysis is rarely publicly available. Companies that can’t access high-quality, multimodal, longitudinal patient data are building on thin foundations.

The “drug just isn’t good enough” problem. As Ignota’s CEO, Sam Windsor pointed out, sometimes patient stratification isn’t the answer; the molecule itself has fundamental issues. A drug with genuine safety problems or an insufficient therapeutic window won’t be rescued by finding better patients for it. The companies building these AI platforms need to be disciplined about walking away from molecules that don’t warrant rescue.

Self-reported early data. Most of the clinical results we’ve seen so far — including Lantern’s LP-300 PFS data — are company-reported, from early-stage trials, in small patient numbers. This is expected at this stage of maturity, but we shouldn’t confuse preliminary signals with validated outcomes. The real test comes in registrational trials with pre-specified subgroups and independently adjudicated endpoints.

🔭 Looking Ahead

So is “rescuing failed drugs with AI” a category now?

The capital providers certainly believe so. Between Biossil ($70M), Pathos ($365M Series D, $1.6B valuation), Formation Bio ($600M+, $1.7B valuation), and Lantern (public, running clinical trials), there’s real money behind the thesis, from investors ranging from traditional life science VCs to Founders Fund and OpenAI.

But a category needs more than capital. It needs clinical proof points. Here’s what to watch:

Near-term (2026–2027):

• Lantern’s HARMONIC trial data in the narrowed L858R NSCLC subgroup. This is one of the most concrete tests of the thesis: a drug that was broadly tested, AI-identified a specific molecular subgroup, and a redesigned trial is now running in just that population. If LP-300 produces confirmatory data, it’s a powerful proof of concept for the entire space.

• Pathos’s pocenbrodib Phase 1b/2a data in mCRPC — particularly whether their biomarker-defined subgroups show differential response.

• Biossil’s first clinical readouts from any of its ten programs.

Medium-term (up to 2028 and beyond):

• Whether any AI-identified subgroup leads to a regulatory filing. This would be the true inflection point — a drug that failed in a broad population, succeeded in an AI-defined subgroup, and got approved for that subgroup.

• The maturation of multimodal foundation models in oncology (the AstraZeneca-Tempus-Pathos collaboration). If these models can reliably predict responders across tumor types, the implications extend far beyond drug rescue.

• Whether pharma companies begin systematically reanalyzing their own shelved assets with these tools, either internally or through partnerships. The volume of failed late-stage programs sitting in corporate vaults is enormous.

The open questions:

• Can retrospective AI-driven subgroup discovery produce biomarkers robust enough for prospective trial enrichment? This is the central scientific question.

• Will regulators create clearer frameworks for AI-informed trial redesign, or will each program remain a bespoke negotiation with the FDA?

• Is there a sustainable business model here, or will drug rescue remain a niche strategy for specific asset classes? The economics depend heavily on how cheaply you can acquire failed assets, how efficiently AI can identify the right subgroup, and how large that subgroup turns out to be.

These are early days. The tools are getting dramatically more powerful, including multimodal models, massive patient datasets, causal inference frameworks, and agentic AI systems. But the clinical validation is thin, with only a handful of companies running trials. None has yet produced the definitive proof point: a failed drug, rescued by AI-driven patient stratification, approved by regulators.

We think this is a space worth watching closely, because the underlying logic is sound, the unmet need is enormous (90%+ failure rates, billions in sunk R&D), and the technical capabilities are likely approaching what the problem demands. The next 18–24 months of clinical data will tell us whether this is a genuine new paradigm or an expensive lesson in the limits of computational biology.

As always, if you’re working in this space or watching it from the inside, we’d love to hear what you’re seeing. Leave a comment!

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The one insight: the field has converged on a pragmatic route to market — prove a mechanism in a recognized age-related disease, measure something that moves earlier than mortality, and treat longevity as the long-term upside. Aging itself is still not a regulatory indication.

Published in full on BioPharmaTrend.

What’s inside:

• Cellular reprogramming and rejuvenation: Altos Labs, Life Biosciences, NewLimit, Shift Bioscience, Retro Biosciences — and why some are starting in dermatology

• Senotherapeutics: who is clearing senescent cells, who is blunting their signalling, and what Unity Biotechnology’s liquidation means for the thesis

• The metabolic hub: mTOR inhibitors, metformin, GLP-1s, and the cardiometabolic companies

• Mitochondrial function, immune rejuvenation and the thymus, stem cell exhaustion, and inflammaging — with the companies and funding behind each

• Why several biotechs are running their first trials in companion dogs

• The capital and policy picture: longevity funds, ARPA-H’s PROSPR program, and the proposed 40.5% cut to the US National Institute on Aging

Parts 2 and 3 cover aging biomarkers and biological clocks, then consumer-facing longevity clinics.

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The one insight: agents are getting better at talking about science faster than they are getting better at doing it.

Published in full on BioPharmaTrend.

What’s inside:

• Stanford’s “Virtual Biotech” — 37,000 agents run in parallel, and what the analysis actually turned up

• The compute arms race at Lilly, Roche and Thermo Fisher, and why the hardware is running ahead of the named results

• What is genuinely in production: AstraZeneca’s honest account of what breaks, IQVIA’s 150-agent platform, Daiichi Sankyo, and the FDA’s own bumpy rollout

• Lab-in-the-loop: where agents meet real experiments, and the three conditions that have to align for it to work

• What doesn’t work — reliability, architectural narrowness, and why multi-agent debate produces an echo chamber rather than a check

• What can go wrong: the adversarial attack surface almost nobody is prepared for

• What to watch next: regulation, the first IND with agentic contributions, interoperability standards, and the talent shortage

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The one insight: synthetic control arms built from historical data have already supported label expansions and accelerated approvals. AI-generated individualized digital twins have not yet served as primary evidence in a completed approval. The distinction matters.

Published in full on BioPharmaTrend.

What’s inside:

• The four pressures pushing sponsors toward simulation: recruitment, Phase II attrition, rare disease populations, and the ethics of placebo arms

• How trial digital twins actually work, and how they differ from synthetic control arms and traditional external controls

• The commercial landscape — Unlearn.ai, Phesi, Medidata, ConcertAI — and the two approaches with real traction

• Simulation before trials begin: Aitia, VeriSIM Life, Orakl Oncology

• Four limitations the technology hasn’t solved, including a data-standards problem that predates it

• Where regulators stand: EMA qualification, FDA discussion papers, the EU’s Virtual Human Twins initiative, and why Japan and China are further behind

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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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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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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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Invasive & Minimally Invasive BCIs

Brain-computer interface (BCI) systems are being explored and used as a way to restore lost motor, speech, or sensory functions, particularly in patients with paralysis or neurodegenerative conditions. They work by placing electrodes on or in the brain to capture high-resolution neural activity, which is then translated into actions like moving a cursor, generating speech, or triggering stimulation.

Typically, BCIs include implanted pulse generators and wireless connections to external processors, which decode brain signals such as spikes or local field potentials from targeted brain areas, then use trained algorithms to translate those activity patterns into outputs such as cursor motion, text, or stimulation commands.

In 2025, several programs moved into multi-center or early pivotal territory:

• Neuralink extended its PRIME program into Great Britain with the GB-PRIME study at UCLH and Newcastle, evaluating the fully implantable N1 interface in patients with motor neuron disease and spinal cord injury, and reporting the first UK patient controlling a computer within hours after surgery. The same implant was used at home by ALS patient Brad Smith to control a motorized Insta360 webcam, demonstrating extended real-world use beyond cursor control.

• Paradromics received FDA IDE approval for its Connexus system to start the Connect-One early feasibility study, targeting speech restoration and computer control in people with severe paralysis via a high-bandwidth, fully implantable BCI. The Connect-One trial is designed around speech restoration as a primary endpoint rather than generic cursor control.

• Precision Neuroscience advanced its thin-film Layer 7 cortical interface. The 1,024-electrode subdural array, FDA-cleared as a temporary mapping device, was profiled in first human recipients as a minimally invasive, high-density platform that sits on the cortical surface rather than penetrating tissue.

• CorTec’s Brain Interchange BCI system reached first-in-human use in a stroke patient as a fully wireless, closed-loop implant capable of recording and stimulating cortex in real time, positioning it as a European competitor in implantable neuromodulatory BCIs.

• Synchron introduced an updated version of its endovascular Stentrode BCI that integrates Nvidia AI and the Apple Vision Pro headset to let people with severe paralysis control digital and physical environments using neural signals. Later, Synchron publicly demonstrated a person with ALS using its implanted Stentrode to control an iPad entirely by thought by converting neural motor-intent signals into native iPadOS inputs.

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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.

Nothing Lasts Forever

Aging is the gradual, time-dependent decline in the physiological functions required for survival and reproduction. Unlike age-related diseases (such as cancer or heart disease), the defining features of aging are shared by all individuals within a species.

As an integral part of life, aging has caused a multitude of philosophical disputes throughout history, tracing back to 350 BCE when Aristotle first tried to explain senescence, viewing it as a ‘natural illness’. However, conventional aging research started much later, in the 20th century.

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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 t…

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EvolutionaryScale emerged in 2023 after Rives, along with Tom Sercu and Sal Candido, left Meta’s AI protein group (FAIR) during the company’s “year of efficiency” (there are, again, plans to cut 600 AI jobs after a $14.3 billion Scale AI investment and hiring spree this summer). Backed by the likes of Amazon and Nvidia, the team raised $142 million to develop large-scale generative models for protein design and became known for the ESM family of protein language models (PLMs) trained directly on amino-acid sequences.

Its flagships, ESM3 and ESM Cambrian, extended this work to fully generative modeling of protein structure and function. ESM3, trained on 2.7 billion proteins, has already been used to design molecules like the novel green fluorescent protein variant, esmGFP, said to represent roughly 500 million years of natural evolution.

CZI’s Biohub folds this hire into its broader “virtual biology” plan, setting out four scientific challenges: building an AI-based model of the cell, advancing imaging, instrumenting inflammation, and using AI to reprogram the immune system, with the Virtual Immune System as one of the flagship projects. The ES team is brought in “to help advance this initiative.” In the VIS roadmap, the molecular-interactions axis explicitly calls for protein language models that “can learn the universal grammar of immune recognition and enable the rational design of novel receptors.”

With that, let’s step back and look closer at what protein language models are, what kinds of applications companies are building them for, and where pharma is already involved.

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Last year, OpenAI partnered with Formation Bio and Sanofi as well as signed agreements with Moderna, Eli Lilly; followed by a Thermo Fisher Scientific deal in 2025. At the same time xAI advertises Grok for Government with support for science and healthcare purposes, while DeepSeek gains adoption across Chinese hospitals.

Today we’ll look at LLMs entering biomedical workflows, examine what these systems can do in lab- and clinic-adjacent tasks, and how hybrid designs aim to mitigate common failure modes.

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