AI Models for Life Sciences in 2026AI Models for Life Sciences in 2026

Google DeepMind and Isomorphic Labs

DeepMind has the longest tenure in production biology of the six labs. AlphaFold 2 reset target validation in 2021, and AlphaFold 3 carried the work into protein-with-ligand and protein-with-nucleic-acid territory in May 2024, with roughly a 50% accuracy gain on protein-molecule interactions. The science is the strongest of the six labs. The hard part is buying it. AlphaFold Server is free for non-commercial use, and the AlphaFold 3 code plus request-gated model parameters went to academia in November 2024 under a non-commercial license. Commercial AF3-class capability flows through Isomorphic Labs, the DeepMind spinout that holds the pharma deal pipeline. Eli Lilly and Novartis signed at up to $3 billion combined in January 2024, Isomorphic raised $600 million in March 2025 led by Thrive Capital, Johnson & Johnson joined in a January 2026 research collaboration, and a $2.1 billion Series B closed in May 2026. Isomorphic's stated goal is to advance programs toward clinical development.

Around AlphaFold sits a wider biology stack:

• AlphaProteo (September 2024) designs novel protein binders for cancer and diabetes targets.
• AlphaGenome (June 2025) reads regulatory effects of mutations across DNA sequences up to a million bases and is free for non-commercial use.
• AlphaMissense scores pathogenicity of missense variants across the human genome.
• Med-Gemini is Gemini fine-tuned for medicine, scoring 91.1% on MedQA. Google presents it as research rather than a productized enterprise offering.

Best for: Teams whose primary bottleneck is structure prediction, target validation, or protein design. Less useful when the work is literature synthesis or regulatory drafting.

NVIDIA

NVIDIA does not compete on the model layer alone. It sells the infrastructure plus a curated catalog of biology models running on its hardware, which makes it the only one of the six labs whose business depends on every other lab succeeding.

The core stack is the BioNeMo Platform, a generative AI framework for drug discovery built around pre-trained models, NIM microservices for containerized inference, and reference Blueprints. BioNeMo runs on DGX Cloud and on AWS, GCP, and Azure. The Generative Virtual Screening Blueprint chains MSA-Search, OpenFold2, GenMol, and DiffDock for hit identification.

Around BioNeMo, NVIDIA wraps three other frameworks:

• MONAI handles medical imaging across CT, MRI, pathology, and endoscopy. Its components include MAISI for synthetic CT, VISTA-3D with 130 anatomical classes, and VISTA-2D for cellular imaging, with more than one million downloads.
• Parabricks accelerates genomics on GPU (BWA-MEM, GATK, DeepVariant), with HaplotypeCaller dropping from around 36 hours on CPU to around 25 to 35 minutes on GPU depending on the platform.
• Clara is the umbrella spanning BioNeMo for discovery, Holoscan for medical devices, Parabricks for genomics, and MONAI for imaging.

The pharma partnership list is the longest of any of the six labs:

• NVIDIA and Eli Lilly announced a co-innovation AI lab on January 12, 2026, valued at up to $1 billion over five years and built on BioNeMo plus the Vera Rubin architecture.
• Recursion took a $50 million strategic investment from NVIDIA in 2023 and now hosts its Phenom foundation model on BioNeMo.
• Amgen runs a DGX SuperPOD at deCODE Genetics in Iceland for training on human datasets.
• Genentech integrated BioNeMo into its lab-in-a-loop workflows.
• Novo Nordisk uses BioNeMo together with NIM and NeMo for agentic drug discovery through DCAI.

Access is multi-cloud (DGX Cloud, AWS, GCP, Azure) and the customer pays for compute, not per seat. This is the only platform among the six where the cost structure scales with workload rather than headcount.

Best for: Biotechs and pharma running high-volume computational workflows where GPU economics dominate the bill. The clearest fits are virtual screening, genomics pipelines, structural and imaging analysis at scale, and agentic discovery workflows.

Anthropic

Anthropic moved from horizontal LLM to a vertical pharma product on October 20, 2025, when it launched Claude for Life Sciences with five named pharma anchor customers and pre-built connectors into the systems pharma already runs.

Claude for Life Sciences is a bundle of MCP connectors and biomedical skills wrapped around the Claude Enterprise stack. Launch connectors covered Benchling for ELN and LIMS, 10x Genomics for single-cell and spatial analysis, PubMed, BioRender, Synapse.org, and Wiley Scholar Gateway. Medidata and ClinicalTrials.gov were added after launch. Anthropic's launch materials cite improvements in Claude Sonnet 4.5 on figure interpretation, computational biology, and protein understanding benchmarks. The biomedical capability comes from prompting, tools, and connectors layered on a general-purpose Claude rather than from specialized biology weights.

The five anchor customers at launch were Novo Nordisk, Sanofi, AbbVie, AstraZeneca, and Genmab. The headline case study sits with Novo Nordisk, where clinical study report drafting moved from 10+ weeks to around 10 minutes per report. Novo Nordisk started on Claude 3.5 Sonnet before the Life Sciences bundle existed. In May 2026, Bristol Myers Squibb signed a strategic agreement to roll Claude Enterprise plus Claude Code to more than 30,000 BMS employees across R&D, manufacturing, and commercial. In 2026, Anthropic also acquired Coefficient Bio for around $400 million, around 10 people largely drawn from Genentech computational biology, folded into the life sciences division.

Access is per-seat Claude Enterprise pricing with MCP connectors handling the system integrations. There are no specialized biology weights to license separately.

Best for: Teams whose dominant work is regulatory writing, clinical study reports, literature synthesis, protocol drafting, and translation across legacy lab data formats. Not the right pick when structure prediction or molecule generation is the bottleneck.

Microsoft

Microsoft has the deepest and longest-running life sciences stack of any cloud provider. Five years of Microsoft Research output sits alongside a healthcare model catalog in Azure AI Foundry, EHR-integrated voice products from the Nuance acquisition, and a 2019 Novartis alliance that has been extended multiple times.

The biomedical model catalog inside Azure AI Foundry covers most of Microsoft Research's biology output:

• BioGPT is a generative transformer pre-trained on around 15 million PubMed abstracts, state-of-the-art on biomedical relation extraction and QA at release.
• BiomedCLIP is a vision-language foundation model trained on PMC-15M, 15 million figure-caption pairs from PubMed Central.
• MedImageInsight, MedImageParse, and CXRReportGen are healthcare imaging foundation models covering embedding and classification, segmentation across X-ray, CT, MRI, ultrasound, dermatology, and pathology, and chest X-ray report generation. Mass General Brigham (MedImageInsight) and the University of Wisconsin-Madison (CXRReportGen) are cited customers.
• TamGen is a target-aware molecule generator published in Nature Communications in 2024, demonstrated on TB ClpP protease with 14 candidate compounds and a lead IC50 of 1.9 μM.
• Hist-AI handles pathology and ECG-FM handles electrocardiogram analysis.

The pharma partnerships are older and deeper than at most of the other labs. Novartis signed an AI Innovation Lab alliance with Microsoft in October 2019 and has extended it multiple times since, covering generative chemistry, image segmentation for cell and gene therapy, and personalized treatment for macular degeneration. Paige.AI partnered with Microsoft in January 2023, expanded in September 2023, and built Virchow2 and Virchow2G, the latter a 1.8 billion parameter pathology foundation model trained on 3 million slides using Microsoft supercomputing.

The clinical and EHR layer is what no other lab on this list offers:

• Dragon Copilot launched at HIMSS in March 2025 as a unified voice AI assistant from the Nuance acquisition stack, covering ambient documentation, dictation, and generative summarization. It was extended to nurses in October 2025.
• Healthcare Agent Orchestrator and Copilot Studio provide healthcare-specific templates with EHR connectors.
• Microsoft Fabric Healthcare data solutions, generally available since 2024, handle FHIR ingestion, OMOP transformations, DICOM imaging pipelines, and SDOH datasets.

Foundry deployment is Azure-only. Some underlying models also ship via Hugging Face, GitHub, or partner provider channels, but customers running them through Microsoft commit to Azure spend and pull from the Foundry catalog. The strongest fit is in regulated environments already cleared on Azure's existing compliance posture.

Best for: Pharma and clinical health systems already on Azure. Microsoft is the only lab on this list with a complete clinical voice, EHR, healthcare imaging, data pipeline, and biomedical model stack under one cloud account.

Meta and EvolutionaryScale

Meta's biology work continues through EvolutionaryScale, founded in 2023 by former members of Meta's FAIR ESM team. The protein language models in this lineage (ESM2, ESM3, ESMFold) are the most-used open biology models in the world, and the open-weight access route is what separates Meta's line from every other lab on this list.

ESM2 and ESMFold, released in 2022, gave the field a protein language model and a structure predictor that works from sequence alone, faster than AlphaFold 2 with some accuracy tradeoffs. The ESM Metagenomic Atlas, built using ESMFold, has more than 600 million predicted structures. ESM3 was released by EvolutionaryScale in June 2024 as the first generative model jointly reasoning over protein sequence, structure, and function, trained on 2.78 billion proteins. The launch demo generated a novel green fluorescent protein roughly equivalent to 500 million years of evolution. EvolutionaryScale raised over $142 million in seed funding, with Amazon and NVIDIA among the backers.

Access splits across the lineage. ESM2 and ESMFold are fully open weights through the original Meta GitHub repository. ESM3 is hosted on AWS, with only a smaller variant available on Hugging Face under a non-commercial license. EvolutionaryScale, not Meta, is the active developer.

Best for: Teams that need open-weight protein models for in-house training, fine-tuning, or air-gapped deployment. Particularly attractive to academic groups and biotechs holding proprietary protein data they are unwilling to send to a third-party API.

OpenAI

OpenAI ships into pharma as a generalist with a recently added biology-specific entry. The strategy combines ChatGPT Enterprise deployments, selective scientific partnerships, a healthcare benchmark, and, as of April 2026, GPT-Rosalind, OpenAI's first dedicated life sciences model.

The public enterprise deployments to date:

• Moderna started in April 2024, with ChatGPT Enterprise rolled across around 3,000 employees and more than 750 custom GPTs built internally, including a Dose ID tool for clinical-trial dose review and analytics.
• In May 2024, Sanofi, Formation Bio, and OpenAI announced a three-way collaboration to fine-tune OpenAI models on Sanofi's proprietary data and build purpose-built AI agents for drug development.
• Color Health released a GPT-4o-based cancer copilot in 2024 that builds patient-specific cancer screening and workup plans for clinicians, validated with the UCSF Helen Diller Family Comprehensive Cancer Center. Color reports four times more identified missing labs and imaging compared to status quo, and a shift from weeks to around 5 minutes to surface care gaps.

Before GPT-Rosalind, OpenAI's closest biology-specific work was GPT-4b micro, released in January 2025 through a Retro Biosciences collaboration. GPT-4b micro is a GPT-4o-derived model specialized for protein engineering, used to redesign the Yamanaka factors (Sox2 and Klf4) with a reported 50 times improvement in pluripotency marker expression and a reduction in iPSC generation time from around 3 weeks to around 1 week. GPT-4b micro is a research collaboration rather than a generally available product.

HealthBench, released in May 2025, is OpenAI's open-source healthcare benchmark, built from 5,000 multi-turn conversations annotated by 262 physicians across 60 countries with around 48,562 rubric criteria. OpenAI uses HealthBench to position o3 and GPT-4.1 nano as healthcare-grade.

Access is ChatGPT Enterprise per seat and the OpenAI API per token, with GPT-Rosalind as the dedicated life sciences entry on the price sheet.

Best for: Pharma and biotech where the dominant use case is enterprise productivity (literature review, document drafting, internal copilots) and the buyer wants a generalist model with the widest reach across third-party tools.

Side-by-side comparison of AI tools for life sciences

The six labs differ across flagship offering, specialized biology model, access route, pricing structure, and target buyer. The table below summarizes the comparison in one view.

How to integrate a foundation model into a LIMS or ELN

Buyers ask this question after they pick a model, and the answer is the same regardless of which lab they picked. The data layer is the harder problem. Foundation models are useless without clean, structured input, and biotechs consistently underspend on the data substrate and overspend on the model itself. We covered the upstream version of this in our analysis of lab software data lock-in, which is the form the problem takes before any AI gets near the stack.

The integration lift varies by lab. Anthropic Claude for Life Sciences offers the lowest lift because the pre-built MCP connectors cover Benchling, 10x Genomics, PubMed, and most of what pharma already runs, but it ties the buyer to Claude. NVIDIA BioNeMo is API-first and runs against the buyer's data, which means higher integration lift but more flexibility on what the model sees. Microsoft Azure AI Foundry has the lowest lift if the buyer is already on Azure and the highest if they are not. Google DeepMind's AlphaFold Server is fine for one-off predictions, but production use needs a wrapper, and Med-Gemini ties the buyer to Google Cloud. Meta and EvolutionaryScale's ESM3 sits at the highest integration lift because the buyer is doing everything in-house, but it also gives full control and runs in the buyer's own VPC. OpenAI is API-first with a growing third-party tool layer, and the integration work falls on the buyer.

Regardless of which lab the buyer picks, four things have to work before any of this lands in production:

1. Instrument and ELN data flow into a structured store the model can read
2. An audit trail captures every input, every model output, and every human review
3. Validation evidence sits beside the output for regulated use cases under 21 CFR Part 11 and EU Annex 11
4. A model-to-human handoff is built in, and no model output goes to a regulator without a sign-off step

Picking the model is the small part. Wiring it into a LIMS, ELN, and the broader data pipeline is where time and budget actually go. Our broader take on adoption patterns sits in the state of AI in biotech 2026, which covers the pilot-to-production gap from the data side.

Deploying one of these models in a regulated lab? CodePhusion builds custom LIMS, ELN, and data infrastructure for biotechs running AI in production. get in touch if you want to talk through the integration.

Conclusion

The model layer commoditized faster than anyone predicted. Six labs now ship usable biology offerings, with deals running into the billions and the first AI-designed Isomorphic programs heading toward clinical development. The differentiator has moved from access to a frontier model to whether a biotech can wire that model into a LIMS, ELN, and data pipeline without breaking validation. The next 24 months will reward the biotechs that put integration budget ahead of model budget.

Frequently Asked Questions

What does OpenAI offer for life sciences and drug discovery?

OpenAI sells ChatGPT Enterprise and the OpenAI API into pharma and biotech, and in April 2026 introduced GPT-Rosalind, its first dedicated life sciences model. Notable case studies include Moderna (around 3,000 employees on ChatGPT Enterprise), the Sanofi and Formation Bio collaboration, Color Health's cancer copilot, and Retro Biosciences (GPT-4b micro for protein engineering). HealthBench is OpenAI's open-source healthcare benchmark.

What is Claude for Life Sciences used for?

Claude for Life Sciences is Anthropic's October 2025 vertical product for pharma, built on Claude Enterprise with MCP connectors into Benchling, 10x Genomics, PubMed, BioRender, Synapse.org, Wiley Scholar Gateway, and other systems pharma already runs. Medidata and ClinicalTrials.gov were added after launch. Anchor customers at launch were Novo Nordisk, Sanofi, AbbVie, AstraZeneca, and Genmab. It is best suited to regulatory writing, clinical study reports, and literature synthesis.

What is NVIDIA BioNeMo and who is it for?

NVIDIA BioNeMo is a generative AI framework for drug discovery, with pre-trained biology models, NIM microservices for containerized inference, and reference Blueprints such as the Generative Virtual Screening Blueprint. BioNeMo runs on DGX Cloud and on AWS, GCP, and Azure. It is best for biotechs and pharma running high-volume computational workflows where GPU economics dominate the bill.

AlphaFold 3 vs ESMFold vs RoseTTAFold, which should you use?

AlphaFold 3 has the highest accuracy on protein-with-ligand and protein-with-nucleic-acid structures, with academic weights and an Isomorphic Labs commercial path. ESMFold is open weights and faster, which makes it the right pick when fine-tuning on proprietary data or running an in-house pipeline. RoseTTAFold is open-source and competitive on certain protein families. The right pick depends on data residency, throughput, and whether commercial use is in scope.

How do OpenAI, Anthropic, and Google DeepMind compare for biotech?

OpenAI sells enterprise productivity through ChatGPT Enterprise and added GPT-Rosalind, its first biology-specific model, in April 2026. Anthropic ships Claude for Life Sciences with MCP connectors aimed at regulatory writing and clinical reporting. Google DeepMind ships the deepest specialized portfolio for structure prediction and target validation, with commercial use routed through Isomorphic Labs. The three answer different buyer questions and the picks are not interchangeable.

Does Microsoft have a life sciences AI model?

Yes. The Azure AI Foundry catalog includes BioGPT (biomedical text), BiomedCLIP (biomedical vision-language), TamGen (target-aware molecule generation), MedImageInsight and MedImageParse for imaging, CXRReportGen for chest X-ray reports, and Virchow2G (1.8 billion parameter pathology model). Microsoft also ships Dragon Copilot for clinical voice and Microsoft Fabric Healthcare for FHIR, OMOP, and DICOM pipelines. Foundry deployment is Azure-only, though some underlying models also ship via Hugging Face, GitHub, or partner provider channels.

What does Meta offer for biotech?

Meta's biology work continues through EvolutionaryScale, founded in 2023 by former members of Meta's FAIR ESM team. The ESM family of protein models (ESM2, ESM3, ESMFold) is the most-used open biology lineage in the world. ESM2 and ESMFold ship as open weights. ESM3 is a generative model trained on 2.78 billion proteins, hosted on AWS, with only a smaller variant available on Hugging Face under a non-commercial license.

How do you integrate a foundation model into a LIMS or ELN?

Picking the model is the smaller half of the problem. Production integration requires instrument and ELN data flowing into a structured store the model can read, an audit trail covering every input and output, validation evidence under 21 CFR Part 11 and EU Annex 11, and a model-to-human handoff step before any output reaches a regulator. The data substrate matters more than the model choice.

Last updated: May 28, 2026