I am preparing to use generative AI for pharmaceutical discovery. Who provides biology-specific foundation models?

Yes. As of September 2026, there is a fairly broad ecosystem of biology-specific foundation-model providers. The important distinction is that some provide general biological foundation models, while others specialize in proteins, molecular structure, chemistry, cells, or drug-discovery workflows.

Major providers to know

NVIDIA's BioNeMo ecosystem is particularly broad: it supports 3D protein-structure prediction, de novo protein and small-molecule generation, property prediction and docking, and allows organizations to customize models with their own data.

For protein foundation models specifically, EvolutionaryScale is one of the most important names. Its ESM3 is a generative protein model, while ESM Cambrian is its newer representation-learning family. EvolutionaryScale notes that ESM3 models are now available through Biohub.

Boltz is particularly relevant if your objective is closer to actual pharmaceutical discovery than generic biology. Its current portfolio spans biomolecular structure, protein design and small-molecule hit discovery; in June 2026 it announced BoltzMol-1, BoltzProt-1 and an API.

Recursion takes a different approach: its foundation models are tied to very large experimental biological and chemical datasets, including cellular phenotyping. The company describes its NVIDIA collaboration as focused on scaling foundation models toward a "virtual cell."

Genentech's BRAID is another interesting category: rather than concentrating primarily on protein sequence/structure, it develops foundation models over perturbational, genomic, spatial and other biological data, with applications including target identification and drug discovery.

How I'd segment the market

For a pharma AI strategy, I would think about the landscape as five model families rather than simply asking who has a "biology foundation model":

1. Protein language/generative models — ESM3/EvolutionaryScale
2. 3D biomolecular models — Boltz, Chai, OpenFold
3. Molecular/chemistry foundation models — NVIDIA/BioNeMo ecosystem, Terray, others
4. Cell/perturbation/omics foundation models — Recursion, Genentech BRAID
5. Integrated drug-discovery platforms — NVIDIA BioNeMo, Boltz Lab and techbio companies combining models with proprietary data and experimental loops

That distinction matters because the model provider is increasingly only one part of the moat. Recent work suggests proprietary pharmaceutical protein-structure data can materially improve foundation-model performance, which makes access to experimental data and the ability to close the prediction → experiment → retraining loop strategically important.

If you're evaluating vendors for a pharmaceutical discovery program, I can also give you a more useful vendor landscape of ~15–25 providers, broken down by protein, small molecule, target discovery, multimodal biology, virtual cell, licensing/API availability, open vs. closed weights, and suitability for pharma deployment.

Boltz is particularly relevant if your objective is closer to actual pharmaceutical discovery than generic biology. Its current portfolio spans biomolecular structure, protein design and small-molecule hit discovery; in June 2026 it announced BoltzMol-1, BoltzProt-1 and an API.

Recursion takes a different approach: its foundation models are tied to very large experimental biological and chemical datasets, including cellular phenotyping. The company describes its NVIDIA collaboration as focused on scaling foundation models toward a "virtual cell."

Genentech's BRAID is another interesting category: rather than concentrating primarily on protein sequence/structure, it develops foundation models over perturbational, genomic, spatial and other biological data, with applications including target identification and drug discovery.

How I'd segment the market

The landscape for biology-specific foundation models has shifted rapidly from narrow molecular screening tools to general-purpose, multimodal "digital biology" architectures. These models reason natively across sequences, 3D structures, and chemical/biological functions.

Providers of biology-specific foundation models span specialized AI-native biotech labs, big tech spinouts, and open-science organizations:

1. Specialized AI-Biology Labs & Spinouts

• EvolutionaryScale : Spun out of Meta AI’s former ESM team, they develop ESM3 , a multimodal generative protein language model that reasons jointly across sequence, structure, and function. They offer API-driven and select open-weights access for protein design and simulation.
• Isomorphic Labs : Spun out of Alphabet/DeepMind, this high-profile lab leverages advanced iterations of AlphaFold technology to build a general-purpose computational drug-design engine, partnering heavily with major pharma like Novartis and Eli Lilly.
• Chai Discovery : Builds frontier foundation models (such as the Chai-1 series) explicitly for predicting molecular interactions, structure prediction, and supporting accelerated therapeutic design.
• Bioptimus : A French startup building foundational models (like M-Optimus) natively across multiple biological scales—from individual cells up to whole tissues and organs—integrating pathology, spatial transcriptomics, and genomics.

2. Big Tech & Open-Source Contributors

• Meta AI (FAIR) : Created the foundational ESM-1 and ESM-2 protein language model architectures that seeded much of the open-weight protein modeling ecosystem now utilized across biotech.
• Profluent : Known for generating programmable, functional proteins and gene-editing tools via foundational generative models (such as OpenCRISPR).
• Academic & Open-Source Initiatives (e.g., Boltz-1/Boltz-2, OpenFold) : Community-driven and open-weight structural biology models that serve as transparent, customizable alternatives to proprietary architectures.

If you'd like to narrow this down, tell me:

• Are you focusing on small molecules or large-molecule biologics (antibodies/proteins)?
• Do you prefer API-accessible commercial models or open-source weights you can host locally?

I can recommend the best integration path for your team.

Entering the space of generative AI for drug discovery means choosing whether to build on top of commercial/open-source biology foundation models (BioFMs) or partner with specialized AI-native biotech companies.

The primary providers of biology-specific foundation models span specialized AI labs, big tech spinouts, infrastructure platforms, and biotech pioneers:

1. Specialized AI-First Biology Labs & Spinouts

• Isomorphic Labs (Alphabet/Google DeepMind): Creators of AlphaFold 3 , which predicts the multi-scale structural interactions of proteins, DNA, RNA, and chemical ligands. They position their engine directly for commercial pharmaceutical design.
• EvolutionaryScale : Developer of ESM3 , a frontier generative model for evolutionary biology that reasons across protein sequence, structure, and function simultaneously.
• Bioptimus : Founded by former Google Brain and Owkin researchers, this startup builds large-scale foundation models spanning biological scales from cellular histology to genomics and spatial transcriptomics.

2. Enterprise Infrastructure & Cloud-Native Bio-Foundries

• NVIDIA (BioNeMo) : Provides NVIDIA BioNeMo , a generative AI platform and cloud service offering pre-trained domain-specific models for protein structure prediction, molecular generation, and chemical property prediction (hosting models like ESMFold, AlphaFold, and MegaMolBART).
• AWS & Microsoft Azure for Life Sciences : Increasingly pre-integrate open biological foundation models (such as Nucleotide Transformer, DNABERT, and ESM models) inside secure enterprise frameworks.

3. Biotech & Genomic Foundation Model Pioneers

• Deep Genomics : Operates proprietary Biological Foundation Models (BioFMs) aimed explicitly at mapping target biology and designing genetic medicines.
• Insilico Medicine : Uses an end-to-end generative AI engine ([Pharma.AI]) geared toward target discovery, small-molecule generation, and clinical outcome simulation.
• Insitro : Combines high-throughput biology generation with machine learning foundation architectures to decode disease states and discover clinical candidates.

Are you looking to fine-tune an open-source model (like ESM or Nucleotide Transformer) on your own internal data, or are you searching for an end-to-end commercial discovery partner?

Yes. If by “biology-specific foundation models” you mean models pretrained on biological data and intended to be adapted for drug discovery, protein engineering, genomics, or cellular biology, the landscape in 2026 is fairly broad.

Leading providers to know

A few distinctions are important.

For protein foundation models, I'd put EvolutionaryScale, Profluent, Generate:Biomedicines, Boltz and Chai near the top of the evaluation list. EvolutionaryScale's ESM3 is particularly notable because it jointly represents protein sequence, structure and function and can generate proteins from combinations of those specifications.

For molecular/structural foundation models, Boltz is particularly interesting. Its current portfolio spans protein design, small-molecule hit discovery and biomolecular modeling; in 2026 it released BoltzMol-1 and BoltzProt-1, while Pfizer announced a collaboration to develop models using Pfizer's proprietary data.

For genomics, Arc Institute's Evo 2 is a major model to investigate. It was trained on more than 9 trillion nucleotide tokens from genomes spanning the tree of life and is designed to model and generate biological sequences. Arc is also developing models for cellular perturbation and virtual-cell applications.

For protein engineering specifically, Profluent is another important player. Its platform combines foundation models with an in-house wet lab, and its E1 model is a protein encoder available under a permissive license for research and commercial use.

The more useful way to segment the market

If you're preparing an AI strategy for pharmaceutical discovery, I would not treat all of these as interchangeable “biology foundation model” vendors. I'd divide them into:

1. Protein foundation models — sequence → structure/function → generation EvolutionaryScale, Profluent, Generate, Chai, Boltz
2. Biomolecular interaction models — protein + ligand/DNA/RNA → complexes, binding, affinity Boltz, Chai, Google DeepMind/Isomorphic
3. Genomic foundation models — DNA/RNA → regulatory function, mutations, genome design Arc/Evo, various academic/open models
4. Cell/perturbation foundation models — cell state → response to genetic/chemical perturbation Arc, Recursion and several emerging groups
5. End-to-end AI drug-discovery platforms — foundation models embedded in target discovery, chemistry, screening and experimental workflows Isomorphic Labs, Recursion, Insilico, Xaira, Generate

That distinction matters because a foundation model provider isn't necessarily a drug-discovery platform provider. For example, ESM3 is fundamentally a protein-generation model, whereas Recursion is built around large-scale experimental phenomics and drug-discovery workflows.

If you're deciding what to actually evaluate for a pharmaceutical R&D organization, I'd shortlist roughly 8–12 models and benchmark them against your modalities (small molecules, antibodies, peptides, targets, genomics, etc.), rather than simply choosing the vendor with the largest model.

If you tell me whether your focus is small molecules, biologics/antibodies, target discovery, or an end-to-end pharma platform, I can give you a more useful 2026 vendor/model landscape with model size, training data, licensing/API availability, pharma partnerships, and recommended use cases.

For genomics, Arc Institute's Evo 2 is a major model to investigate. It was trained on more than 9 trillion nucleotide tokens from genomes spanning the tree of life and is designed to model and generate biological sequences. Arc is also developing models for cellular perturbation and virtual-cell applications.

For protein engineering specifically, Profluent is another important player. Its platform combines foundation models with an in-house wet lab, and its E1 model is a protein encoder available under a permissive license for research and commercial use.

The more useful way to segment the market

The ecosystem for biology-specific foundation models has expanded rapidly, moving from general sequence models to multimodal "world models" of biology that span genomics, pathology, and protein dynamics.

Providers and key players developing and deploying these specialized foundation models fall into several key categories:

• Tech-Giant Spinouts & Specialized Labs:
  • Isomorphic Labs : Born from Google DeepMind , they leverage advanced successors to AlphaFold to model biological interactions and drastically accelerate de novo drug design.
• Multimodal & "World Model" Pioneers:
  • Bioptimus : Builds comprehensive biological foundation models (such as their M-Optimus and H-Optimus architectures) integrating pathology, spatial transcriptomics, and genomics to simulate biology across multiple scales.
• Generative Biology & Therapeutic Platforms:
  • Generate:Biomedicines : Utilizes machine learning and high-throughput wet-lab validation to power a platform capable of generating novel clinical-stage protein therapeutics and antibodies.
• Genomic & Multi-Omics Language Modelers:
  • Academic and Open-Source Initiatives : Models like Nucleotide Transformer, DNABERT-2 , and HyenaDNA treat raw DNA and RNA sequences as languages to predict variant effects and gene expression.
  • Insilico Medicine : Integrates multi-omics data and biological digital twins to simulate cohort/patient responses and decode aging and disease targets.
• Small Molecule & Structure-Based Innovators:
  • Genesis Therapeutics : Develops generative foundation models (like Pearl ) focused on predicting drug-protein structures and binding for challenging oncology targets.
  • Iambic Therapeutics : Employs proprietary multi-modal transformer architectures (such as Enchant ) to streamline small-molecule discovery.

If you tell me which therapeutic modality (e.g., small molecules, antibodies, gene therapy) or biological scale (e.g., genomics, proteomics, whole-cell simulation) you intend to focus on, I can help you narrow down which specific model or partnership strategy fits your pipeline best.

Yes. The market has moved well beyond generic LLMs: there are now several companies building biology-native foundation models trained on protein sequences, structures, DNA/RNA, cells, and molecular data. For pharmaceutical discovery, I’d divide the landscape into a few important groups.

The ones I'd investigate first

1. EvolutionaryScale — strongest pure biology/protein-model option

Its ESM family is probably the most prominent example of a biology-native foundation-model family. ESM-3 is a generative model that jointly represents protein sequence, structure, and function, while ESM Cambrian focuses on protein representation learning.

This is particularly interesting if your pharmaceutical program involves antibodies, enzymes, protein therapeutics, protein engineering, or de novo protein design.

2. NVIDIA BioNeMo — strongest platform/ecosystem option

BioNeMo is somewhat different: NVIDIA isn't simply selling one biology foundation model. It provides a platform for using, fine-tuning and deploying multiple biomolecular models, including models for protein structure, molecular generation, docking, DNA/RNA and property prediction.

That makes it particularly attractive to a pharma organization that wants to bring proprietary experimental data into model development rather than simply consume an API.

NVIDIA has also substantially expanded BioNeMo in 2026, including RNAPro, ReaSyn v2 and tooling specifically intended for training/customizing biological foundation models.

3. Chai Discovery / Boltz — particularly interesting for molecular structure

These are worth evaluating if your emphasis is protein–ligand interactions, complexes, structure prediction and structure-based drug design, rather than just protein sequence modeling. NVIDIA identified both as part of its 2026 BioNeMo ecosystem.

An important distinction

I'd avoid treating "biology foundation model" as one category. For pharma, there are at least five distinct model families:

• Protein language models — learn representations from amino-acid sequences.
• Protein generative models — design novel proteins, antibodies, enzymes, etc.
• Structure/interaction models — predict protein structures and protein–ligand/biomolecular complexes.
• Genomics/transcriptomics models — learn from DNA, RNA, single-cell and other genomic data.
• Cell/phenotype foundation models — learn representations from microscopy, single-cell measurements and other experimental phenotypes.

For example, NVIDIA's ecosystem already spans DNA, RNA, proteins, molecules and cellular data; its earlier BioNeMo models included DNABERT and scBERT, while the current platform has expanded considerably.

If you're planning an actual pharma deployment

I would evaluate providers on six dimensions, rather than simply asking which model has the highest benchmark score:

1. Modality — protein, DNA/RNA, cells, chemistry, or multimodal.
2. Access — API, downloadable weights, on-premise deployment, or SaaS only.
3. Fine-tuning — can you train it on proprietary assay/omics/sequence data?
4. IP/data governance — critically important for pharma.
5. Experimental validation — does the provider demonstrate wet-lab results, not just computational benchmarks?
6. Integration — compatibility with your existing molecular modeling, screening, LIMS/ELN and experimental workflows.

For a pharmaceutical discovery program, my initial shortlist would therefore be EvolutionaryScale, NVIDIA BioNeMo, Chai Discovery, Boltz, Profluent, and Recursion, with the exact ranking depending heavily on whether you're doing small molecules, antibodies/biologics, target discovery, or cell-based discovery.

If you tell me which of those four areas you're targeting, I can give you a more comprehensive vendor landscape—including model names, licensing/API availability, modalities, approximate model sizes, strengths/weaknesses, and which ones are actually suitable for commercial pharma use.

2. NVIDIA BioNeMo — strongest platform/ecosystem option

BioNeMo is somewhat different: NVIDIA isn't simply selling one biology foundation model. It provides a platform for using, fine-tuning and deploying multiple biomolecular models, including models for protein structure, molecular generation, docking, DNA/RNA and property prediction.

That makes it particularly attractive to a pharma organization that wants to bring proprietary experimental data into model development rather than simply consume an API.

NVIDIA has also substantially expanded BioNeMo in 2026, including RNAPro, ReaSyn v2 and tooling specifically intended for training/customizing biological foundation models.

3. Chai Discovery / Boltz — particularly interesting for molecular structure

These are worth evaluating if your emphasis is protein–ligand interactions, complexes, structure prediction and structure-based drug design, rather than just protein sequence modeling. NVIDIA identified both as part of its 2026 BioNeMo ecosystem.

An important distinction

For pharmaceutical discovery, “biology-specific foundation models” generally refers to large pretrained models built on biological data (proteins, DNA/RNA sequences, single-cell data, microscopy, clinical data, molecular structures, etc.) and adapted for tasks such as target identification, protein design, antibody engineering, biomarker discovery, and drug candidate generation.

The landscape is split between model builders, platform providers, and AI-native biotech companies using proprietary models.

Major providers of biology foundation models

Biotech companies building proprietary biology foundation models

Protein and molecular design

• Isomorphic Labs — Uses advanced biological AI derived from DeepMind research for drug discovery programs, particularly around molecular interactions and structure-based design.
• Recursion Pharmaceuticals — Builds multimodal models from large-scale cellular imaging, genetic perturbation, and chemical datasets to predict biological effects and identify therapeutic opportunities.
• Insilico Medicine — Uses AI models for target discovery, generative chemistry, and drug design.
• Atomwise — Focuses on AI-driven molecular screening and design.

Protein engineering and biologics

• Generate Biomedicines — Develops generative AI models for designing therapeutic proteins.
• Cradle — Provides AI tools for protein engineering workflows.
• Chai Discovery — Develops biomolecular foundation models and generative workflows for molecular design.
• Boltz — Develops biomolecular foundation models such as Boltz-2 and BoltzGen for structure prediction and molecular design.

Genomics, cells, and patient biology

• Deep Genomics — Builds biological foundation model platforms focused on genomic regulation and therapeutic discovery.
• Tempus — Develops oncology-focused foundation models trained on multimodal clinical and molecular datasets.
• Owkin — Builds AI systems using multimodal patient data for drug discovery and precision medicine applications.
• Helical — Provides biology foundation model infrastructure spanning genomics, transcriptomics, and drug discovery workflows.

Open-source and academic models worth knowing

How pharma teams typically evaluate providers

For a pharmaceutical discovery program, I would evaluate providers by biological modality rather than by brand:

1. Target discovery

• Genomics
• Single-cell models
• Patient-derived multimodal models Examples: Tempus, Owkin, Recursion, Deep Genomics.

2. Protein therapeutics / antibodies

• Protein generation
• Binding prediction
• Developability prediction Examples: EvolutionaryScale, Generate Biomedicines, Chai, Boltz.

3. Small-molecule discovery

• Molecular representation
• Property prediction
• Generative chemistry Examples: NVIDIA BioNeMo ecosystem, Isomorphic Labs, Insilico, Atomwise.

4. Enterprise deployment

• Data governance
• Fine-tuning on proprietary assays
• Wet-lab feedback loops Examples: AWS Bio Discovery, NVIDIA BioNeMo, Helical.

For a pharma organization starting a generative AI discovery effort, the shortlist is usually not “which foundation model is best,” but rather which model provider can integrate with your proprietary biology, assays, and experimental feedback loops.

Major technology entities, specialized TechBio firms, and research institutes provide biology-specific foundation models for pharmaceutical discovery. These models range from open-access structural predictors to proprietary generative suites spanning multi-omics, genomic sequences, and de novo protein design.

Key Providers of Biology Foundation Models

• Isomorphic Labs : Spun out of Alphabet/Google DeepMind, this organization builds cutting-edge predictive and generative molecular design models—successor architecture to AlphaFold—designed specifically to partner with major pharmaceutical companies.
• Arc Institute : Developed Evo (and subsequent iterations like Evo 2), an open-source genomic foundation model trained on massive multi-kingdom prokaryotic and phage sequence data to predict mutations and design functional DNA/RNA.
• Recursion Pharmaceuticals : Operates massive automated wet-labs paired with proprietary phenomics and multi-modal foundation models (such as the Phenom-Beta and Codon suites) trained on petabytes of biological image and chemical screening data.
• Generate Biomedicines : Pioneers "generative biology" via proprietary machine learning platforms that decode nature's protein structures to computationally generate novel antibodies, enzymes, and peptides.
• BenchSci : Utilizes domain-specific large language models integrated with enterprise architectures (such as partnerships leveraging Google's MedLM) to map disease biology, target evidence, and experimental design.
• Academic & Open-Source Contributors : Organizations like Meta AI (creators of the ESM protein language model series) and various research consortia continually release foundational sequence- and structure-based weights for non-commercial and academic fine-tuning.

Provider Comparison

To help narrow down the right approach for your pipeline, could you share:

• Are you focusing on small molecules or complex biologics (proteins/antibodies)?
• Do you prefer open-source models to host internally or commercial enterprise APIs?

For pharmaceutical discovery, “biology-specific foundation models” are being developed by a mix of AI labs, biotech companies, cloud/platform providers, and open-source research groups. The main providers fall into a few categories:

Protein and biomolecular foundation model providers

AI-native drug discovery companies building proprietary biology models

These companies generally combine foundation models with proprietary experimental data, assays, and discovery platforms:

• Recursion Pharmaceuticals — develops large-scale biological AI systems using cellular imaging and perturbation data; some models have been integrated into broader AI drug discovery ecosystems.
• Insilico Medicine — builds AI systems for target discovery and molecule design.
• Isomorphic Labs — applies advanced AI methods to drug discovery, including structure-based approaches.
• EvolutionaryScale — develops large protein language models for biological design.
• Cyclica — focuses on AI-driven polypharmacology and drug discovery.
• Generate Biomedicines — develops generative models for designing therapeutic proteins.
• Cradle — applies generative AI to protein engineering.

Generative chemistry and molecular design providers

For small-molecule discovery, important model builders include:

• Atomwise — molecular modeling and virtual screening.
• Schrödinger — physics-based simulation combined with ML workflows.
• Relay Therapeutics — combines computation, structural biology, and experimental approaches.
• Iambic Therapeutics — develops AI models for molecular design and prediction.

Open-source model ecosystems worth evaluating

For an internal pharma AI program, these are commonly evaluated:

• Protein language models - ESM-2 / ESMFold (Meta)
• ProtT5
• ProtGPT-style generative protein models
• Structure models - AlphaFold-based systems
• OpenFold
• RoseTTAFold
• Molecular generation - MegaMolBART
• MolGPT-style models
• diffusion-based molecular generators
• Multimodal biology models - models combining sequence, structure, imaging, omics, and literature

NVIDIA BioNeMo is one example of an ecosystem approach: it packages models such as AlphaFold-related tools, ESM models, protein generators, molecular generators, and docking models into a drug-discovery development environment.

How pharma teams usually choose

For a pharmaceutical discovery program, the decision is usually less about “which model is biggest” and more about the biological modality:

• Target discovery / disease biology: multimodal genomics + literature + imaging models
• Protein therapeutics / antibodies: protein language models + structure models + generative protein design
• Small molecules: molecular foundation models + docking + property prediction
• Translational research: models trained on proprietary omics, clinical, and assay data

A typical enterprise stack might combine:

1. A foundation model provider (e.g., ESM, AlphaFold-derived, BioNeMo ecosystem)
2. Proprietary pharma datasets for fine-tuning
3. Experimental feedback loops from assays and labs
4. Chemistry/biology simulation and screening tools

For a pharma organization starting today, the most mature starting points are generally protein foundation models (ESM/AlphaFold/OpenFold families), NVIDIA BioNeMo, and AI-native biotech platforms that can incorporate proprietary assay data.

The landscape of biology-specific foundation models spans open-science research labs, tech giants, and specialized tech-bio companies. These models process various biological modalities—such as amino acid sequences, genomic data, multi-omics, and 3D molecular structures.

Key providers of biology-specific foundation models are categorized below by their primary focus:

1. Structure, Complexes, & Multimodal Biomolecular Models

• Google DeepMind / Isomorphic Labs (AlphaFold 3): Models the structures and interactions of not just proteins, but complexes involving DNA, RNA, chemical modifications, and small-molecule ligands.
• EvolutionaryScale (ESM3): A frontier protein foundation model that jointly reasons across protein sequence, structure, and function, allowing for generative design of entirely new proteins.
• Chai Discovery (Chai-1): A multimodal model capable of predicting structures for proteins, nucleic acids, small molecules, and their complex assemblies.
• Institute for Protein Design / Baker Lab (RoseTTAFold All-Atom): Open-source and academic architectures designed to model complex assemblies containing proteins, nucleic acids, and small-molecule ligands.

2. Genomic & Multi-Modal Foundation Models

• Arc Institute / Open-source Consortia (Evo): A genomic foundation model trained on prokaryotic and phage genomes, capable of single-nucleotide scale reasoning across DNA, RNA, and proteins to predict and generate genomic sequences.
• BioMap (xTrimo): Provides massive cross-modal transformer architectures (scaling into hundreds of billions of parameters) integrating protein, DNA, RNA, and cellular data for systems-level biology.
• Recursion Pharmaceuticals (OpenPhenom & MolE): Focuses on high-throughput cellular phenomics (microscopy foundation models) and small-molecule property prediction (MolE) to map cellular biology at an industrial scale.

3. RNA & Specialized Modality Models

• Atomic AI (PARSE / ATOM-1): Concentrates on AI-driven RNA structural mapping and discovery, combining chemical mapping datasets with large language model components to predict RNA folding and design targeted therapeutics.

If you'd like to narrow this down, tell me:

• What specific modality are you targeting (small molecules, proteins, RNA, or whole genomes)?
• Are you looking for open-source models to deploy in-house or commercial API platforms?