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EvolutionaryScale

  • What it is:EvolutionaryScale is a private AI company that develops generative models for protein design and drug discovery, enabling scientists to create novel proteins with applications in therapeutics and materials science.
  • Best for:Academic protein researchers, Pharma protein engineering teams, Biotech startups in drug discovery
  • Pricing:Free tier available, paid plans from Closed beta
  • Rating:85/100Very Good
  • Expert's conclusion:ESM3 is a “gold mine” of research for developing novel proteins using generative design techniques; however, due to the requirement of technical sophistication to utilize the models and the non-commercial usage restriction of the API, ESM3 is not suitable for many of the production biotech workflows.
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Reviewed byMaxim Manylov·Web3 Engineer & Serial Founder

What Is EvolutionaryScale and What Does It Do?

EvolutionaryScale is an artificial intelligence (AI) firm that develops advanced generative AI technologies for biological applications, specifically protein design, in order to enhance drug discovery, materials science and therapeutic development. In terms of the development of advanced generative AI models such as ESM3, EvolutionaryScale is among the first companies to pioneer models that can reason across a protein's sequence, structure, and function in order to develop novel proteins that are far beyond those found through natural evolution.

Active
📍New York City, NY
📅Founded 2023
🏢Private
TARGET SEGMENTS
Pharmaceutical CompaniesBiotechnology FirmsAcademic ResearchersLife Sciences Researchers

What Are EvolutionaryScale's Key Business Metrics?

📊
$142M
Seed Funding
📊
4B+
Proteins Trained On
🏢
11-50
Employees
📊
NVIDIA, Amazon, Nat Friedman
Key Investors
📊
AWS, NVIDIA BioNeMo
Major Partnerships

How Credible and Trustworthy Is EvolutionaryScale?

85/100
Excellent

EvolutionaryScale has access to significant investment capital from top tier investors, along with the most advanced AI technologies available today, positioning it as one of the leaders in the field of generative biology; however, as a relatively young startup with very few public reviews from users.

BREAKDOWN
Product Maturity85/100
Company Stability90/100
Security & Compliance70/100
User Reviews65/100
Transparency80/100
Support Quality75/100
TRUST SIGNALS
$142M seed funding from top investorsPartnerships with AWS and NVIDIAFree academic access via BioNeMoGenerative models trained on billions of proteins

What is the history of EvolutionaryScale and its key milestones?

2023

Company Founded

EvolutionaryScale was founded by former researchers at Meta who sought to apply large language models to protein engineering and drug discovery.

2023

Seed Funding Round

EvolutionaryScale raised a $142M seed round, led by Nat Friedman, which included investments from many of the world’s leading technology companies, including NVIDIA, Amazon, etc.

2024

ESM3 Launch

EvolutionaryScale released ESM3, the first generative AI model that could reason jointly about both the sequence, structure and function of a protein.

2025

ESM Cambrian Launch

EvolutionaryScale introduced its ESM Cambrian family of frontier models for protein sequence modeling, which achieved a major breakthrough in efficiency.

2024

AWS Partnership

EvolutionaryScale announced a go-to-market partnership with Amazon Web Services (AWS), to make ESM3 available to their SageMaker and HealthOmics customer base, for use in the life sciences industry.

What Are the Key Features of EvolutionaryScale?

✨
ESM3 Generative Model
EvolutionaryScale is the first AI model capable of reasoning simultaneously about a protein's sequence, structure, and function in order to generate novel proteins from scratch.
✨
ESM Cambrian
The ESM family of protein sequence modeling is the state of the art in this area, and represents a major breakthrough in terms of performance and efficiency in representation learning.
✨
Multi-Domain Protein Generation
EvolutionaryScale can create complex multi-domain proteins and compose novel structures that have never been seen in nature.
✨
Antibody Design
EvolutionaryScale understands antibody sequence and structure to enable diversification, optimization, and directed evolution.
✨
Chain-of-Thought Prompting
EvolutionaryScale generates evolutionary breakthroughs like esmGFP, a fluorescent protein requiring 500 million years of natural evolution.
🔗
AWS Integration
EvolutionaryScale will be accessible to customers via Amazon SageMaker, for fine tuning on proprietary data and end-to-end drug discovery workflows.
✨
Multimodal Reasoning
EvolutionaryScale is able to process mixed input types of sequence, structure, and function data to allow exploration of a wide range of possible protein designs.

What Technology Stack and Infrastructure Does EvolutionaryScale Use?

Infrastructure

AWS with GPU instances, Trainium, and Inferentia accelerators

Technologies

PyTorchTransformersLarge Language Models

Integrations

AWS SageMakerAWS HealthOmicsNVIDIA BioNeMoAmazon Bedrock

AI/ML Capabilities

Frontier generative language models (ESM3, ESM Cambrian) trained on billions of protein sequences spanning 3.8 billion years of evolution, using transformer architectures adapted for biological sequence, structure, and multimodal function reasoning

Inferred from AWS/NVIDIA partnerships, model descriptions, and industry standards for protein LMs

What Are the Best Use Cases for EvolutionaryScale?

Drug Discovery Researchers
Develop new therapeutic proteins, antibodies, and enzymes from scratch to cut down R&D time frames by many years to identify a target
Pharmaceutical Companies
Use ESM3 to generate your own protein therapeutics on AWS to protect your proprietary data, while fine tuning your sequence to keep it private
Academic Biologists
Get access to frontier protein models for free from NVIDIA BioNeMo to ask the big question in biology and get published
Biotech Protein Engineers
Make multi-domain proteins and fluorescent proteins like GFP for experimental work flows that are not limited by how nature evolved them
Materials Scientists
Develop new protein based materials by creating new sequences with new structures and functions
NOT FORReal-time Clinical Diagnostics
Not appropriate -- focused on research/design generation and not on developing a real-time diagnostic application
NOT FORNon-Protein Therapeutics Developers
Limited use -- focused on modeling sequence/structure/function of proteins, not small molecules or gene therapy

How Much Does EvolutionaryScale Cost and What Plans Are Available?

Pricing information with service tiers, costs, and details
☐Service$CostℹDetails🔗Source
Non-commercial Use$0Small version of ESM3 available for offline non-commercial research via Forge developer platform and NVIDIA BioNeMoOfficial announcements
API AccessClosed betaAPI opening for closed beta; full access via cloud platformsTechCrunch
Commercial PartnershipsCustom / Usage fees / Revenue sharingPartnerships with pharmaceutical companies, integration into AWS SageMaker/Bedrock/HealthOmics and NVIDIA NIM microservices with enterprise licensesTechCrunch , AWS , NVIDIA
Non-commercial Use$0
Small version of ESM3 available for offline non-commercial research via Forge developer platform and NVIDIA BioNeMo
Official announcements
API AccessClosed beta
API opening for closed beta; full access via cloud platforms
TechCrunch
Commercial PartnershipsCustom / Usage fees / Revenue sharing
Partnerships with pharmaceutical companies, integration into AWS SageMaker/Bedrock/HealthOmics and NVIDIA NIM microservices with enterprise licenses
TechCrunch , AWS , NVIDIA

How Does EvolutionaryScale Compare to Competitors?

FeatureEvolutionaryScale ESM3Google DeepMind AlphaFoldIsomorphic LabsRecursion
Core FunctionalityGenerative protein design (sequence/structure/function)Structure predictionDrug discovery AI (AlphaFold-based)AI-driven cellular imaging & drug discovery
Pricing (starting)Free non-commercial / Custom enterpriseFree academicCustom pharma contractsPublicly traded / Partnership-based
Free Tier AvailabilityYes (non-commercial model)YesNoNo
Enterprise FeaturesAWS/NVIDIA cloud integration, fine-tuningLimitedPharma contracts with SSO/auditEnterprise partnerships
API AvailabilityYes (closed beta, NIM microservices)Yes (AlphaFold DB)Yes (select partners)Yes (platform access)
Integration CountAWS SageMaker/Bedrock/HealthOmics, NVIDIA BioNeMo/NIMLimitedCustom pharma integrationsProprietary platform
Support OptionsPartnership-based, enterprise licensesCommunity/academicDedicated pharma supportEnterprise support
Security CertificationsN/A (early stage)Google Cloud complianceEnterprise-gradeHealthcare compliance
Core Functionality
EvolutionaryScale ESM3Generative protein design (sequence/structure/function)
Google DeepMind AlphaFoldStructure prediction
Isomorphic LabsDrug discovery AI (AlphaFold-based)
RecursionAI-driven cellular imaging & drug discovery
Pricing (starting)
EvolutionaryScale ESM3Free non-commercial / Custom enterprise
Google DeepMind AlphaFoldFree academic
Isomorphic LabsCustom pharma contracts
RecursionPublicly traded / Partnership-based
Free Tier Availability
EvolutionaryScale ESM3Yes (non-commercial model)
Google DeepMind AlphaFoldYes
Isomorphic LabsNo
RecursionNo
Enterprise Features
EvolutionaryScale ESM3AWS/NVIDIA cloud integration, fine-tuning
Google DeepMind AlphaFoldLimited
Isomorphic LabsPharma contracts with SSO/audit
RecursionEnterprise partnerships
API Availability
EvolutionaryScale ESM3Yes (closed beta, NIM microservices)
Google DeepMind AlphaFoldYes (AlphaFold DB)
Isomorphic LabsYes (select partners)
RecursionYes (platform access)
Integration Count
EvolutionaryScale ESM3AWS SageMaker/Bedrock/HealthOmics, NVIDIA BioNeMo/NIM
Google DeepMind AlphaFoldLimited
Isomorphic LabsCustom pharma integrations
RecursionProprietary platform
Support Options
EvolutionaryScale ESM3Partnership-based, enterprise licenses
Google DeepMind AlphaFoldCommunity/academic
Isomorphic LabsDedicated pharma support
RecursionEnterprise support
Security Certifications
EvolutionaryScale ESM3N/A (early stage)
Google DeepMind AlphaFoldGoogle Cloud compliance
Isomorphic LabsEnterprise-grade
RecursionHealthcare compliance

How Does EvolutionaryScale Compare to Competitors?

vs Google DeepMind AlphaFold

ESM3 can do what AlphaFold does better in terms of predicting protein structures, plus ESM3 has the capability to generate novel proteins from scratch -- so you can reason about sequence/structure/function of proteins. AlphaFold can predict protein structures very well, but it cannot generate novel proteins.

ESM3 is used for de novo protein engineering; AlphaFold is used for structural analysis.

vs Isomorphic Labs

Both former FAIR teams have been trying to partner with pharma; however Evolutionary Scale is emphasizing open non-commercial access to their frontier model while Isomorphic has already established commercial relationships with pharma, for example Novartis. ESM3 may be more accessible for research.

Evolutionary Scale wants to provide its frontier model to the broadest possible research community; Isomorphic wants to limit its use to established pharma pipelines.

vs Recursion Pharmaceuticals

Recursion focuses on cellular imaging and machine learning as opposed to Evolutionary Scale, which specializes in generating proteins. Recursion is publicly traded and has a clinical pipeline; Evolutionary Scale is still early stage and has a frontier model.

A recursive system for a complete drug-discovery platform; an evolutionary-scale ESM3 for protein-specific innovation.

vs Insitro

Insitro uses machine learning across multiple areas of biology including proteomics and genomics, while Evolutionary Scale is hyper-focused on a single area -- the foundational protein models. Both want to establish partnerships with pharma, but they are taking different technical approaches.

An evolutionary-scale ESM3 for protein engineering specialists; a multi-omic drug-discovery platform for Insitro.

What are the strengths and limitations of EvolutionaryScale?

Pros

  • The breakthrough generative ability to create novel proteins that have never been found in nature.
  • The massive scale of training – 2.8 billion proteins with 98 billion parameters, 25 times greater than its predecessor.
  • Academic use is free — a smaller version of the model is available through Forge and NVIDIA’s BioNeMo for non-commercial use.
  • Major support — a $142 million seed round from top venture capital firms (Lux Capital and Nat Friedman) along with an Amazon and NVIDIA strategic investment.
  • Native cloud — seamless integration of the model into AWS SageMaker and Bedrock as well as NVIDIA’s NIM platform for enterprise customers.
  • Programmable biology — allows engineers to design proteins from first principles much like they design software.
  • Demonstrated success — has produced a novel GFP variant that demonstrates practical use.

Cons

  • In the early stages — Founded in 2024; approximately 20 employees; no commercial revenue has been reported to date.
  • Commercially restricted access — an API for the model is currently in closed beta, and the full model can only be accessed by partners or through enterprise licenses.
  • A long commercialization timeline — according to the company's pitch deck, it will take at least a decade for this technology to begin having a direct impact on therapy design.
  • Competition from pharmaceutical companies — is competing against Isomorphic Laboratories (which has already secured some commercial contracts with pharmaceutical companies) and other established players such as Recursion.
  • High demand for computing resources — requires large amounts of infrastructure (such as H100 GPUs) to operate and is therefore expensive for organizations to host themselves.
  • There are no publicly available prices — instead the company negotiates custom enterprise deals that make the total cost of ownership (TCO) for commercial users difficult to determine.
  • Focuses exclusively on biology — unlike general biotech platforms like Insitro that also work with other types of biological data.

Who Is EvolutionaryScale Best For?

Best For

  • Academic protein researchers — Provides a free, non-commercial version of the model — via Forge and BioNeMo; which should accelerate the advancement of fundamental research.
  • Pharma protein engineering teams — Ability to generate new therapeutic proteins with AWS/NVIDIA enterprise integration.
  • Biotech startups in drug discovery — Validates the concept with $142 million prior to commercialization; while also providing free research access.
  • Materials science researchers — Ability to engineer proteins for novel enzymes/materials outside of the realm of therapeutics.
  • AWS/NVIDIA enterprise AI customers — Seamless integration with SageMaker/Bedrock/NIM with fine-tuning capabilities for enterprise customers.

Not Suitable For

  • Small labs without cloud expertise — 98B parameter model needs an Enterprise Cloud Infrastructure. Consider ESM2 (open) or another alternative.
  • Budget-constrained commercial teams — Academic/Enterprise Pricing versus Established Tools. Use Academic version of AlphaFold for Production.
  • Non-protein biology researchers — Focus on Proteins. Consider Broader Platforms such as Recursion or Insitro.
  • Immediate clinical trial needs — Tech Estimating Decades to Therapies. Use Validated Computational Tools.

Are There Usage Limits or Geographic Restrictions for EvolutionaryScale?

Model Availability
Small version for non-commercial use; full 98B ESM3 via enterprise/cloud partners
API Access
Closed beta only; enterprise licensing through AWS/NVIDIA
Commercial Use
Requires partnerships, usage fees, or revenue sharing
Fine-tuning
Available to select AWS and NVIDIA enterprise customers
Geographic Availability
Cloud-based via AWS/NVIDIA (global where services available)
Compute Requirements
Requires H100 GPU-scale infrastructure for full model
Data Training Scope
Proteins only (2.8B sequences); expanding to general biotech

Is EvolutionaryScale Secure and Compliant?

AWS Infrastructure SecurityHosted via Amazon SageMaker/Bedrock/HealthOmics with AWS enterprise-grade security and compliance
NVIDIA AI EnterpriseFull ESM3 family available as NIM microservices with NVIDIA enterprise software license and security
Cloud Data ProtectionData processed in AWS/NVIDIA compliant cloud environments with customer fine-tuning controls
Pharma Partnership SecurityEnterprise-grade security for commercial integrations with pharmaceutical workflows
Responsible AI DevelopmentEx-Meta FAIR team with established AI safety practices for biological foundation models

What Customer Support Options Does EvolutionaryScale Offer?

Channels
Forge platform documentation and communityAWS/NVIDIA/pharma dedicated supportNVIDIA BioNeMo support channelsAmazon, NVIDIA enterprise support teams
Hours
Business hours via cloud partners; 24/7 enterprise SLAs available
Response Time
Cloud provider SLAs apply (enterprise: <4 hours; standard: <24 hours)
Satisfaction
N/A (early stage; no public review data)
Specialized
Pharma workflow integration specialists via partnerships
Business Tier
Dedicated AWS/NVIDIA enterprise support for licensed customers
Support Limitations
•No direct consumer support; routed through AWS/NVIDIA/pharma partnerships
•Academic users limited to platform documentation
•Commercial requires enterprise licensing/partnership

What APIs and Integrations Does EvolutionaryScale Support?

API Type
Python SDK API for inference (ESM3InferenceClient, ESM3ForgeInferenceClient). Supports protein generation, embedding, and logits prediction.
Authentication
Hugging Face API token (Read permission) for open models. Forge access token required for larger ESM3 models via forge.evolutionaryscale.ai.
Webhooks
No webhooks support mentioned in documentation.
Official SDKs
Python library ('pip install esm'). Provides ESM3, ESMC, ESMProtein classes and Forge client integration.
Documentation
Good - detailed GitHub repository (github.com/evolutionaryscale/esm) with code examples, installation, and API usage. Forge API examples provided.
Sandbox
Open ESM3-sm model available locally or via Hugging Face. Forge beta access for larger models requires application.
SLA
No public SLA information. AWS SageMaker deployment available for self-hosted production use.
Rate Limits
No public rate limits documented. Forge API token-based access.
Use Cases
Protein sequence generation, structure prediction, function annotation, embeddings. Local inference on GPU/CPU or cloud via Forge/AWS SageMaker.

What Are Common Questions About EvolutionaryScale?

ESM3 is a Multimodal Generative Language Model that was Trained on Evolutionary-Scale Protein Data. It Simultaneously Reasons Over Sequence, Structure and Function Using Masked Language Modeling to Generate and Predict Proteins Across Modalities.

ESM3 Generates Proteins From Partial Specifications of Sequence, Structure or Function, Simulating the Process of Evolution. Unlike Structure Prediction Tools Such as AlphaFold, ESM3 Is a Generative Tool Which Creates New Proteins, Rather Than Simply Predicting Existing Ones.

The API Models Are for Non-Commercial Use Only and May Not Be Used by Commercial Entities Per Terms of Service. There Are No Specific Security Certifications Mentioned; Self Hosting via AWS Sage Maker Keeps Your Data In Your Environment.

Apply For Beta Access to Evolutionary Scale Forge at forge.evolutionaryscale.ai. Use the Python SDK With Your Forge Token to Access Remote Models Like ESM3-Medium and ESMC 6B.

Smaller Open Models Like ESM3-SM Can Be Downloaded Via Hugging Face And Run Locally On A GPU/CPU Using The Python SDK. Larger Models Require API Access To Forge.

API Models Are Restricted to Non-Commercial Research Use Only. Commercial Entities Cannot Access API Models. Production Deployment Requires AWS Sage Maker Self Hosting.

Deploy Using AWS Sage Maker Market Place and Cloud Formation Templates. Runs On Dedicated GPU Instances That Are Billed to Your AWS Account. Cleanup Required to Avoid Ongoing Costs.

Use of The Python SDK Only (pip install esm) Provides Full Interface for Model Inference, Protein Manipulation, and Integration With The Forge API. No Other Language SDK Available.

Is EvolutionaryScale Worth It?

With the release of EvolutionaryScale’s ESM3, there has been a revolutionary advancement in the area of Protein Generative AI. This technology can produce novel proteins that are applicable in all three of the major areas of sequence, structure, and function. Although the open source nature of this project along with the ability to use a python SDK to accelerate your research, the commercialization of the models and the potential for gating access to those models for advanced models may provide challenges in deploying the models.

Recommended For

  • Researchers at academic institutions who have an interest in designing proteins using generative techniques.
  • Early stage discovery phase biotech R&D teams.
  • Computational biologists with experience in both Python and machine learning.
  • Organizations who are able to self-host their models in AWS SageMaker.

!
Use With Caution

  • Commercial biotech firms – The ESM3 API is limited to non-commercial use as stated in the terms of use agreement.
  • Biotech teams without the GPU infrastructure or budget to support running the inference models.
  • Users who require mature production APIs with Service Level Agreements (SLA) to deploy the models into production.
  • Non-technical biologists – Requires proficiency in machine learning.

Not Recommended For

  • Production therapeutic development teams who do not have the capability to self-host the models.
  • Budget constrained teams who cannot afford to run GPU-based inference models.
  • Biotech firms who need to rapidly deploy their models commercially.
  • Teams who require REST and/or GraphQL enterprise APIs to integrate the models into their existing architectures.
Expert's Conclusion

ESM3 is a “gold mine” of research for developing novel proteins using generative design techniques; however, due to the requirement of technical sophistication to utilize the models and the non-commercial usage restriction of the API, ESM3 is not suitable for many of the production biotech workflows.

Best For
Researchers at academic institutions who have an interest in designing proteins using generative techniques.Early stage discovery phase biotech R&D teams.Computational biologists with experience in both Python and machine learning.

What do expert reviews and research say about EvolutionaryScale?

Key Findings

ESM3 is a cutting-edge multimodal protein language model which generates novel proteins by utilizing large scale training datasets based upon evolutionary principles. Although the models are available through an open source Python SDK which allows users to run the models locally or remotely through the Forge API, and in addition allows users to deploy the models into production environments through AWS SageMaker, the API for accessing these models is strictly limited to non-commercial usage per the terms of use agreement which limits the ability of biotech/pharmaceutical companies to adopt the technology in a commercial environment.

Data Quality

Good - comprehensive technical documentation via GitHub/PyPI. Limited commercial/pricing transparency typical for research-focused AI biotech tools.

Risk Factors

!
Restrictions on non-commercial usage of the API prevent the use of the technology for production purposes in biotechnology/pharmaceutical companies.
!
Access to the Forge API is gated through a beta application process that prevents widespread adoption of the technology.
!
As the technology continues to be developed, it will continue to be heavily dependent on continued advancements in the models.
!
There are no Service Level Agreements (SLA) or rate limits provided for production use of the models.
Last updated: February 2026

What Are the Best Alternatives to EvolutionaryScale?

  • •
    AlphaFold3 (Google DeepMind): The leading protein structure prediction model which has a few elements of generative capabilities. An older ecosystem with an increased emphasis on structural biology research rather than generating new proteins or structures. Best for structural biology research needing the highest possible levels of accuracy. (DeepMind.Google)
  • •
    RFdiffusion (Baker Lab): A diffusion-based protein backbone generator that is designed to generate the backbone structure of proteins from scratch. It generates backbone structures, and provides structural constraints, that can be used to generate protein sequences and then fold into the predicted backbone structure. This is complementary to ESM3, as it generates backbone structures better than ESM3, whereas ESM3 predicts protein sequences that are folded into the best backbone structure. It is open-source with a large user-base. Best for structure-first protein design workflows. (GitHub.com/RosettaCommons/RFdiffusion)
  • •
    ProtGPT2 / ProGen (Salesforce): Older protein language models were primarily focused on generating protein sequences. They do not have the same level of sophistication as ESM3’s multimodal approach, but they can still be used for certain sequence-only protein design tasks. They serve as a good baseline for comparison to ESM3. (HuggingFace.Co)
  • •
    ChromaX / Chroma (Generate:Biomedicines): Protein design software company with commercial product that has generative capabilities. Is production-ready; unlike ESM3, which is a research-focused effort. Requires enterprise license to use. Best for biotech companies that require GMP-trackable designs. (GenerateBiomedicines.com)
  • •
    RosettaFold All-Atom (Baker Lab): Commercial protein design software suite that uses both sequence and structure in its design process. Has a more traditional machine-learning plus physics approach compared to ESM3’s language modeling. Used by researchers in academia and pharma. Best for validating therapeutic design pipelines. (Bake-Lab.org)

What Additional Information Is Available for EvolutionaryScale?

Technical Innovation

ESM3 simulates 500 million years of evolution by performing masked multimodal training across sequence, structure, and function. Uses this simulated evolution to achieve state-of-the-art protein generation performance.

Open Source Commitment

Released ESM3-sm publicly through Hugging Face along with a Python SDK. Also allows users to access larger models through Forge Beta. Allows the global research community to rapidly iterate with ESM3.

Deployment Options

Available to deploy into production using AWS SageMaker Marketplace on dedicated GPU instance. Users will pay directly to their AWS account with CloudFormation automation.

Research Publication

Published in the Science journal detailing the methodology and benchmarking results for ESM3. Validates ESM3 as the peer-reviewed frontier capability for protein design.

Design Success & Validation Metrics

500 million years of evolution simulated
Evolutionary Divergence
4 billion proteins
Training Dataset Scale
high emergent with model scale
Atomic Coordination Accuracy
hundreds of millions predicted structures & functions
Synthetic Data Augmentation
esmGFP - novel fluorescent protein beyond natural evolution
Protein Design Novelty

Supported Generative Models

ESM3 Frontier Language Model

Simultaneously reason about sequence, structure, and function with multimodal masked language model.

ESM Cambrian Representation Learning

Advanced Protein Sequence Modeling Advanced Protein Sequence Modeling with Performance & Efficiency

Chain-of-Thought Prompting

Producing Complex Proteins like ESM-GFP using Structured Reasoning Prompts Producing Complex Proteins like ESM-GFP Using Structured Reasoning Prompts

Multimodal Training

Trained On a Large Diverse Set Of Sequences And Their Predicted Structures And Functions Across Billions Of Proteins Trained on a large diverse set of sequences, their predicted structures and functions across billions of proteins.

Atomic Level Structure Generation

Achieving Functional Protein Design Through Atomic Coordination For Distant Sequence Positions Achieving Functional Protein Design through atomic coordination for distant sequence positions.

Evolutionary Simulation

Evolution Over 500 Million Years To Generate New Protein Architectures Evolution over 500 million years to generate new protein architectures.

Core ESM3 Evaluation Capabilities

Sequence-Structure-Function ReasoningAtomic Coordination AccuracyEvolutionary PlausibilityFluorescence Validation (esmGFP)Multimodal ReasoningSynthetic Data IntegrationScale-Dependent EmergencePrompt-Based ControlProtein ScaffoldingFunctional Site Design

Supported Design Applications

Drug Discovery
Novel therapeutic proteins, antibody engineering, protein therapeutics design
Synthetic Biology
De novo protein design, fluorescent proteins (esmGFP), novel enzymes
Materials Science
Engineered protein materials, novel protein architectures
Academic Research
Free access via NVIDIA BioNeMo, frontier protein design exploration
Pharma Partnerships
Specialized ESM3 versions for drug design applications

Supported Data Modalities & Input Formats

Data ModalityInput FormatRequired/OptionalIntegration Depth
Protein SequencesNatural language prompts, tokenized sequencesRequiredPrimary generative input for ESM3
3D StructuresPredicted structures, atomic coordinatesRequiredMultimodal reasoning with sequence
Functional AnnotationsFluorescence, enzymatic activity, bindingRequiredJoint sequence-structure-function learning
Evolutionary DataBillions of natural proteinsTraining4 billion protein training dataset
Synthetic StructuresHundreds of millions predictedTrainingData augmentation for multimodal learning
API AccessREST API, NVIDIA BioNeMo, AWS platformsOptionalProduction deployment and scaling
Prompt EngineeringChain-of-thought natural languageOptionalPrecise control of generation process

Computational Requirements & Infrastructure

Deployment Platforms
NVIDIA BioNeMo, Amazon Bedrock, SageMaker, AWS HealthOmics
Model Scale
Billions of parameters across ESM3 family
Training Compute
Evolutionary-scale dataset processing (4B proteins)
Inference Access
API-based access, cloud deployment optimized
Academic Access
Free via NVIDIA BioNeMo platform
Enterprise Deployment
AWS cloud infrastructure optimized
Open Model Availability
Research models available for scientific exploration

Regulatory Compliance & Responsible AI

Scientific Rigor CommitmentOpen research, reproducible protein design results
Responsible AI DevelopmentSafe AI for biology, scientific community partnerships
Academic Validation AccessFree access via NVIDIA BioNeMo for benchmarking
Pharma Partnership ValidationBeta API access prioritized for high-impact applications
Model TransparencyOpen models for scientific exploration and validation
Benchmark ReproducibilityesmGFP experimental validation, published capabilities

Scientific Workflow Integration

API-First Design

REST API for Production Generation, Beta Access Prioritization REST API for protein generation and beta access prioritzation.

NVIDIA BioNeMo Integration

Academic Use is Free, Production Deployment Pipeline Available Academic use is free; Production deployment pipeline available.

AWS Cloud Integration

Bedrock, SageMaker, HealthOmics Deployment Options Bedrock, SageMaker, HealthOmics deployment options.

Prompt-Based Interface

Chain-Of-Thought Control of Protein Design Using Natural Language Chain-of-thought control of protein design using natural language.

Open Model Ecosystem

Research Community Access to Tool Development Research community access to tool development.

Pharma Collaboration Pipeline

Enterprise Partnerships for Custom ESM3 Versions Enterprise partnerships for custom ESM3 versions.

Synthetic Biology Workflows

From Prompt to Novel Protein (ESM-GFP Workflow) From Prompt to Novel Protein (ESM-GFP Workflow).

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