term6
type: DefinedTerm
description: The three-dimensional arrangement of atoms in a protein, determined by the amino acid sequence, which determines the protein's biological function.
term name: Protein Structure

faq2
type: Question
accepted answer: faq2Answer
question: How does ESMFold2 differ from other structure prediction models?

has accuracy metric
type: Property
range: string

Structure Prediction Model
type: Class
comment: Protein structure prediction models that predict 3D protein structures from amino acid sequences
subclass of: Protein Model

Looped Transformer Architecture
type: Thing
description: ESMFold2 architecture that passes representations through the same parameters multiple times, optimized through this loop during training. Enables compute scaling at inference time by running more loops.

Evaluate Predicted Binders
type: HowToStep
position: 5
text: Review computational predictions for binding affinity and selectivity. For scFvs, expect success rates around 21% with higher compute. Select top candidates for experimental validation.

AWS Bio Discovery
type: Organization
description: Partner platform for ESM access

term3
type: DefinedTerm
description: The protein language model component of ESM, trained on approximately 2.8 billion sequences from across all of life, providing representations that capture fundamental principles of protein structure and function.
term name: ESMC (ESM Cambrian)

ESM Protein Biology Glossary
type: DefinedTermSet
has defined term: term1
has defined term: term2
has defined term: term3
has defined term: term4
has defined term: term5
has defined term: term6
has defined term: term7
has defined term: term8
has defined term: term9
has defined term: term10

faq6
type: Question
accepted answer: faq6Answer
question: What are minibinders and scFvs in protein design?

biohubPlatformAccess
type: Offer
availability: Available
price: 0.0
price currency: USD

Sequence Atlas
type: Class
comment: Comprehensive maps of protein sequences and predicted structures across life

faq3
type: Question
accepted answer: faq3Answer
question: What therapeutic targets has ESMFold2 been validated against?

Technical Details of ESM Architecture
type: ArticleSection
description: Deep dive into the technical architecture of ESMC and ESMFold2 including transformer language models, looped transformer architecture, and scaling laws

faq3Answer
type: Answer
text: ESMFold2 has been validated against five clinically relevant targets: EGFR and PDGFRβ (receptor tyrosine kinases in tumor growth), PD-L1 and CTLA-4 (immune checkpoints cancer cells exploit), and CD45 (immune cell signaling regulator). An ESMFold2-designed scFv bound PD-L1 with 4.3 nM affinity.

ESMFold2
type: Structure Prediction Model
type: SoftwareApplication
application category: Protein Structure Prediction
citation: ESM Research Preprint
description: State-of-the-art protein structure prediction model using looped transformer architecture that predicts protein structure and powers the design of new proteins
has accuracy metric: 55% antibody-antigen complexes; 71% protein-protein interactions (single sequence); 77% with alignment data
has training scale: Not applicable (inference-time compute scaling via loop iterations)
uses architecture: Looped Transformer

faq7Answer
type: Answer
text: Sparse autoencoders decomposed ESMC's internal representations into more than 16,000 distinct features. The model independently recovered basic organizing principles of biology: amino acid chemistry, local structural interactions, abstract functional concepts recurring across unrelated proteins, and evolutionary themes connecting all of life.

CRISPR-Cas Discovery
type: ArticleSection
text: In SAE feature clusters derived from ESM Atlas, a cluster of RNA-guided DNA endonucleases - functional class including CRISPR-Cas systems - brings together eukaryotic Fanzor proteins and their evolutionary ancestor prokaryotic TnpB, despite high evolutionary divergence and low sequence similarity.

CTLA-4
type: Product
type: Thing
description: Cytotoxic T-Lymphocyte Antigen 4 - an immune checkpoint that cancer cells exploit to evade immune surveillance, one of five clinically relevant therapeutic targets validated with ESMFold2

term10
type: DefinedTerm
description: The two primary secondary structure arrangements that form when a protein backbone folds into its most stable conformations.
term name: Alpha Helix and Beta Sheet

Iterate and Optimize
type: HowToStep
position: 7
text: Use experimental feedback to refine design parameters. ESM enables rapid iteration - designs can be computationally validated before bench experiments, accelerating the design cycle for therapeutic protein development.

faq4
type: Question
accepted answer: faq4Answer
question: What is the scale of ESM Atlas?

scFv (Single-chain Variable Fragment)
type: Thing
description: Single-chain variable fragment antibodies - antibody-derived molecules using unstructured loops to bind targets. Success rates nearly doubled from 12% to 21% across five targets.

uses architecture
type: Property
range: string

PD-L1
type: Product
type: Thing
description: Programmed Death-Ligand 1 - an immune checkpoint that cancer cells exploit to evade immune surveillance, one of five clinically relevant therapeutic targets validated with ESMFold2. An ESMFold2-designed scFv bound PD-L1 with 4.3 nM affinity.

NVIDIA
type: Organization
description: Partner providing TransformerEngine and cuEquivariance kernels for training ESMC and ESMFold2

Personalized Medicine Potential
type: ArticleSection
text: While disease follows common patterns, much of disease is individual. For cancer and rare diseases, potential is most immediate. ESM can design lab-validated protein binders for five clinically relevant targets in days.

Benchling AI
type: Organization
description: Partner platform for ESM access

Validate in Laboratory
type: HowToStep
position: 6
text: Test designed binders experimentally using cell-based assays to measure affinity and functional activity. Verify structure using cryo-electron microscopy if crystal structure confirmation is needed.

Cryo-EM Verification
type: ArticleSection
text: Using cryo-electron microscopy, researchers verified structure of ESMFold2-designed minibinder bound to its target. High concordance between computational and observed models demonstrated 1.204 Å RMSD, exemplifying ESMFold2's design capabilities.

Feature Decomposition with SAEs
type: ArticleSection
text: Sparse autoencoders decomposed ESMC's internal representations into more than 16,000 distinct features. The model, trained only to predict amino acid sequences with no prior biological knowledge, independently recovered many basic organizing principles of biology.

EGFR
type: Product
type: Thing
description: Epidermal Growth Factor Receptor - a receptor tyrosine kinase implicated in tumor growth, one of five clinically relevant therapeutic targets validated with ESMFold2

Phylo
type: Organization
description: Partner platform for ESM access

Alpha Helix
type: Thing
description: Primary secondary structure arrangement that forms when a protein backbone folds into stable conformations. Features representing alpha helices were discovered in ESMC's latent space.

faq9
type: Question
accepted answer: faq9Answer
question: What partner platforms provide access to ESM?

faq7
type: Question
accepted answer: faq7Answer
question: What did sparse autoencoders reveal about ESMC's latent space?

faq5Answer
type: Answer
text: ESMC was trained on approximately 2.8 billion sequences drawn from across all of life. Researchers identified a scaling law linking compute power used in training to how accurately representations capture biology. In ESMC, this powers linear returns with scale, leading to state-of-the-art protein representations.

term8
type: DefinedTerm
description: Single-chain variable fragment antibodies - antibody-derived molecules that use unstructured loops to bind their targets, particularly demanding examples of the binder design problem.
term name: scFv (Single-chain Variable Fragment)

faq4Answer
type: Answer
text: ESM Atlas contains 6.8 billion sequences and 1.1 billion predicted structures, enabling the sequences and structures of proteins across all of life to be studied as a complete picture.

Future of Biology
type: ArticleSection
text: Future of biology will depend on systems combining scaled data, predictive models, and experimental feedback. This world model is an important step toward that future: an open foundation for understanding proteins, discovering new biology, and designing molecular tools that can help prevent, treat, or cure disease.

What This Makes Possible
type: ArticleSection
description: Future implications of ESM for medicine, drug discovery, and understanding biology

ESM Atlas
type: Sequence Atlas
type: SoftwareApplication
application category: Protein Sequence Atlas
description: A map of 6.8 billion sequences and 1.1 billion predicted structures, enabling the sequences and structures of proteins across all of life to be studied as a complete picture
has training scale: 6.8 billion sequences; 1.1 billion predicted structures

Minibinder
type: Thing
description: Compact, de novo protein scaffolds with no predetermined structure used for binder design. Success rates averaged 54% to 70% at higher compute across five targets.

term7
type: DefinedTerm
description: Compact, de novo protein scaffolds with no predetermined structure used for designing protein binders computationally.
term name: Minibinder

faq8Answer
type: Answer
text: ESMFold2 uses a looped transformer where representations pass through the same parameters multiple times. The model learns to use each pass to refine its structural representation based on previous computations. This allows compute scaling at inference by running more loops without retraining, allocating more capacity to harder predictions.

MIT License
type: CreativeWork
description: All ESM models released under MIT license
license: MIT

Modal
type: Organization
description: Partner platform for ESM access

faq10Answer
type: Answer
text: ESM enables computational protein binder design validated against five clinically relevant targets in oncology and immunology. When digital representations of biology become accurate enough, protein designs can be tested computationally before they reach the bench. This is particularly promising for cancer and rare diseases where much of disease is individual.

SandboxAQ
type: Organization
description: Partner platform for ESM access

faq5
type: Question
accepted answer: faq5Answer
question: How was ESMC trained and what scaling laws were discovered?

TnpB
type: Thing
description: Prokaryotic evolutionary ancestor of eukaryotic Fanzor proteins, appearing in the same SAE feature cluster in ESM Atlas despite high evolutionary divergence and low sequence similarity

faq1Answer
type: Answer
text: ESM is a world model of protein biology comprising three artifacts: ESMFold2 (structure prediction), ESM Atlas (map of 6.8B sequences/1.1B structures), and ESMC (protein language model trained on ~2.8B sequences). It learns from protein sequences produced by evolution to represent, map, predict, and design proteins.

faq6Answer
type: Answer
text: Minibinders are compact, de novo protein scaffolds with no predetermined structure used for binder design. scFvs (single-chain variable fragment antibodies) are antibody-derived molecules that use unstructured loops to bind targets. ESMFold2 can design both formats computationally with therapeutic-relevant affinities.

Nucleophilic Elbow Example
type: ArticleSection
text: Example: nucleophilic elbow, a catalytic motif that evolved independently many times, appearing in enzymes sharing no common ancestor and adopting entirely different folds. Features representing fundamental concepts in proteins combine to create complex functions.

PDGFRβ
type: Product
type: Thing
description: Platelet-Derived Growth Factor Receptor beta - a receptor tyrosine kinase implicated in tumor growth, one of five clinically relevant therapeutic targets validated with ESMFold2

Foldbench
type: Thing
description: A suite of structure prediction benchmarks used to evaluate ESMFold2's state-of-the-art accuracy

faq1
type: Question
accepted answer: faq1Answer
question: What is ESM (Evolutionary Scale Models)?

binder design algorithm
type: Property
range: string

has training scale
type: Property
range: string

has validation method
type: Property
range: string

faq10
type: Question
accepted answer: faq10Answer
question: What is the significance of ESM for medicine?

Five Validated Targets
type: ArticleSection
text: Five targets central to oncology and immunology: EGFR and PDGFRβ (receptor tyrosine kinases implicated in tumor growth); PD-L1 and CTLA-4 (immune checkpoints that cancer cells exploit to evade immune surveillance); CD45 (regulator of immune cell signaling).

term5
type: DefinedTerm
description: Chains of 20 chemical building blocks (amino acids) whose order determines how the chain folds into a three-dimensional structure, and folding determines function.
term name: Protein Sequence

Feature Categories Discovered
type: ArticleSection
text: Features discovered: specific amino acids; classes of amino acids (aromatics, small hydrophobics); context-dependent configurations; alpha helices and beta sheets; abstract concepts like cellular localization signals, post-translational modifications, local biochemical and electrostatic environments, functional classes independent of fold.

Beta Sheet
type: Thing
description: Primary secondary structure arrangement that forms when a protein backbone folds into stable conformations. Features representing beta sheets were discovered in ESMC's latent space.

term9
type: DefinedTerm
description: A technique developed to identify interpretable structure in large language models by decomposing internal representations into distinct features.
term name: Sparse Autoencoders (SAE)

ESM Atlas: Map of Known and Unknown Biology
type: ArticleSection
description: How ESM Atlas reveals evolutionary relationships and functional connections across all of life

Select Target Molecule
type: HowToStep
position: 1
text: Choose a clinically relevant molecular target such as EGFR, PDGFRβ, PD-L1, CTLA-4, or CD45. Define the binding site and desired affinity characteristics for therapeutic activity.

Therapeutic Protein Design Results
type: ArticleSection
description: Results of ESMFold2 validation against five clinically relevant targets in oncology and immunology

faq9Answer
type: Answer
text: ESM is available through partner platforms including AWS Bio Discovery, Benchling AI, Modal, Phylo, SandboxAQ, Tamarind Bio, and Tool Universe. ESMFold2, ESMC, and ESM Atlas are available at the Biohub Platform.

Tamarind Bio
type: Organization
description: Partner platform for ESM access

Cryo-EM
type: Thing
description: Cryo-electron microscopy used to verify the structure of ESMFold2-designed minibinders bound to targets. High concordance between computational and observed models (1.204 Å RMSD) demonstrated.

Organization of Concepts in ESMC's Latent Space
type: ArticleSection
description: How sparse autoencoders revealed the structure of concepts learned by ESMC without any prior biological knowledge

faq2Answer
type: Answer
text: ESMFold2 uses a looped transformer architecture rather than searching for evolutionarily related sequences (MSAs). It operates directly from ESMC's learned protein representations, capturing evolutionary information encoded during language model pretraining. It achieves 55% on antibody-antigen complexes and can predict a 1024-length protein structure in 9.4 seconds.

How to Design Protein Binders with ESMFold2
type: HowTo
description: Step-by-step guide to computationally designing protein binders using ESMFold2 for clinically relevant molecular targets
step: Select Target Molecule
step: Choose Binder Format
step: Define Design Constraints
step: Run ESMFold2 Design Algorithm
step: Evaluate Predicted Binders
step: Validate in Laboratory
step: Iterate and Optimize

ESMFold2-Fast
type: Structure Prediction Model
description: Fast protein structure prediction variant that predicts a 1024-length protein structure in 9.4 seconds while outperforming existing models on antibody-antigen folding
has training scale: 9.4 seconds per 1024-length protein
uses architecture: Looped Transformer (Optimized)

ESMC (ESM Cambrian)
type: Protein Language Model
type: SoftwareApplication
application category: Protein Language Modeling
citation: ESM Research Preprint
description: State-of-the-art protein language model trained on approximately 2.8 billion sequences drawn from across all of life, providing a foundation for modeling sequence, structure, and function of proteins
has training scale: ~2.8 billion sequences
uses architecture: Transformer

term2
type: DefinedTerm
description: State-of-the-art protein structure prediction model using looped transformer architecture that predicts protein structure from amino acid sequences without requiring multiple sequence alignments.
term name: ESMFold2

Nucleophilic Elbow
type: Thing
description: A catalytic motif that has evolved independently many times, appearing in enzymes that share no common ancestor and adopt entirely different folds. Represented as an abstract feature in ESMC's latent space.

faq8
type: Question
accepted answer: faq8Answer
question: How does ESMFold2 achieve compute-time scaling?

Choose Binder Format
type: HowToStep
position: 2
text: Select between minibinders (compact de novo scaffolds with no predetermined structure) or scFvs (single-chain variable fragment antibodies) depending on therapeutic requirements and target characteristics.

ESM (Evolutionary Scale Models)
type: SoftwareApplication
application category: Scientific Research Software
description: A world model of protein biology comprising ESMFold2, ESM Atlas, and ESMC for protein structure prediction, sequence mapping, and language modeling
offers: biohubPlatformAccess
operating system: Cloud Platform

Define Design Constraints
type: HowToStep
position: 3
text: Establish parameters for the binder design including required affinity (nanomolar potency), specificity requirements, and structural constraints for the target binding interface.

Tool Universe
type: Organization
description: Partner platform for ESM access

Scaling Laws and Representation Quality
type: ArticleSection
text: ESMC identified scaling law linking compute power used in training to degree representations accurately capture biology. Linear returns with scale lead to state-of-the-art protein representations.

CD45
type: Product
type: Thing
description: Cluster of Differentiation 45 - a regulator of immune cell signaling, one of five clinically relevant therapeutic targets validated with ESMFold2

Fanzor
type: Thing
description: Eukaryotic RNA-guided DNA endonuclease proteins that cluster with prokaryotic TnpB in ESM Atlas SAE features, despite high evolutionary divergence and low sequence similarity

CRISPR-Cas Systems
type: Thing
description: Naturally occurring gene-editing systems that bacteria evolved to defend themselves against viruses. Featured in ESM Atlas SAE feature clusters, bringing together Fanzor and TnpB proteins despite high evolutionary divergence.

kg-generator skill
type: SoftwareApplication
description: AI agent skill for generating knowledge graphs from web content

Transformer Language Model Evolution
type: ArticleSection
text: ESM program developed the first transformer language model of protein sequences in 2019. Core scientific hypothesis: training a language model across sequences of all life causes it to internalize fundamental properties governing protein biology - rules underlying protein folding, interaction, and function.

Run ESMFold2 Design Algorithm
type: HowToStep
position: 4
text: Use ESMFold2's design algorithm that searches through the joint model of sequence and structure for proteins predicted to bind the target. Allocate compute resources based on desired success rates (higher compute yields up to 70% minibinder success).

Scale and Coverage
type: ArticleSection
text: ESM Atlas is a map of 6.8 billion sequences and 1.1 billion predicted structures. The atlas enables sequences and structures of proteins across all of life to be studied as a complete picture.

Computational Scale Advantages
type: ArticleSection
text: Computing structures for 1.1 billion proteins in a week, or screening millions of candidates in hours, isn't just faster science. Ability to run experiments virtually at scale not possible in laboratory starts to unlock new possibilities in biology.

term1
type: DefinedTerm
description: Evolutionary Scale Models - A world model of protein biology comprising ESMFold2, ESM Atlas, and ESMC that learns from protein sequences produced by evolution to represent, map, predict, and design proteins.
term name: ESM (Evolutionary Scale Models)

A World Model of Protein Biology
type: TechArticle
about: ESM (Evolutionary Scale Models)
date published: 2026-05-28
description: Biohub releases ESM (Evolutionary Scale Models) - a world model of protein biology comprising ESMFold2 (structure prediction), ESM Atlas (map of 6.8B sequences/1.1B structures), and ESMC (protein language model trained on ~2.8B sequences).
has part: Frequently Asked Questions about ESM
has part: ESM Protein Biology Glossary
has part: How to Design Protein Binders with ESMFold2
has part: Technical Details of ESM Architecture
has part: Therapeutic Protein Design Results
has part: Organization of Concepts in ESMC's Latent Space
has part: ESM Atlas: Map of Known and Unknown Biology
has part: What This Makes Possible
publisher: Biohub
was generated by: this

Design Success Rates
type: ArticleSection
text: Averaged across targets, minibinder success rates rose from 54% to 70% at higher compute. For scFvs, success rates nearly doubled from 12% to 21%. ESMFold2-designed de novo scFv bound PD-L1 with measured affinity of 4.3 nM.

term4
type: DefinedTerm
description: A map of 6.8 billion protein sequences and 1.1 billion predicted structures, enabling comprehensive study of proteins across all of life.
term name: ESM Atlas

Frequently Asked Questions about ESM
type: FAQPage
type: ArticleSection
main entity: faq1
main entity: faq2
main entity: faq3
main entity: faq4
main entity: faq5
main entity: faq6
main entity: faq7
main entity: faq8
main entity: faq9
main entity: faq10

Looped Transformer Architecture
type: ArticleSection
text: ESMFold2 uses looped transformer where representations pass through same parameters multiple times, optimized through this loop during training. Each pass refines structural representation based on previous computation.

Sparse Autoencoders (SAE)
type: Thing
description: Technique developed to identify interpretable structure in large language models, used to decompose ESMC's internal representations into more than 16,000 distinct features

Biohub
type: Organization
description: A scientific research organization developing world models of protein biology

Protein Model
type: Class
comment: Base class for protein models developed at Biohub

Protein Language Model
type: Class
comment: Language models trained on protein sequences that learn evolutionary patterns and biological properties
subclass of: Protein Model

ESM Research Preprint
type: Article
description: Preprint describing full results of ESM research program, to be posted on bioRxiv
license: MIT