Embeddings, attention maps, contact predictions, and function annotations from ESM-3.

ESM-3 returns embedding vectors — per-residue (L x D) and pooled per-sequence (1 x D) — plus optional attention maps and metadata. What you do next depends on which you use: pooled per-sequence embeddings feed cosine-similarity comparison and clustering, per-residue embeddings feed residue-level ML features and contact prediction, so choose the output that matches your downstream task.

Output artifacts

Each completed ESM-3 job returns embedding vectors, optional attention maps, and metadata about the computation.

Primary output fields

Field	Shape	Description
per_residue_embeddings	L x D	Per-residue embedding matrix (L = sequence length, D = hidden dimension)
per_sequence_embedding	1 x D	Pooled embedding vector representing the entire protein
attention_maps	H x L x L	Multi-head attention matrices (H = number of heads, if requested)
sequence	string	Input amino acid sequence
model	string	Model variant used (esm3_sm, esm3_md, esm3_lg)
embedding_dim	integer	Embedding dimension D

Understanding embeddings

Per-residue embeddings

Each amino acid position gets a D-dimensional vector that encodes:

Local structural context: Secondary structure propensity, solvent accessibility, flexibility
Evolutionary conservation: Positions under strong selection have distinctive embedding patterns
Functional information: Active site residues, binding interfaces, and catalytic residues have characteristic signatures
Positional relationships: The embedding at each position captures context from the entire sequence via self-attention

Per-sequence embeddings

The pooled embedding reduces the full L x D matrix to a single 1 x D vector:

Global protein identity: Encodes overall fold, function, and taxonomic origin
Similarity metric: Cosine similarity between per-sequence embeddings correlates with functional relatedness
Clustering input: Suitable for direct use in k-means, HDBSCAN, or other clustering algorithms
Classification features: Input for protein family, localization, or activity classifiers

Embedding dimensions by model

Model	Hidden Dimension (D)	Per-Residue Shape	Per-Sequence Shape
Small	1,536	L x 1,536	1 x 1,536
Medium	2,560	L x 2,560	1 x 2,560
Large	6,144	L x 6,144	1 x 6,144

Attention maps

When return_attention is enabled, the output includes attention weight matrices from the transformer layers.

Interpreting attention maps

Residue-residue contacts: High attention weights between distant residues often indicate physical contacts in the 3D structure
Functional interactions: Attention patterns highlight residues that are functionally coupled
Symmetry: Attention maps are not symmetric; attention from residue i to j may differ from j to i

Contact prediction from attention

Average attention across multiple heads and layers to produce a contact map, then threshold it yourself to binarize predictions
Compare with ESMFold PAE maps for structural validation
Contact predictions are approximate; use ESMFold for higher-accuracy structural information

Similarity analysis

Cosine similarity

Compute similarity between two per-sequence embeddings:

>0.9: Very high similarity; likely same protein family or close homologs
0.7-0.9: High similarity; functionally related proteins, possibly in the same superfamily
0.5-0.7: Moderate similarity; may share functional domains or motifs
<0.5: Low similarity; likely unrelated proteins

Comparison with sequence identity

ESM-3 embeddings capture functional similarity beyond raw sequence identity:

Proteins with 20% sequence identity may have >0.7 embedding similarity if they share a fold
Disordered regions contribute less to embedding similarity than structured regions
Functionally convergent proteins (similar function, different sequences) may cluster together in embedding space

Batch output format

For multi-sequence batch jobs, results are organized per sequence:

Each sequence gets its own embedding vectors, keyed by FASTA header
Pairwise similarity matrix can be computed across all sequences in the batch
Batch statistics include mean embedding magnitude and variance across sequences

Downloading artifacts

NPY files: NumPy arrays of per-residue and per-sequence embeddings (best for Python ML pipelines)
PT files: PyTorch tensors (best for PyTorch-based downstream models)
JSON: Metadata, per-sequence embeddings, and attention summaries
CSV: Per-residue embedding values in tabular format (large files for long sequences)
Contact map PNG: Visualized attention-based contact predictions (if attention maps requested)

Canvas visualization

When an ESM-3 job completes, a result card appears on the canvas showing:

Embedding dimension and model information
Per-residue embedding magnitude plot (highlights important positions)
Attention heatmap (if requested)
Sequence length and processing time

Quality assessment

Embedding quality indicators

Embedding magnitude: Per-residue embedding norms should vary across the sequence; uniform magnitudes may indicate a quality issue
Attention entropy: Attention distributions should be focused (low entropy); uniform attention suggests insufficient model capacity for the input
Consistency: Re-running with the same model and sequence should produce identical embeddings

Common quality concerns

Short sequences (<30 residues): May produce lower-quality embeddings due to limited context
Repetitive sequences: Low-complexity or repetitive regions may have similar embeddings at each position
Non-natural sequences: Synthetic or heavily engineered sequences may fall outside the training distribution

ESM-3 outputs and interpretation