ESMFold Model

An inference-only oracle that runs ESMFold <https://huggingface.co/facebook/esmfold_v1>``_ structure prediction on amino acid sequences and returns a scalar confidence score per candidate. Suitable for use as a black-box oracle inside ``Oracle(scorer=ESMFoldModel(ESMFoldModelConfig(...))).

Three scoring metrics are available, all in the range [0, 1] (higher is better):

Scoring metric interpretation

Metric

What it measures

Interpretation thresholds

ptm (default)

Global structural plausibility of the entire fold. Analogous to TM-score against a hypothetical template; captures whether the sequence adopts a coherent 3-D topology.
> 0.5 — confident fold, well-defined topology
0.2–0.5 — moderate confidence, partially structured
< 0.1 — low confidence; typical for disordered proteins or peptides < ~20 residues

mean_plddt

Per-residue predicted local distance difference test (pLDDT) score, averaged over all non-padding residues. Measures local structural accuracy at the residue level. (ESMFold normalises pLDDT to [0, 1] internally.)
> 0.7 — well-structured, confident local geometry
0.5–0.7 — moderate confidence, flexible or partially ordered regions
< 0.5 — low confidence, likely disordered or unreliable residues

combined

Weighted average w * ptm + (1 − w) * mean_plddt (default w = 0.5). Balances global topology confidence with local residue accuracy.

Inherits the [0, 1] range; apply the same per-metric thresholds above to each component before interpreting the combined score.

Key properties:

  • Input: Amino acid sequences stored in Candidate.data (Modality.SEQUENCE)

  • Output: Mean-only scalar predictions (no uncertainty estimates)

  • Batching: batch_size > 1 is supported only for scoring_metric='mean_plddt'; pTM/combined require batch_size=1

  • CPU use: Backbone is automatically cast to fp32 on CPU to avoid fp16 numerical errors

  • Memory: Pass chunk_size to reduce peak memory for long sequences; use cleanup() to free GPU memory after inference

Note

This model requires the optional esmfold dependency. Install it with:

pip install "transformers>=4.36.0" "accelerate>=0.26.0"