Menu

Individual skill

structure-prediction

Protein structure prediction from sequence. ESMFold-based, single GPU, no MSA needed. Predicts 3D structures with pLDDT confidence scores for drug discovery targets.

backend/cli/skills/chemistry/structure-prediction/SKILL.md

Read pinned source

Source commit a08e0ad0ad3d6448123039ee4659163eb2e35d99

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
Yes
Repository scan complete
No
Skill scan complete
No
Observed behavior
Network or broad shell
Last checked
Nov 2, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
4,351
Repository last updated
Nov 6, 2026
License
Apache-2.0

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

# Structure Prediction (ESMFold)

## Overview

This skill provides protein 3D structure prediction from amino acid sequences using **ESMFold** (Evolutionary Scale Modeling Fold). ESMFold is a single-sequence protein structure prediction model developed by Meta AI that produces accurate 3D coordinates directly from an amino acid sequence without requiring multiple sequence alignments (MSA) or template search.

Key advantages of ESMFold:
- **No MSA required**: Predictions run on a single sequence, making inference dramatically faster than AlphaFold2 (seconds vs. minutes/hours).
- **Single GPU execution**: The entire model fits on one GPU (requires ~16 GB VRAM for sequences up to ~400 residues, more for longer sequences).
- **End-to-end**: Takes a raw amino acid string and outputs a full PDB structure with per-residue confidence scores (pLDDT).
- **Drug discovery ready**: Suitable for rapid screening of target structures, variant modeling, and initial structural hypotheses.

## When to Use This Skill