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genie3

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Atualizado12 de maio de 2026 às 11:07

Run de-novo protein design with Genie 3 — a fast, all-atom, SE(3)-equivariant diffusion model. Use this skill when: (1) Generating unconditional monomeric protein backbones (length 50–600+), (2) Solving motif-scaffolding problems (single- and multi-motif), (3) Designing protein binders for a target with hotspot residues, (4) Running beam search or iterative-conditioning binder pipelines, (5) Loading legacy Genie 2 (Cα-trace) checkpoints, (6) Training the Genie 3 model from scratch. This skill covers installation, the unified `genie3` CLI (run / generate / evaluate / status / train), the experiment YAML schema, the three application workflows, multi-GPU + multi-node sharding, problem-set preparation, output layout, and the in-silico success filters. Pairs with: `proteinmpnn` (inverse folding), `colabfold` / `esmfold` (structure prediction back-ends used by the evaluation pipeline), `foldseek` (cluster successful designs), `protein-qc` (QC thresholds), and `binder-design` (tool selection between Genie 3, BindCr

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Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.

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LiorZ

LiorZ/protein-design-skills

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Bioquímicos e biofísicosCiências da Vida, Físicas e Sociais·SOC 19-1021

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name	genie3
description	Run de-novo protein design with Genie 3 — a fast, all-atom, SE(3)-equivariant diffusion model. Use this skill when: (1) Generating unconditional monomeric protein backbones (length 50–600+), (2) Solving motif-scaffolding problems (single- and multi-motif), (3) Designing protein binders for a target with hotspot residues, (4) Running beam search or iterative-conditioning binder pipelines, (5) Loading legacy Genie 2 (Cα-trace) checkpoints, (6) Training the Genie 3 model from scratch. This skill covers installation, the unified `genie3` CLI (run / generate / evaluate / status / train), the experiment YAML schema, the three application workflows, multi-GPU + multi-node sharding, problem-set preparation, output layout, and the in-silico success filters. Pairs with: `proteinmpnn` (inverse folding), `colabfold` / `esmfold` (structure prediction back-ends used by the evaluation pipeline), `foldseek` (cluster successful designs), `protein-qc` (QC thresholds), and `binder-design` (tool selection between Genie 3, BindCraft, RFdiffusion, BoltzGen).
license	MIT
category	design-tools
tags	["structure-design","backbone-generation","motif-scaffolding","binder","diffusion","equivariant"]
repo	https://github.com/aqlaboratory/genie3
preprint	https://www.biorxiv.org/content/10.64898/2026.05.01.722168v1

Genie 3 — Fast All-Atom SE(3)-Equivariant Protein Design

What this is

Genie 3 is a single diffusion model with three application modes selected by the YAML config:

`generation.dataset.source`	`evaluation.version`	What it does
`unconditional`	`unconditional`	De-novo monomers; sweep length range
`motif`	`scaffold`	Motif scaffolding (single or multi)
`target`	`binder`	Binder design against a target PDB + hotspots

All workflows are driven by one CLI: genie3 {run, generate, evaluate, status, train}. A single genie3 conda env covers everything (incl. ColabFold, ESMFold, ProteinMPNN, FoldSeek, TMscore, DSSP).

Prerequisites

Requirement	Minimum	Notes
OS	Linux (x86_64)	Setup script targets Linux
GPU	NVIDIA, CUDA 12+	Required for generation, ColabFold, ESMFold
GPU VRAM	24 GB	40+ GB for long monomers / beam search / large complexes
Conda	Any modern conda	The setup script creates the `genie3` env (Python 3.10)
Disk	~30 GB	ColabFold AF2 multimer weights + pretrained Genie 3 checkpoint + datasets
Network	Internet at setup	HuggingFace + ColabFold MSA server (only at problem-prep time)

Three-step quick start

1) Install (one-time)

git clone https://github.com/aqlaboratory/genie3.git
cd genie3
bash scripts/setup/setup.sh        # creates conda env "genie3", installs all deps
bash scripts/setup/download.sh     # downloads pretrained weights + training data
conda activate genie3

The setup script installs into the genie3 conda env: torch 2.7.1, ColabFold (alphafold-multimer-v3 weights), ESMFold (openfold + ESM2), ProteinMPNN, FoldSeek, TMscore/TMalign, DSSP. See references/setup.md.

2) Pick an example config

genie3 run -c examples/unconditional/experiment.yaml      # de-novo monomers
genie3 run -c examples/motif_scaffolding/experiment.yaml  # motif scaffolding
genie3 run -c examples/binder_design/experiment.yaml      # binder design

run chains generate → evaluate → reduce in three child processes (so GPU memory is freed between stages). To split stages or run multi-node, see step 3.

3) Inspect outputs

Every config sets paths.rootdir. Results land under <rootdir>/results/ (or <rootdir>/<problem_name>/results/ for per-problem tasks). The most useful files:

info.csv — per-design metrics (one row per generated sample)
successful_*_generations/ and v0_success/ — PDBs that passed in-silico filters
successful_*_cluster.csv — FoldSeek clusters at TM 0.5/0.6/0.8

See references/outputs.md for full schema and success criteria.

CLI at a glance

Command	Description
`genie3 run -c <CFG>`	Generate + evaluate + reduce, all in one
`genie3 generate -c <CFG>`	Generation only (one shard)
`genie3 evaluate -c <CFG>`	Evaluate one shard
`genie3 evaluate --reduce -c <CFG>`	Merge shard outputs and produce final CSVs
`genie3 status -c <CFG>`	Show shard/round progress
`genie3 train --config <CFG> -d N`	Train the model

Shared flags: -c/--config, --num-devices N, --shard-id ID, --num-shards N, --verbose, --log-dir <DIR>. Full reference in references/cli.md.

Choosing the right reference doc

You want to…	Read
Install the env or download weights	references/setup.md
Understand every CLI flag and process model	references/cli.md
Author or modify an experiment YAML	references/configuration.md
Run unconditional generation	references/unconditional.md
Run motif scaffolding (single or multi-motif)	references/motif-scaffolding.md
Run binder design (basic)	references/binder-design.md
Use beam search or iterative conditioning for binders	references/iterative-and-beam.md
Build a new binder problem set from PDB targets	references/problem-prep.md
Distribute work across many GPUs / nodes	references/multi-node.md
Read the per-design CSV columns and success filters	references/outputs.md
Train the model from scratch	references/training.md
Diagnose failures	references/troubleshooting.md

Quick decision tree (which application?)

You want diverse de-novo monomers, no constraint → unconditional. Tune direction_scale (0.8 short / 0.0 long) and length range.
You have a functional motif (active site, epitope) and want a scaffold around it → motif. Define the motif PDB with REMARK 999 headers + a problem JSON (segment_config_str).
You have a target PDB and want a binder for it → target. Provide hotspot residues and binder length range. Choose folding mode: template (no MSA) for speed or msa for accuracy.

If you don't know whether Genie 3 is the right tool vs BindCraft / RFdiffusion / BoltzGen, consult the binder-design skill (tool selection guide).

Hard rules / gotchas

Always activate the env first: conda activate genie3. The CLI dispatches to itself via subprocess, so genie3 must be on PATH.
paths.rootdir is the single source of truth for output. Do not also set generation.io.outdir — the loader will reject conflicting values.
Do not set generation.dataset.datadir — use paths.dataset instead. Same for reward.reward.datadir.
First chain in any binder PDB must be the binder chain. The reducer extracts chain A as the binder.
Re-running genie3 run resumes from the last incomplete stage (sharded or iterative). Sentinel files (.generate_done, .evaluate_done, .shard_markers/) drive this.
genie3 evaluate --reduce runs once after all evaluation shards finish. Don't shard the reduce step.
direction_scale heavily affects quality/diversity. Defaults: 0.8 for short monomers (≤300), 0.0 for long monomers (>300) and binders, 0.1 for motif scaffolding.
ColabFold MSAs are only needed for problem prep (prepare.py) and for evaluation.folding.mode: msa. The MSA server is queried over the network at prep time.
Beam search batches: n_sample in beam mode means number of beam-search runs, and the loader auto-divides by beam_width to hit your requested total. See references/iterative-and-beam.md.
Iterative cond_strategy: iter_common* computes the common interface from prior rounds' successes and caches it under <rootdir>/problems/<problem>.json — that is the dataset path used by subsequent rounds.

Where to install the skill

This directory (skill/) is the skill payload. To make it discoverable to a Claude Code agent on this machine:

mkdir -p ~/.claude/skills
ln -s "$(pwd)/skill" ~/.claude/skills/genie3
# or copy:
cp -R skill ~/.claude/skills/genie3

After that, an agent can invoke it via the Skill tool with skill: genie3 (or you can include the marketplace structure if publishing). Skills are loaded by name from ~/.claude/skills/<name>/SKILL.md and may reference files in their own directory.

Citation

@article{lin2026genie3,
  title   = {Fast and Ultra-Capable Protein Design: Advancing the Frontier
             Through Atomistic SE(3)-Equivariance with Genie 3},
  author  = {Lin, Yeqing and Lee, Minji and Vermani, Aakarsh and Jiang, Ellena
             and {De Cooman}, Sebastiaan and Spetko, Matej and AlQuraishi, Mohammed},
  journal = {bioRxiv},
  year    = {2026},
  doi     = {10.64898/2026.05.01.722168}
}