aqlaboratory

aqlaboratory / genie3

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Genie 3 is a fast, all-atom SE(3)-equivariant diffusion model for protein design. It achieves state-of-the-art performance on unconditional generation, motif scaffolding, and binder design while retaining the computational efficiency of equivariant architectures.

www.biorxiv.orgcontent10.648982026.05.01.722168v1
48
9
100% credibility
Found May 09, 2026 at 48 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Genie 3 is a research tool for generating realistic protein structures using AI diffusion models, supporting standalone proteins, motif scaffolding, and binder design with built-in evaluation.

How It Works

1
🔍 Discover Genie 3

You find this exciting tool for creating new protein shapes, perfect for designing binders or scaffolding motifs.

2
🛠️ Set up your workspace

Run a simple setup script to prepare everything you need on your computer.

3
📥 Grab the models

Download ready-made protein models and example data with one quick command.

4
Choose your design goal
🧬
New proteins

Generate standalone protein structures of any length.

🔗
Scaffold motifs

Build proteins that incorporate specific structural motifs.

🧲
Design binders

Create proteins that bind to your target molecule.

5
📝 Tweak your recipe

Adjust a simple settings file for lengths, numbers, or targets using easy examples.

6
🚀 Launch your designs

Hit one command and watch as Genie creates and checks hundreds of protein candidates.

✅ Celebrate your proteins

Browse stats, successful structures, and ready-to-use files to explore your new protein family.

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Star Growth

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AI-Generated Review

What is genie3?

Genie3 is a Python diffusion model for fast protein design at all-atom resolution, using SE(3)-equivariant networks to generate novel structures. It handles unconditional protein generation, motif scaffolding, and binder design against targets, with state-of-the-art benchmarks on tasks like BinderBench. Users get a CLI for one-command runs, pretrained weights via Hugging Face, and full evaluation pipelines including folding and clustering.

Why is it gaining traction?

Pretrained checkpoints and example YAML configs let you generate and evaluate designs in minutes, scaling to multi-GPU or multi-node via simple sharding flags. Features like beam search, iterative conditioning on prior successes, and integrated ColabFold/ESMFold reward models deliver high-quality outputs without custom pipelines. Efficiency from equivariant design keeps it fast even on long proteins.

Who should use this?

Protein engineers designing binders for drug targets or antigens. Structural biologists scaffolding functional motifs into stable scaffolds. ML researchers prototyping diffusion-based protein generators on datasets like AFDB or PiNDER.

Verdict

Solid pick for genie3 ai protein workflows with excellent CLI and docs, but 48 stars and 1.0% credibility signal early maturity – verify on your data before big runs. Pairs well with genie3 gene regulatory network tools for multi-omics design.

(198 words)

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