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RomeroLab / alphafast

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AlphaFast: ultra-high-throughput AlphaFold3 inference with MMSeqs2-GPU

87
9
100% credibility
Found Feb 25, 2026 at 78 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

AlphaFast accelerates AlphaFold 3 protein structure prediction using GPU-optimized homology search, supporting multi-GPU scaling and serverless cloud deployment.

How It Works

1
🔍 Discover AlphaFast

You learn about a tool that predicts protein shapes much faster than before, perfect for scientists studying biology.

2
📝 Request Special Files

You fill out a quick form to get the thinking files from Google, which arrive in a few days.

3
☁️ Pick Easy Online Setup

You choose the simple cloud option that handles everything without needing powerful computers.

4
🚀 Upload and Prepare

You upload your files and prepare the building blocks with one command, everything sets up automatically.

5
📄 Add Your Proteins

You create simple lists of your protein recipes in everyday text files.

6
▶️ Start Prediction

You press go and watch as the tool thinks and builds shapes lightning-fast.

🎉 See Your Results

Beautiful 3D protein models appear, ready to explore and use in your research.

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Star Growth

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AI-Generated Review

What is alphafast?

AlphaFast is a Python toolkit for ultra-high-throughput AlphaFold3 inference, replacing slow CPU homology searches with MMseqs2-GPU acceleration to slash end-to-end runtimes by over 22x on a single H200 GPU. It processes protein complexes via simple JSON inputs, outputting structures and confidences, with linear scaling to 4.5s per input on 8 GPUs. Users run it via Docker on local servers, SLURM on HPC, or serverless on Modal.

Why is it gaining traction?

AlphaFast stands out by fixing AlphaFold3's MSA bottleneck without accuracy loss, delivering 68x faster searches and seamless multi-GPU batching through phase-separated pipelines. Its one-command scripts handle database prep, weights placement, and inputs, plus a Modal setup for $0.035 predictions in 28s—no infra hassles. Developers flock to it for turning hours-long jobs into minutes on commodity GPUs.

Who should use this?

Structural biologists batch-folding complexes for drug design or variant screening on GPU clusters. Bioinformatics teams on university HPC running thousands of predictions overnight via SLURM. Solo researchers prototyping via the alphafast Modal tab without managing Docker or databases.

Verdict

AlphaFast earns a strong buy for scaled AlphaFold3 inference—mature docs, easy CLIs, and proven speedups make it production-ready despite 74 stars and 1.0% credibility reflecting its youth. Test it if GPU throughput matters; pair with the bioRxiv preprint for confidence.

(198 words)

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