# Alpha_BioAgent

- **Event:** [Gemini 3 서울 해커톤](https://cerebralvalley.ai/e/gemini-3-seoul-hackathon)
- **When:** Sat, Feb 28 at 9:00 AM – 10:00 PM (GMT+9)
- **Where:** 서울 Seocho District, Olympic-daero, 2085-14 세빛둥둥섬
- **Placement:** Finalist
- **Team:** [Sehoon Park](https://cerebralvalley.ai/u/SehoonPark)
- **GitHub:** https://github.com/sehooni/Alpha_Agent
- **Demo video:** https://youtu.be/QKJIS8hlva4
- **Gallery:** https://cerebralvalley.ai/e/gemini-3-seoul-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/gemini-3-seoul-hackathon/hackathon/gallery/32

🔥 The Problem: AI "Hallucinations" in Biology
Normal AIs (like ChatGPT) are great at talking but bad at physics. If you ask an AI to fix a protein, it often suggests adding a big molecule into a small space. In text, it sounds perfectly logical. But in a real lab, that protein would crash into itself and instantly fall apart (Steric Clash). We call this "Structural Hallucination."

✅ The Solution: Teaching AI to Use Physics Tools
Alpha-Agent solves this by forcing the AI to use physical tools (like the Bio.PDB Python library):

It Calculates First: Before the AI proposes a mutation, it secretly runs a Python script to measure the exact 3D distance between atoms.
It Corrects Itself: If the physical measurement shows that the AI's idea won't fit, the AI realizes its mistake and automatically recalculates until it finds a design that actually works in the real world.
In short: Alpha-Agent anchors the AI to reality, giving researchers protein designs they can actually trust in the lab.

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