This is just the beginning of what's possible with AI that truly understands the molecular machinery of life. Join us in transforming protein engineering: www.openprotein.ai/early-access...

Ready to try it yourself? PoET-2 is available now on OpenProtein.AI:
- Free academic access
- Python client & APIs
- Web interface

Want to see the technical details? Read our white paper: www.openprotein.ai/a-multimodal-foundation-model-for-controllable-protein-generation-and-representation-learning

The implications are enormous for:
- Drug discovery
- Enzyme engineering
- Protein therapeutics
- And much more

This means PoET-2 doesn't just memorize - it learns fundamental principles of how proteins work, enabling accurate zero-shot variant effect prediction and highly data efficient property learning.

How does it work? PoET-2's tiered attention mechanism processes large protein families with order equivariance and long context lengths, letting it learn from evolutionary examples at inference time.

In real-world testing, PoET-2 can:
- Design proteins with multiple simultaneous constraints
- Learn from just dozens of examples
- Make accurate predictions for challenging proteins
- Run fast inference on standard hardware

PoET-2 introduces a powerful prompt grammar for controlled protein generation - enabling everything from inverse folding to motif scaffolding in a single model.

The results are remarkable:
- 500x more compute efficient than contemporary models
- 30x less experimental data needed for protein optimization
- Improved on structure understanding
- Handles insertions and deletions naturally

Key breakthrough: PoET-2's multimodal architecture learns to reason about sequences, structures, and evolutionary relationships simultaneously through in-context learning.

Most protein language models rely on massive scale - up to 100B parameters - to memorize sequences from nature. PoET-2 takes a fundamentally different approach, learning the grammar of protein evolution.