Unified 3D Modeling for Biology & Medicine
Bridging Generation & Understanding through Autoregressive Learning
Uni-3DAR introduces a groundbreaking framework that unifies 3D structural generation and understanding through autoregressive prediction on compressed spatial tokens.
- Seamlessly integrates previously separate tasks in 3D modeling
- Applies techniques from large language models to biological structures
- Enables more efficient representation of complex molecular structures
- Shows particular promise for drug discovery and medical applications
Why It Matters: This approach creates a foundation for better understanding and generating 3D biological structures like proteins, molecules, and crystals—potentially accelerating drug discovery and material science innovation.