metadata
license: mit
tags:
- biology
- chemistry
- protein-structure
- binding-affinity
- drug-discovery
- biomolecular-modeling
- boltz
- boltz-2
- deep-learning
- structural-biology
Boltz-2: Accurate and Efficient Biomolecular Interaction Prediction
Boltz-2 is an open-source deep learning model for biomolecular interaction prediction, developed by researchers at MIT and Recursion. It extends the capabilities of its predecessor, Boltz-1, by jointly modeling complex 3D structures and binding affinities, achieving near-physics-based accuracy while operating approximately 1000 times faster than traditional methods. This advancement makes accurate in silico screening practical for early-stage drug discovery.
🧬 Model Overview
Boltz-2 is designed to predict:
- 3D structures of biomolecular complexes, including proteins, RNA, DNA, and small molecules.
- Binding affinities, providing insights into molecular interactions critical for drug design.
Key features:
- Joint Modeling: Simultaneously predicts structural conformations and binding affinities.
- Speed and Efficiency: Achieves near-free energy perturbation (FEP) accuracy with significantly reduced computational requirements.
- Open-Source: Released under the MIT license, encouraging both academic and commercial use.