Boltz-1

Extends Boltz-1 to jointly predict 3D complex structure AND protein-ligand binding affinity. Approaches FEP accuracy at 1,000x lower compute cost.

Joint structure prediction of proteins, DNA, RNA, small molecules, ions, and covalent modifications in a single diffusion-based model.

Multi-modal foundation model for joint structure prediction of proteins, small molecules, DNA, RNA, and glycosylations. Performs well in single-sequence mode.

Fully open-source PyTorch reproduction of AlphaFold3 architecture. Protenix-v1 (Feb 2026) reported to outperform AF3 across diverse benchmarks.

Predicts single-chain and multimer protein 3D structures from amino acid sequence using MSA-based deep learning. Set the modern benchmark on CASP14.

Wraps AlphaFold2 with MMseqs2-based MSA generation, making AF2 runs 40-60x faster. Accessible via Google Colab or local install.

Extends Boltz-1 to jointly predict 3D complex structure AND protein-ligand binding affinity. Approaches FEP accuracy at 1,000x lower compute cost.