AlphaFold2
Google DeepMind
Predicts single-chain and multimer protein 3D structures from amino acid sequence using MSA-based deep learning. Set the modern benchmark on CASP14.
Best For
Standard structure prediction; validated workhorse for most use cases
License
Open Source (Apache 2.0)
Strengths
- +Production-ready
- +214M+ structures in AlphaFold Database
- +Well-validated across protein families
Limitations
- −Single static state only
- −No ligand co-folding
- −Poor on disordered regions
- −Large assemblies (>2500 residues) problematic
R&D Pipeline Coverage
Related Tools
ColabFold
Steinegger Lab (Seoul National University)
Wraps AlphaFold2 with MMseqs2-based MSA generation, making AF2 runs 40-60x faster. Accessible via Google Colab or local install.
Boltz-1
MIT Jameel Clinic
First fully open-source model achieving AlphaFold3-level accuracy for joint structure prediction of proteins, nucleic acids, and small molecules.
ESMFold
Meta AI (FAIR)
Single-sequence protein structure prediction using the ESM-2 protein language model (15B parameters). No MSA required — fast inference directly from sequence.
AlphaFold3
Google DeepMind / Isomorphic Labs
Joint structure prediction of proteins, DNA, RNA, small molecules, ions, and covalent modifications in a single diffusion-based model.
More in Structure Prediction
ColabFold
Steinegger Lab (Seoul National University)
Wraps AlphaFold2 with MMseqs2-based MSA generation, making AF2 runs 40-60x faster. Accessible via Google Colab or local install.
Boltz-1
MIT Jameel Clinic
First fully open-source model achieving AlphaFold3-level accuracy for joint structure prediction of proteins, nucleic acids, and small molecules.
Boltz-2
MIT + Recursion Pharmaceuticals
Extends Boltz-1 to jointly predict 3D complex structure AND protein-ligand binding affinity. Approaches FEP accuracy at 1,000x lower compute cost.
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