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EGFR kinase with gefitinib (cancer drug target)
HIV-1 protease homodimer with darunavir (antiviral)
p53 tumor suppressor bound to DNA (transcription factor)
Controllable biomolecular structure prediction model for proteins, ligands, DNA, RNA, and multi-component complexes. IntelliFold 2 supports fast v2-Flash inference, optional MSA generation, and ranked confidence outputs.
OpenFold-3 is an open-source AI model for biomolecular structure prediction, aiming to reproduce AlphaFold3. Predicts 3D structures for proteins, RNA, DNA, and small molecule ligands with high accuracy.
Enhanced Protenix v2 biomolecular structure prediction by ByteDance. Predicts 3D structures for proteins, RNA, DNA, and small molecule ligands with high accuracy.
Open-source structure prediction neural network for proteins, nucleic acids, and small molecules. State-of-the-art accuracy with multi-chain support.
Boltz-2 is a biomolecular foundation model for structure and binding affinity prediction. Supports proteins, ligands, DNA, and RNA in multi-component complexes. Automatically scales GPU resources for large complexes. Predicts binding affinity with near-FEP accuracy at 1000x faster speed.
LMI4Boltz is a low-memory fork of Boltz for biomolecular structure and binding affinity prediction. It preserves Boltz inference behavior while reducing VRAM use with in-place pair updates, CPU offload, reduced precision pair representation, and aggressive chunking.
ABodyBuilder3 predicts antibody variable-domain structures from paired heavy and light chain sequences. It returns a PDB structure and, for the pLDDT checkpoint, per-residue confidence values.
Generate protein conformational ensembles with ESMFlow, the single-sequence AlphaFlow model family. Produces multiple diverse structures showing protein flexibility and dynamics.
AlphaFold2 via ColabFold for high-accuracy protein structure prediction. Uses MMSeqs2 API for MSA generation with no local databases required. Supports monomer and multimer prediction.
ESMfold is a fast, single-sequence protein structure predictor from Meta AI. Predicts 3D protein structures directly from amino acid sequences without requiring multiple sequence alignments (MSA), making it significantly faster than AlphaFold while automatically scaling GPU resources for larger proteins.
Chai-1 is a multi-modal foundation model for molecular structure prediction developed by Chai Discovery. Released in October 2024, it can predict 3D structures for proteins, small molecules, DNA, RNA, and multi-component complexes containing any combination of these molecular types.
Chai-1 addresses a key limitation of earlier structure prediction tools by supporting truly unified multi-modal predictions. Rather than requiring separate models for different molecular types, Chai-1 handles everything in a single framework—including protein-ligand complexes, protein-DNA interactions, and systems with post-translational modifications.
Since its release, the Chai-1 performance and capabilities have been surpassed by Boltz-2 and BoltzGen.
Chai-1 uses a diffusion-based architecture to generate molecular structures. Like image generation models, it starts from random noise and iteratively refines toward physically plausible 3D coordinates through learned denoising steps.
The model learns to reverse a diffusion process that gradually corrupts molecular structures with noise. During inference, it starts from random atomic coordinates and progressively denoises them through hundreds of steps to produce a coherent structure.
Each diffusion step adjusts atomic positions based on the model's learned understanding of molecular geometry, chemical bonding, and inter-molecular interactions. More diffusion steps generally produce higher-quality structures at the cost of increased computation time.
For protein structure prediction, Multiple Sequence Alignment (MSA) provides crucial evolutionary information. MSA aligns your target protein against thousands of homologous sequences from different organisms.
Coevolution patterns in the MSA reveal which residues are spatially close in 3D—if two positions consistently mutate together across evolution, they're likely in contact. This signal is one of the most powerful inputs for modern structure prediction.
Chai-1 can automatically generate MSAs via the ColabFold server, searching sequence databases to find homologs for each protein chain.
Chai-1 uses a recycling mechanism where the model feeds its prediction back through the network multiple times. Each recycling step allows the model to refine its prediction based on the previous iteration, progressively improving accuracy.
This is similar to how AlphaFold2 works—the model essentially "thinks twice" about difficult regions, using information from earlier predictions to resolve ambiguities.
Unlike models trained exclusively on proteins, Chai-1 was designed from the ground up to handle diverse molecular types. Proteins, nucleic acids, and small molecules are all represented in a unified framework that captures their distinct chemical properties while enabling cross-modal interactions.
This unified architecture is what enables Chai-1 to predict complex multi-component systems like protein-ligand complexes, protein-DNA interactions, or systems with covalently attached modifications.
Chai-1 accepts multiple molecule types that you can combine into a single prediction job. Chain IDs (A, B, C...) are assigned automatically and displayed in the UI—you'll need these when defining constraints.
MSA (Multiple Sequence Alignment) provides evolutionary context that substantially improves protein structure prediction accuracy. Templates are known experimental structures from homologous proteins that can guide the prediction.
Constraints guide the prediction toward specific structural features when you have prior knowledge about the system. This is particularly useful when you know the binding site from experimental data or have crosslinking mass spectrometry data.
chain:residue,chain:residue|max_distance. Example: A:10,B:5|8.0 constrains residue 10 of chain A within 8Å of residue 5 in chain B.ligand_chain|protein:res1,protein:res2|max_distance. Example: C|A:45,A:46|6.0 constrains chain C (ligand) near residues 45–46 of chain A (protein).Post-translational modifications (PTMs) can significantly affect protein structure. Chai-1 supports common modifications using standard CCD (Chemical Component Dictionary) codes from the PDB.
chain:position:CCD_code. Common codes: SEP (phosphoserine), TPO (phosphothreonine), PTR (phosphotyrosine), MSE (selenomethionine), MLY (methylated lysine).Chai-1 returns multiple ranked predictions, each with a structure file and associated confidence information.
Each prediction produces a CIF structure file representing one possible conformation. We recommend examining the top 2–3 structures rather than relying solely on the highest-ranked prediction—especially for flexible complexes or multi-domain proteins.
If predictions differ substantially across samples, this may indicate:
In such cases, consider enabling MSA generation or increasing the number of samples to better explore conformational space.
Enable MSA for proteins. The evolutionary information from MSA substantially improves prediction accuracy. Only disable it when working with synthetic proteins that lack natural homologs, or when you need a quick rough estimate.
Use experimental structures when available. If you have an experimental structure for part of your complex (e.g., the protein receptor), provide it directly rather than predicting from sequence.
Check your SMILES stereochemistry. Ligand binding is highly stereospecific. Make sure your SMILES strings correctly represent the enantiomer you intend to model.
| Use case | Samples | MSA | Diffusion steps | Notes |
|---|---|---|---|---|
| Quick screening | 1–3 | Off | 100–150 | Fast turnaround, lower accuracy |
| Standard prediction | 5 | On | 200 | Good balance for most cases |
| High-accuracy | 5–10 | On | 300–500 | Use for final candidates |
| Conformational diversity | 10 | On | 200 | Captures structural diversity |
Pocket constraints are valuable when:
Contact constraints are useful for: