Predict controllable biomolecular structures for proteins, ligands, DNA, RNA, and complexes. Learn more
Input
Inputs
Add a molecule to begin
Choose a building block to assemble your structure.
Settings
Output
Configure inputs to begin
Set options on the left, then click “Submit job”.
What is IntelliFold 2?
IntelliFold 2 is an all-atom biomolecular structure prediction model for proteins, ligands, DNA, RNA, and mixed complexes. ProteinIQ runs the open-source IntelliFold 2.0.4 release on its vendored AlphaFold 3 JAX engine.
The model generates several diffusion samples for each seed, scores them, and returns the complete native result set. Optional physical steering can guide ligand poses using differentiable geometric and chemical potentials.
Applications
- Single-chain and multimer protein structure prediction
- Protein-ligand complex modeling
- Protein-DNA and protein-RNA complex modeling
- Alternative-conformation sampling across seeds and diffusion samples
- Physically guided ligand-pose generation
How to use IntelliFold 2 online
Inputs
At least one protein, DNA, or RNA chain is required. The combined limit is 3,000 residues after molecule copies are counted.
| Input | Description |
|---|---|
Protein | One FASTA sequence per entry, supplied as text, .fasta/.fa, or an RCSB sequence fetch. |
Precomputed MSA | Optional protein alignment in A3M format. Multiple A3M files are matched to protein inputs in submission order. |
Ligand (SMILES) | One SMILES string per entry, entered directly or fetched from PubChem. |
Ligand (CCD) | One PDB Chemical Component Dictionary code, such as ATP, NAD, or SAH. |
DNA | One DNA sequence per FASTA entry. |
RNA | One RNA sequence per FASTA entry. |
Molecule copies, chain IDs, and supported protein or nucleic-acid modifications are carried into the AlphaFold 3 version 4 input contract. Cyclic polymers are rejected because IntelliFold 2.0.4 does not define a cyclic-chain input field.
When no A3M is supplied, ProteinIQ runs the source-supported single-sequence path. It does not claim to generate an MSA remotely; the 2.0.4 data pipeline requires large local sequence databases that are not part of this deployment.
Settings
| Setting | Description |
|---|---|
Diffusion samples | Structures generated per seed (1-10, default 5). |
Recycling iterations | Model recycling passes (1-12, default 10). |
Seeds | One integer seed or up to 10 unique comma-separated seeds. Duplicate numeric seeds are removed. |
Enable steering | Turns on IntelliFold's physical and chemical diffusion guidance. Off by default. |
Steering optimization steps | Gradient-descent iterations per denoising step when steering is enabled (default 20). |
Steering weight scale | Global multiplier for the steering potentials (default 1.0). |
IntelliFold 2.0.4 uses one published v2 checkpoint and writes mmCIF. Earlier v1, v2, v2-Flash, sampling-step, MMseqs2, and PDB-output controls belonged to the previous inference implementation and are not options in the current release.
Output
ProteinIQ returns every native text artifact produced by the run:
| Output | Description |
|---|---|
| Ranked sample CIF files | One predicted structure for every seed and diffusion sample. |
| Top-ranked CIF copy | The source-selected best model at the root of the result directory. |
| Summary confidence JSON | Ranking score and model-level confidence metrics for each structure. |
Full confidence JSON (.zst) | Per-atom and pairwise confidence data produced by the model, using the source-supported Zstandard compression. |
| Ranking CSV | Native seed, sample, and ranking-score rows in source order. |
| Processed input JSON | The exact AlphaFold 3 input consumed by inference. |
| Terms file | The native output terms supplied by the AlphaFold 3 engine. |
The data table uses the native ranking_score; higher values rank first in the structure viewer. The original ranking CSV remains downloadable without rewriting its row order.
How does IntelliFold 2 work?
The 2.0.4 release runs IntelliFold's converted v2 parameters through an AlphaFold 3 JAX inference engine. Protein, nucleic-acid, ligand, MSA, copy, chain, and modification data are serialized to the official AlphaFold 3 JSON dialect before invoking the published intellifold predict command.
Diffusion sampling and recycling
Each seed initializes an independent model trajectory. Diffusion samples broaden the generated ensemble, while recycling feeds intermediate representations through the network again for refinement. Increasing either control raises compute cost.
Optional physical steering
Steering adds gradient guidance for physical and chemical constraints during diffusion, including ligand geometry and clash-related potentials. Because steering changes the scientific run and increases computation, it is disabled unless explicitly selected.
Interpreting results
Start with the native ranking score, then inspect the summary and full confidence JSON for the selected sample. Confidence metrics may include pTM, ipTM, disorder, and clash-related values depending on the system. Agreement among high-ranked samples is often more informative than one structure alone.
Limitations
- Predictions are computational hypotheses, not experimental measurements.
- Flexible regions, weak interfaces, novel chemistry, and sparse sequence information can reduce reliability.
- This deployment supports uploaded A3M data or single-sequence inference; it does not run the source database search pipeline.
- The current source release writes CIF structures, not PDB files.
- Total protein, DNA, and RNA length is limited to 3,000 residues after copies.
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