<!-- Source: https://docs.biosimulant.com/examples/boltz-affinity -->

# Structure: Boltz-2 Affinity

Sequence-only protein–ligand structure and binding-affinity prediction using **Boltz-2 v2.0.2**. Provide a protein amino-acid sequence and a ligand SMILES, get back a predicted complex plus binding probability and pIC50.

## What it simulates

- Joint structure and affinity prediction from sequence-only inputs.
- Pinned `boltz[cuda]==2.0.2` driven by the upstream Boltz CLI on a GPU runner.
- Diffusion + recycling pipeline with optional MSA via the configured server.
- Bundled example protein and tyrosine-derivative ligand so a fresh run produces a renderable complex without setup.

{/* TODO: add screenshot once Boltz lab assets are captured */}

## Run it on the Hub

1. Open the [Boltz: Boltz2AffinityPredictor Lab](https://hub.biosimulant.com/labs/64c7f06d-f307-4169-b600-1e5dc080f407) on the public Hub.
2. Click **Run**. The bundled defaults predict the example complex without any parameter editing.

## Inputs you can tune

| Input | Meaning |
|---|---|
| `protein_sequence` | Amino-acid sequence string. |
| `ligand_smiles` | SMILES string for the ligand. |
| `msa_path` | Optional path to a pre-computed MSA (`.a3m`). When unset and `use_msa_server: true`, Boltz queries the configured MSA server. |
| `run_options.recycling_steps` | Recycling iteration count. |
| `run_options.sampling_steps` | Diffusion sampling steps. |
| `run_options.diffusion_samples` | Number of diffusion samples. |
| `run_options.accelerator` | Compute accelerator (`gpu` recommended; CPU is plumbing-only). |

## What results to expect

- **3D structure view**: predicted protein–ligand complex assembled from the top-ranked Boltz output. Sanity-check that the ligand lands in a plausible pocket on the predicted fold.
- **Affinity summary**: binding probability (0–1) and predicted affinity in pIC50. Useful for ranking related candidates against the same target; less useful as absolute numbers.
- **Confidence summary**: pTM and ipTM (global / interface fold confidence, 0–1) and pLDDT (per-residue confidence, 0–100). Low ipTM with reasonable pTM usually means the fold is fine but ligand placement is uncertain.
- **Run metadata**: Boltz version, output directory, truncated stdout/stderr, and `status: ok` / `status: error`.

## Reference

[Boltz-2](https://github.com/jwohlwend/boltz) v2.0.2.

**Info:**

  Source on GitHub: [models-boltz](https://github.com/Biosimulant/models-boltz). Boltz-2 is GPU-bound: remote runs use the GPU-enabled runtime image; local runs need a CUDA device.