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### Boltz-2 Structure Prediction (`boltz2-prediction`) Predicts the 3D structure of a biomolecular complex. Each input complex can combine protein, DNA, RNA, and ligand chains; the assembly is folded by Boltz-2 and returned as a predicted `Structure` per complex with confidence metrics: a complex pLDDT, pTM, interface pTM, per-chain and pairwise-chain pTM/ipTM, and predicted aligned error. #### API Reference

Source List of complexes to predict structures for. Inherited from `StructurePredictionInput`. Each complex can contain multiple chains of proteins, DNA, RNA, and/or ligands. Chains in the complex, in input order. Pre-computed MSAs, one entry per complex. Each entry is a `ComplexMSAs` (per-chain MSAs keyed by chain index); `paired=True` marks rows taxonomy-aligned across chains. Populated by preprocess() or supplied directly.

Source Iterative refinement passes through the model. Higher = more accurate but slower. Default 3 (matches upstream). Denoising steps in the diffusion process. Higher = more refined but slower. Default 200 (matches upstream). Independent structure samples per complex; the best by confidence is returned. Default 1 (matches upstream). Diffusion step size (typical range 1.0-2.0). Lower = more sample diversity. Default 1.5 (matches upstream). Maximum number of MSA sequences fed into the model. Lower to reduce GPU memory on deep MSAs. Default 8192. Randomly subsample the MSA on each run for sample diversity (loses determinism). Default False. Number of dataloader workers for prediction. Automatically set to the minimum of available CPU cores or 4. Must be at least 1. Default: `min(cpu_count, 4)`. Whether to print status messages during execution including MSA generation, model loading, and prediction progress. Inherited from `StructurePredictionConfig`. Default: `False`. Device to run the model on. Options include `"cuda"` (NVIDIA GPU), `"cpu"` (CPU execution), or specific GPU devices like `"cuda:0"`. Structure prediction is computationally intensive and strongly benefits from GPU acceleration. Default: `"cuda"`. Maximum execution time in seconds. `None` waits indefinitely. Default: 1200. Random seed. When set, tools run reproducibly up to small GPU float noise (see `BaseToolOutput.approx_equal`), and the seed participates in cache keys. When None, cacheable seed-sensitive tools skip cache until seeded. Attach `pae` (`avg_pae` always emitted). Default: `False`. Whether to generate and use Multiple Sequence Alignments (MSAs) for protein chains using MMseqs2 homology search. Supplied MSAs are always used and override `use_msa=False`. Inherited from `MSAStructurePredictionConfig`. Default: `True`. Configuration for MMseqs2 homology search (MSA generation). Only used when `use_msa=True`. Inherited from `MSAStructurePredictionConfig`. Default: `None`. Whether heterocomplex protein chains should use taxonomy-paired MSA generation. Inherited from `MSAStructurePredictionConfig`. Default: `True`.

Source Predicted structures, each carrying a :class:`Boltz2Metrics` instance on `.metrics`. Raw structure content in PDB or CIF format. Format of the content string (auto-detected if omitted). What the B-factor column represents. Optional source identifier (filepath or tool name). Associated metrics (e.g., pLDDT, pTM scores, per-chain lists, pairwise matrices). None values are stripped at construction. **Metrics** (one set per `structures` item) | Metric | Type | Range | Availability | | ------------------ | ------------------- | ----------- | ------------------------------ | | `confidence_score` | float | 0.0 to 1.0 | always | | `ptm` | float | 0.0 to 1.0 | always | | `iptm` | float | 0.0 to 1.0 | always | | `chains_ptm` | list\[float] | 0.0 to 1.0 | always | | `pair_chains_iptm` | list\[list\[float]] | 0.0 to 1.0 | always | | `avg_pae` | float | 0.0 to 32.0 | always | | `pae` | list\[list\[float]] | 0.0 to 32.0 | when include\_pae\_matrix=True | | `ligand_iptm` | float | 0.0 to 1.0 | depends on complex composition | | `protein_iptm` | float | 0.0 to 1.0 | depends on complex composition | | `complex_plddt` | float | 0.0 to 1.0 | depends on complex composition | | `complex_iplddt` | float | 0.0 to 1.0 | depends on complex composition | | `complex_pde` | float | ≥ 0.0 | depends on complex composition | | `complex_ipde` | float | ≥ 0.0 | depends on complex composition |

#### Applications This tool predicts the structure of multi-component assemblies such as protein-DNA and protein-RNA complexes or protein-ligand binding poses. Running it on a multi-chain complex also estimates how confidently the components are placed relative to each other through interface pTM and PAE, which is informative for assessing predicted interfaces. #### Usage Tips * **`use_msa` defaults to `True`.** A ColabFold search generates an MSA for each protein chain; set it `False` for single-sequence prediction, or attach precomputed MSAs to the input. Protein chains with no detectable homologs fall back to an empty MSA. * **Structures come from a diffusion process.** `diffusion_samples` (default `1`) independent samples are drawn per complex and the best is kept by `confidence_score`; `sampling_steps` (default `200`) sets the number of denoising steps and `step_scale` (default `1.5`) trades accuracy for sample diversity, where lower values are more diverse. * **`recycling_steps` (default `3`) trades accuracy for time.** More recycling iterations refine the prediction but increase runtime. * **Confidence is reported as a complex pLDDT, pTM, ipTM, and PAE.** `confidence_score`, the primary metric, is `iptm` for multi-chain complexes and `ptm` for a single chain; `complex_plddt` is on a 0 to 1 scale and PAE is in angstroms (0 to about 32). Set `include_pae_matrix` to attach the full per-token PAE matrix. * **Multi-modal inputs.** Protein, DNA, RNA, and ligand entities are supported; chain modifications are not.

### Boltz-2 Affinity (`boltz2-affinity`) Predicts the binding affinity of a single small-molecule ligand against a protein target. Each input complex must contain at least one protein chain and at least one ligand chain; the binder is the complex's sole ligand (auto-detected) or the chain named by `binder_chain`. Each complex returns a predicted `Structure` with the binding pose in the CIF and the affinity scores on `structure.metrics`: `affinity_pred_value` (log10 IC50 in μM; lower is stronger binding) and `affinity_probability_binary` (binder probability in \[0, 1]). #### API Reference

Source Ligand to score per complex; one value broadcasts to all complexes, None auto-detects each complex's sole ligand. Each needs >=1 protein target and >=1 ligand chain. Chains in the complex, in input order. Inherited per-complex MSAs; each entry is a `ComplexMSAs` (`paired=True` for taxonomy-paired heterocomplexes).

Source Apply molecular-weight correction to the affinity value head. Default: `False`. Denoising steps for the affinity pass. Default: `200`. Diffusion samples per complex for the affinity pass. Default: `5`. Whether to print status messages during execution including MSA generation, model loading, and prediction progress. Inherited from `StructurePredictionConfig`. Default: `False`. Device to run the model on. Options include `"cuda"` (NVIDIA GPU), `"cpu"` (CPU execution), or specific GPU devices like `"cuda:0"`. Structure prediction is computationally intensive and strongly benefits from GPU acceleration. Default: `"cuda"`. Maximum execution time in seconds. `None` waits indefinitely. Default: 1200. Random seed. When set, tools run reproducibly up to small GPU float noise (see `BaseToolOutput.approx_equal`), and the seed participates in cache keys. When None, cacheable seed-sensitive tools skip cache until seeded. No-op for affinity; excluded from the cache key. Default: `False`. Inherited. Use MMseqs2 MSAs for protein chains. Supplied MSAs are always used and override `use_msa=False`. Default: `True`. Inherited. MMseqs2 homology-search config. Default: `None`. Inherited. Use taxonomy-paired MSA generation for heterocomplex protein chains. Default: `True`. Inherited. Refinement passes for the structure pass. Default: `3`. Inherited. Denoising steps for the structure pass. Default: `200`. Inherited. Structure samples per complex. Default: `1`. Inherited. Diffusion step size for the structure pass. Default: `1.5`. Inherited. Cap on MSA depth fed into the model. Default: `8192`. Inherited. Randomly subsample the MSA each run. Default: `False`. Inherited. Dataloader workers for prediction. Default: `min(cpu_count, 4)`.

Source List of predicted structures, one per input complex. Each structure contains the 3D coordinates in CIF format along with model-specific confidence metrics. The order matches the input complexes order. Raw structure content in PDB or CIF format. Format of the content string (auto-detected if omitted). What the B-factor column represents. Optional source identifier (filepath or tool name). Associated metrics (e.g., pLDDT, pTM scores, per-chain lists, pairwise matrices). None values are stripped at construction. **Metrics** (one set per `structures` item) | Metric | Type | Range | Availability | | ------------------------------ | ----- | ---------- | ------------------------------------ | | `affinity_pred_value` | float | unbounded | always | | `affinity_probability_binary` | float | 0.0 to 1.0 | always | | `affinity_pred_value1` | float | unbounded | when ensemble emits per-model values | | `affinity_probability_binary1` | float | 0.0 to 1.0 | when ensemble emits per-model values | | `affinity_pred_value2` | float | unbounded | when ensemble emits per-model values | | `affinity_probability_binary2` | float | 0.0 to 1.0 | when ensemble emits per-model values |

#### Applications This tool ranks candidate ligands against a chosen protein target, pairing a predicted affinity with a predicted binding pose — supporting hit discovery, structure-activity studies, and library-screening loops over a list of SMILES. #### Usage Tips * **`affinity_pred_value` is on a log10-IC50 (μM) scale.** Values below `0` (sub-μM IC50) indicate strong binders; positive values indicate weaker binding. `affinity_probability_binary` is an independent binder probability and can stay high even when the IC50 estimate is uncertain. * **One binder ligand per complex.** The binder is auto-detected when a complex has exactly one ligand; set `binder_chain` to name it when a complex has several. `binder_chain` is per-complex: pass one value (e.g. `["B"]`) to apply it to every complex, or one per complex (e.g. `["B", None]`, where `None` auto-detects). The binder must be a ligand chain with at most 128 heavy atoms. * **Structure-side and affinity-side settings are independent.** `recycling_steps`, `sampling_steps`, `diffusion_samples`, and MSA settings control the structure pass that runs first; `sampling_steps_affinity` (default `200`) and `diffusion_samples_affinity` (default `5`) control the affinity pass. Set `affinity_mw_correction` to apply Boltz-2's molecular-weight correction to the affinity value head. * **Stochastic predictions.** The diffusion-based affinity head is stochastic; set `seed` for reproducibility.