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### Chai-1 Structure Prediction (`chai1-prediction`) Predicts the 3D structure of a biomolecular complex. Each input complex can combine protein, ligand, and glycan chains; the assembly is folded by Chai-1 and returned as a predicted `Structure` per complex with confidence metrics: average pLDDT, pTM, interface pTM, predicted aligned error, and an overall confidence score. #### API Reference

Source List of complexes to predict structures for. Inherited from `StructurePredictionInput`. Each complex can contain multiple chains of proteins, ligands, and/or glycans. Total token count per complex must not exceed 2,048 (see `Note` below). 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 Whether to use ESM (Evolutionary Scale Modeling) embeddings for improved predictions. ESM embeddings provide evolutionary context from large-scale protein language models, typically improving prediction quality. Independent of `use_msa`; both can be enabled together and Chai-1 conditions on the ESM embeddings and the MSA simultaneously. Default: `True`. Number of iterative refinement passes through the trunk network. Higher values produce more refined structures but increase computation time. Typical range: 0-10. Must be at least 0. Number of denoising steps in the diffusion process. Higher values produce more refined structures but are slower. Typical Number of independent structure samples to generate per complex via the diffusion process. Only the best sample (by confidence) is returned. Higher values explore more conformational space but increase computation time. Must be at least 1. Default: 5. Number of independent trunk forward passes per diffusion sample. Increases diversity in structure generation. Must be at least 1. Default: 1. Stream MSA + template features per sample to reduce peak GPU memory at the cost of speed. Default: True. Stochastically subsample MSA across recycles for diversity. 0 disables (default). Whether to print status messages during execution. Inherited from `StructurePredictionConfig`. Default: `False`. Device to run the model on (`"cuda"`, `"cpu"`). Inherited from `StructurePredictionConfig`. 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. Inherited. 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:`Chai1Metrics` 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 | | ------------------ | ------------------- | ---------- | ------------------------------ | | `avg_plddt` | float | 0.0 to 1.0 | always | | `ptm` | float | 0.0 to 1.0 | always | | `iptm` | float | 0.0 to 1.0 | always | | `avg_pae` | float | ≥ 0.0 | always | | `pae` | list\[list\[float]] | ≥ 0.0 | when include\_pae\_matrix=True | | `confidence_score` | float | 0.0 to 1.0 | always |

#### Applications This tool predicts the structure of multi-component assemblies such as protein-ligand binding poses and glycosylated proteins, which makes it well suited to drug-discovery screening and modeling carbohydrate-decorated targets. For a multi-chain complex it also reports how confidently the chains are placed relative to one another: interface pTM (ipTM) gives a single 0-to-1 score for the overall inter-chain arrangement, and the cross-chain blocks of the PAE matrix show which inter-chain regions are positioned confidently versus uncertainly, so you can rank or filter predicted complexes before trusting a pose downstream. #### Usage Tips * **Total length is capped at 2,048 tokens per complex** (1 per amino-acid residue, 1 per heavy atom for ligands and glycans); longer inputs are rejected. * **`use_esm_embeddings` defaults to `True`.** Chai-1 conditions on embeddings from an ESM protein language model; they are used with or without an MSA. * **`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. * **Sampling and refinement are configurable.** `num_diffn_samples` (default `5`) independent samples are drawn per complex and the best is kept by `confidence_score`; `num_diffn_timesteps` (default `200`) sets the denoising steps and `num_trunk_recycles` (default `3`) trades accuracy for runtime. * **Confidence is reported as pLDDT, pTM, ipTM, PAE, and a confidence score.** `avg_plddt`, the primary metric, is on a 0 to 1 scale; ipTM is meaningful only for multi-chain complexes. Set `include_pae_matrix` to attach the full per-token PAE matrix.