Inference Tools

class atomworks.io.tools.inference.CCDComponent(ccd_code: str, chain_type: atomworks.enums.ChainType | str = 'non-polymer', is_polymer: bool = False, chain_id: str | None = None)

Bases: LigandComponent

ccd_code: str

chain_id: str | None = None

chain_type: ChainType | str = 'non-polymer'

is_polymer: bool = False

class atomworks.io.tools.inference.CIFOrPDBFileComponent(path: os.PathLike | _io.StringIO, msa_paths: dict[str, os.PathLike] | None = None, custom_parse_kwargs: dict[str, Any] | None = None, chain_id: str | None = None)

Bases: ChemicalComponent

chain_id: str | None = None

custom_parse_kwargs: dict[str, Any] | None = None

msa_paths: dict[str, PathLike] | None = None

path: PathLike | StringIO

class atomworks.io.tools.inference.ChemicalComponent

Bases: ABC

as_dict() → dict

static from_dict(args_dict: dict) → ChemicalComponent

class atomworks.io.tools.inference.DNA(seq: str | list[str], chain_type: atomworks.enums.ChainType = <ChainType.DNA: 3>, chain_id: str | None = None, include_bonds: bool = True, msa_path: os.PathLike | None = None)

Bases: SequenceComponent

chain_type: ChainType = 3

class atomworks.io.tools.inference.InChIComponent(inchi: str, chain_type: atomworks.enums.ChainType | str = 'non-polymer', is_polymer: bool = False, chain_id: str | None = None, res_name: str = 'UNL')

Bases: LigandComponent

chain_id: str | None = None

chain_type: ChainType | str = 'non-polymer'

inchi: str

is_polymer: bool = False

res_name: str = 'UNL'

class atomworks.io.tools.inference.LigandComponent

Bases: ChemicalComponent

class atomworks.io.tools.inference.Peptide(seq: str | list[str], chain_type: atomworks.enums.ChainType = <ChainType.POLYPEPTIDE_L: 6>, chain_id: str | None = None, include_bonds: bool = True, msa_path: os.PathLike | None = None, is_polymer: bool = False)

Bases: SequenceComponent

chain_type: ChainType = 6

is_polymer: bool = False

class atomworks.io.tools.inference.Polymer(seq: str | list[str], chain_type: atomworks.enums.ChainType | None = None, chain_id: str | None = None, include_bonds: bool = True, msa_path: os.PathLike | None = None, is_polymer: bool = True)

Bases: SequenceComponent

is_polymer: bool = True

class atomworks.io.tools.inference.Protein(seq: str | list[str], chain_type: atomworks.enums.ChainType = <ChainType.POLYPEPTIDE_L: 6>, chain_id: str | None = None, include_bonds: bool = True, msa_path: os.PathLike | None = None)

Bases: SequenceComponent

chain_type: ChainType = 6

class atomworks.io.tools.inference.RNA(seq: str | list[str], chain_type: atomworks.enums.ChainType = <ChainType.RNA: 7>, chain_id: str | None = None, include_bonds: bool = True, msa_path: os.PathLike | None = None)

Bases: SequenceComponent

chain_type: ChainType = 7

class atomworks.io.tools.inference.SDFComponent(path: os.PathLike | _io.StringIO, chain_type: atomworks.enums.ChainType | str = 'non-polymer', is_polymer: bool = False, chain_id: str | None = None, res_name: str = 'UNL')

Bases: LigandComponent

chain_id: str | None = None

chain_type: ChainType | str = 'non-polymer'

is_polymer: bool = False

path: PathLike | StringIO

res_name: str = 'UNL'

class atomworks.io.tools.inference.SequenceComponent(seq: str | list[str], chain_type: atomworks.enums.ChainType | None = None, chain_id: str | None = None, include_bonds: bool = True, msa_path: os.PathLike | None = None)

Bases: ChemicalComponent

static assert_valid_chain_type(seq: list[str], chain_type: ChainType, allow_other: bool = False) → bool

Asserts that all the CCD codes in the sequence are valid for the given chain type.

Parameters:

• seq (list[str]) – List of three-letter CCD codes.

• chain_type (ChainType) – The chain type to check against.

• allow_other (bool) – If True, allow non-CCD codes (e.g., custom NCAA) to be valid.

Ignore non-CCD codes (e.g., custom NCAA) which are presumed to be valid (and are mapped to “other”)

chain_id: str | None = None

chain_type: ChainType | None = None

static from_seq(seq: str | list[str], *, chain_type: ChainType | str = None, is_polymer: bool | None = None) → SequenceComponent

include_bonds: bool = True

static infer_chain_type(seq: str | list[str]) → ChainType

Infer chain type from sequence notation.

property is_polymer: bool

msa_path: PathLike | None = None

seq: str | list[str]

class atomworks.io.tools.inference.SmilesComponent(smiles: str, chain_type: atomworks.enums.ChainType | str = 'non-polymer', is_polymer: bool = False, chain_id: str | None = None, res_name: str = 'UNL')

Bases: LigandComponent

chain_id: str | None = None

chain_type: ChainType | str = 'non-polymer'

is_polymer: bool = False

res_name: str = 'UNL'

smiles: str

atomworks.io.tools.inference.assign_res_name_from_atom_array_hash(atom_array: AtomArray, hash_to_id: KeyToIntMapper) → AtomArray

Assigns a residue name to an array based on its hash.

The residue names will be assigned as L:{id} where id is a unique integer assigned to each hash.

Parameters:

• ligand_array (AtomArray) – The ligand array to assign a residue name to.

• ligand_hash_to_id (KeyToIntMapper) – A mapper from ligand hash to ligand ID.

atomworks.io.tools.inference.build_chain_atom_array(seq: list[str], chain_id: str, chain_type: ChainType, *, include_bonds: bool = False, ccd_mirror_path: PathLike = None) → AtomArray

Build an AtomArray from a sequence of CCD codes for a single chain.

Calls add_missing_atoms_for_chain() directly to build complete chains from CCD templates, then patches occupancy to 1.0 since every atom in the result is conceptually present (not unresolved).

Parameters:

• seq – Sequence of three-letter CCD codes.

• chain_id – Chain ID for all residues.

• chain_type – Chain type (determines is_polymer and other annotations).

• include_bonds – If True, infer bonds, remove leaving atoms, and fix charges/bond types via infer_bonds_from_residue_names(). Defaults to False.

• ccd_mirror_path – Path to local CCD mirror.

Returns:

AtomArray with residue IDs 1..len(seq), occupancy=1.0, and b_factor=nan.

atomworks.io.tools.inference.build_msa_paths_by_chain_id_from_component_list(components: list[ChemicalComponent]) → dict[str, PathLike]

Build a dictionary of MSA paths by chain ID from a list of ChemicalComponent objects.

The composed dictionary may be encoded as extra metadata in the CIF file, and ultimately loaded into chain_info through parse.

atomworks.io.tools.inference.ccd_code_to_annotated_atom_array(ccd_code: list[str], chain_id: str, *, chain_type: ChainType | str = None, is_polymer: bool | None = None, ccd_mirror_path: PathLike = None) → AtomArray

atomworks.io.tools.inference.components_to_atom_array(components: list[ChemicalComponent | dict], bonds: list[str] | None = None, return_components: bool = False) → AtomArray | list[ChemicalComponent]

Build an AtomArray from a list of ChemicalComponent objects and supporting details (bonds).

Parameters:

• components (list[ChemicalComponent | dict]) – List of ChemicalComponent objects or dictionaries that can be converted to ChemicalComponent objects using ChemicalComponent.from_dict().

• bonds (list[str]) – List of tuples of atom ids to be bonded. We will add them like spoof struct_conn entries, ensuring that we remove leaving groups as appropriate. Bonds tuples must be in the format (1-indexed!): ` (CHAIN_ID / RES_NAME / RES_ID / ATOM_NAME, CHAIN_ID / RES_NAME / RES_ID / ATOM_NAME) ` e.g., [(“A/THR/4/CG”, “D/L:1/0/O13”), (“A/CYS/5/SG”, “A/CYS/137/SG”)]

• return_components (bool) – If True, return the components list as well as the AtomArray. Useful for e.g., mapping components to generated chain IDs or inferred chain types.

NOTE: If manually specifying bonds, we recommend visualizing the bond graph with matplotlib to ensure that the bonds are correctly NOTE: The res_id numbering follows the RCSB convention (1-indexed) NOTE: Custom CCD entries can be registered using register_custom_ccd_entry()

to override standard CCD definitions before calling this function.

Returns:

The assembled AtomArray, used for visualization or inference.

Return type:

AtomArray

Raises:

• ValueError – If there are duplicate chain_ids across input Components

• ValueError – If there are duplicate chain_ids that correspond to non-identical molecular entities.

atomworks.io.tools.inference.inchi_to_annotated_atom_array(inchi: str, chain_id: str, *, chain_type: ChainType | str = 'non-polymer', is_polymer: bool = False, res_name: str = 'UNL') → AtomArray

Convert an InChI string to an annotated AtomArray.

Parameters:

• inchi – The InChI string representing the molecule.

• chain_id – The chain ID to assign.

• chain_type – The chain type. Defaults to “non-polymer”.

• is_polymer – Whether the component is a polymer. Defaults to False.

• res_name – The residue name. Defaults to UNKNOWN_LIGAND.

Returns:

AtomArray with standard ligand annotations.

atomworks.io.tools.inference.read_chai_fasta(fasta_path: Path) → list[ChemicalComponent]

atomworks.io.tools.inference.sdf_to_annotated_atom_array(path: StringIO | PathLike, chain_id: str, *, chain_type: ChainType | str = 'non-polymer', is_polymer: bool = False, res_name: str = 'UNL', backend: Literal['openbabel', 'rdkit'] = 'rdkit') → AtomArray

atomworks.io.tools.inference.sequence_to_annotated_atom_array(seq: list[str], chain_id: str, *, chain_type: ChainType | str = None, include_bonds: bool = True, ccd_mirror_path: PathLike = None, **kwargs) → AtomArray

atomworks.io.tools.inference.smiles_to_annotated_atom_array(smiles: str, chain_id: str, *, chain_type: ChainType | str = 'non-polymer', is_polymer: bool = False, backend: Literal['openbabel', 'rdkit'] = 'rdkit', res_name: str = 'UNL') → AtomArray

atomworks.io.tools.inference.standardize_component_keys(component_dict: dict) → dict

Standardize component dictionary keys for compatibility with AF3’s inference API.

Maps:

• “sequence” -> “seq”

• “id” -> “chain_id”