`deeporigin.drug_discovery.LigandSet`¶

A class representing a set of Ligand objects.

Attributes:

Name	Type	Description
`ligands`	`list[Ligand]`	A list of Ligand instances contained in the set.
`network`	`dict`	A dictionary containing the network of ligands estimated using Konnektor.

Attributes¶

ligands `class-attribute` `instance-attribute` ¶

ligands: list[Ligand] = field(default_factory=list)

network `class-attribute` `instance-attribute` ¶

network: dict = field(default_factory=dict)

Functions¶

add_hydrogens ¶

add_hydrogens() -> None

Add hydrogens to all ligands in the set.

admet_properties ¶

admet_properties(use_cache: bool = True)

Predict ADMET properties for all ligands in the set. This calls the admet_properties() method on each Ligand in the set. Returns a list of the results for each ligand. Shows a progress bar using tqdm.

compute_constraints ¶

compute_constraints(
    *, reference: Ligand, mcs_mol=None
) -> list[list[dict]]

Align a set of ligands to a reference ligand

compute_rmsd ¶

compute_rmsd()

compute pairwise rmsd between all ligands in the set

embed ¶

embed()

Minimize all ligands in the set using their 3D optimization routines. This calls the embed() method on each Ligand in the set.

filter_top_poses ¶

filter_top_poses(
    *, by_pose_score: bool = False
) -> LigandSet

Filter ligands to keep only the best pose for each unique molecule.

Groups ligands by their 'initial_smiles' property and retains only the one with: - Minimum binding energy (default), or - Maximum pose score (when by_pose_score=True)

Parameters:

Name	Type	Description	Default
`by_pose_score`	`bool`	If True, select by maximum pose score. If False (default), select by minimum binding energy.	`False`

Returns:

Name	Type	Description
`LigandSet`	`LigandSet`	A new LigandSet containing only the best pose for each unique molecule.

Raises:

Type	Description
`DeepOriginException`	If required properties are missing from ligands.

Example

Filter by binding energy (default)¶

filtered_ligands = ligand_set.filter_top_poses()

Filter by pose score¶

filtered_ligands = ligand_set.filter_top_poses(by_pose_score=True)

from_csv `classmethod` ¶

from_csv(
    file_path: str, smiles_column: str = "smiles"
) -> LigandSet

Create a LigandSet instance from a CSV file containing SMILES strings and additional properties.

Parameters:

Name	Type	Description	Default
`file_path`	`str`	The path to the CSV file.	required
`smiles_column`	`str`	The name of the column containing SMILES strings. Defaults to "smiles".	`'smiles'`

Returns:

Name	Type	Description
`LigandSet`	`LigandSet`	A LigandSet instance containing Ligand objects created from the CSV file.

Raises:

Type	Description
`FileNotFoundError`	If the file does not exist.
`DeepOriginException`	If the CSV does not contain the specified smiles column or if SMILES strings are invalid.

from_dir `classmethod` ¶

from_dir(directory: str) -> LigandSet

Create a LigandSet instance from a directory containing SDF files.

from_rdkit_mols `classmethod` ¶

from_rdkit_mols(mols: list[Mol])

Create a LigandSet from a list of RDKit molecules.

from_sdf `classmethod` ¶

from_sdf(
    file_path: str,
    *,
    sanitize: bool = True,
    remove_hydrogens: bool = False
) -> LigandSet

Create a LigandSet instance from an SDF file containing one or more molecules.

Parameters:

Name	Type	Description	Default
`file_path`	`str`	The path to the SDF file.	required
`sanitize`	`bool`	Whether to sanitize molecules. Defaults to True.	`True`
`remove_hydrogens`	`bool`	Whether to remove hydrogens. Defaults to False.	`False`

Returns:

Name	Type	Description
`LigandSet`	`LigandSet`	A LigandSet instance containing Ligand objects created from the SDF file.

Raises:

Type	Description
`FileNotFoundError`	If the file does not exist.
`DeepOriginException`	If the file cannot be parsed correctly.

from_sdf_files `classmethod` ¶

from_sdf_files(
    file_paths: list[str],
    *,
    sanitize: bool = True,
    remove_hydrogens: bool = False
) -> LigandSet

Create a LigandSet instance from multiple SDF files by concatenating them together.

Parameters:

Name	Type	Description	Default
`file_paths`	`list[str]`	A list of paths to SDF files.	required
`sanitize`	`bool`	Whether to sanitize molecules. Defaults to True.	`True`
`remove_hydrogens`	`bool`	Whether to remove hydrogens. Defaults to False.	`False`

Returns:

Name	Type	Description
`LigandSet`	`LigandSet`	A LigandSet instance containing Ligand objects from all SDF files.

Raises:

Type	Description
`FileNotFoundError`	If any of the files do not exist.
`DeepOriginException`	If any of the files cannot be parsed correctly.

from_smiles `classmethod` ¶

from_smiles(smiles: list[str] | set[str]) -> LigandSet

Create a LigandSet from a list of SMILES strings.

map_network ¶

map_network(
    *,
    use_cache: bool = True,
    operation: Literal[
        "mapping", "network", "full"
    ] = "network",
    network_type: Literal["star", "mst", "cyclic"] = "mst"
)

Map a network of ligands from an SDF file using the DeepOrigin API.

mcs ¶

mcs() -> str

Generates the Most Common Substructure (MCS) for ligands in a LigandSet

Returns:

Type	Description
`str`	smartsString (str) : SMARTS string representing the MCS

protonate ¶

protonate(
    *, ph: number = 7.4, filter_percentage: number = 1.0
)

Protonate the ligandSet. Only the most abundant species is retained for each ligand.

random_sample ¶

random_sample(n: int) -> LigandSet

Return a new LigandSet containing n randomly selected ligands.

Parameters:

Name	Type	Description	Default
`n`	`int`	Number of ligands to randomly sample	required

Returns:

Name	Type	Description
`LigandSet`	`LigandSet`	A new LigandSet with n randomly selected ligands

Raises:

Type	Description
`ValueError`	If n is greater than the total number of ligands

show ¶

show()

Visualize all ligands in this LigandSet in 3D

show_df ¶

show_df()

Show ligands in the set in a dataframe with 2D visualizations.

show_grid ¶

show_grid(
    mols_per_row: int = 3,
    sub_img_size: tuple[int, int] = (300, 300),
)

show all ligands in the LigandSet in a grid

show_network ¶

show_network()

Show the network of ligands in the set.

to_dataframe ¶

to_dataframe() -> DataFrame

Convert the LigandSet to a pandas DataFrame.

to_rdkit_mols ¶

to_rdkit_mols() -> list[Mol]

Convert all ligands in the set to RDKit molecules.

to_sdf ¶

to_sdf(output_path: Optional[str] = None) -> str

Write all ligands in the set to a single SDF file, preserving all properties from each Ligand's mol field.

Parameters:

Name	Type	Description	Default
`output_path`	`str`	The path to the output SDF file.	`None`

Returns:

Name	Type	Description
`str`	`str`	The path to the written SDF file.

to_smiles ¶

to_smiles() -> list[str]

Convert all ligands in the set to SMILES strings.

deeporigin.drug_discovery.LigandSet¶

Attributes¶

ligands class-attribute instance-attribute ¶

network class-attribute instance-attribute ¶

Functions¶

add_hydrogens ¶

admet_properties ¶

compute_constraints ¶

compute_rmsd ¶

embed ¶

filter_top_poses ¶

Filter by binding energy (default)¶

Filter by pose score¶

from_csv classmethod ¶

from_dir classmethod ¶

from_rdkit_mols classmethod ¶

from_sdf classmethod ¶

from_sdf_files classmethod ¶

from_smiles classmethod ¶

map_network ¶

mcs ¶

protonate ¶

random_sample ¶

show ¶

show_df ¶

show_grid ¶

show_network ¶

to_dataframe ¶

to_rdkit_mols ¶

to_sdf ¶

to_smiles ¶

`deeporigin.drug_discovery.LigandSet`¶

ligands `class-attribute` `instance-attribute` ¶

network `class-attribute` `instance-attribute` ¶

from_csv `classmethod` ¶

from_dir `classmethod` ¶

from_rdkit_mols `classmethod` ¶

from_sdf `classmethod` ¶

from_sdf_files `classmethod` ¶

from_smiles `classmethod` ¶