`deeporigin.drug_discovery.chemistry`¶

Contains classes and functions for working with molecules, proteins, and related files.

Defines Ligand as Protein classes, as well as functions for reading/writing SDF files, SMILES / SDF Conversion, validating data, DataFrame integration, and preparing visualizations. These can be used together with the drug_discovery module for tasks such as docking.

Ligand: Represents a small molecule ligand, accepting either a file path (SDF) or a SMILES string. Provides show() method to display it.
Protein: Represents a protein, accepting a local file path (PDB) or a PDB ID. Provides show() method to display it.

Classes¶

Ligand `dataclass` ¶

Class to represent a ligand (typically backed by a SDF file)

Attributes¶

file `class-attribute` `instance-attribute` ¶

file: Optional[str | Path] = None

properties `class-attribute` `instance-attribute` ¶

properties: Optional[dict] = None

smiles_string `class-attribute` `instance-attribute` ¶

smiles_string: Optional[str] = None

Functions¶

from_csv `classmethod` ¶

from_csv(
    *,
    file: str | Path,
    smiles_column: str,
    properties_columns: list[str] = None
) -> list[Ligand]

create a list of ligands from a CSV file

Parameters:

Name	Type	Description	Default
`file`	`str \| Path`	Path to CSV file	required
`smiles_column`	`str`	Column name containing SMILES strings	required
`properties_columns`	`list[str]`	List of column names to extract as properties	`None`

Returns:

Type	Description
`list[Ligand]`	List of Ligand objects

from_smiles `classmethod` ¶

from_smiles(smiles: str) -> Ligand

create a ligand from a SMILES string

show ¶

show()

show a ligand in a Jupyter notebook using molstar

Protein `dataclass` ¶

Class to represent a protein (typically backed by a PDB file)

Attributes¶

file `class-attribute` `instance-attribute` ¶

file: Optional[str | Path] = None

name `class-attribute` `instance-attribute` ¶

name: Optional[str] = None

pdb_id `class-attribute` `instance-attribute` ¶

pdb_id: Optional[str] = None

Functions¶

show ¶

show()

visualize the protein in a Jupyter notebook using molstar

Functions¶

canonicalize_smiles ¶

canonicalize_smiles(smiles: str) -> str

Canonicalize a SMILES string.

Parameters:

Name	Type	Description	Default
`smiles`	`str`	SMILES string.	required

Returns:

Name	Type	Description
`str`	`str`	Canonicalized SMILES string.

count_molecules_in_sdf_file ¶

count_molecules_in_sdf_file(sdf_file: str | Path) -> int

Count the number of valid (sanitizable) molecules in an SDF file using RDKit, while suppressing RDKit's error logging for sanitization issues.

Parameters:

Name	Type	Description	Default
`sdf_file`	`str \| Path`	Path to the SDF file.	required

Returns:

Name	Type	Description
`int`	`int`	The number of molecules successfully read in the SDF file.

download_protein ¶

download_protein(pdb_id: str, save_dir: str = '.') -> str

Downloads a PDB structure by its PDB ID from RCSB and saves it to the specified directory.

Parameters:

Name	Type	Description	Default
`pdb_id`	`str`	PDB ID of the protein.	required
`save_dir`	`str`	Directory to save the downloaded PDB file.	`'.'`

Returns:

Name	Type	Description
`str`	`str`	Path to the downloaded PDB file.

Raises:

Type	Description
`Exception`	If the download fails.

filter_sdf_by_smiles ¶

filter_sdf_by_smiles(
    *,
    input_sdf_file: str | Path,
    output_sdf_file: str | Path,
    keep_only_smiles: list[str] | Series
)

Extracts the SMILES strings of all valid molecules from an SDF file using RDKit.

Parameters:

Name	Type	Description	Default
`input_sdf_file`	`str \| Path`	Path to the SDF file.	required
`output_sdf_file`	`str \| Path`	Path to the output SDF file.	required
`keep_only_smiles`	`list[str] \| Series`	List or Series of SMILES strings to keep.	required

get_properties_in_sdf_file ¶

get_properties_in_sdf_file(sdf_file: str | Path) -> list

Returns a list of all user-defined properties in an SDF file

Parameters:

Name	Type	Description	Default
`sdf_file`	`str \| Path`	Path to the SDF file.	required

Returns:

Name	Type	Description
`list`	`list`	A list of the names of all user-defined properties in the SDF file.

ligands_to_dataframe ¶

ligands_to_dataframe(ligands: list[Ligand])

convert a list of ligands to a pandas dataframe

merge_sdf_files ¶

merge_sdf_files(
    sdf_file_list: list[str],
    output_path: Optional[str] = None,
) -> str

Merge a list of SDF files into a single SDF file.

Parameters:

Name	Type	Description	Default
`sdf_file_list`	`list of str`	List of paths to SDF files.	required

Returns:

Name	Type	Description
`str`	`str`	Path to the merged SDF file.

read_molecules_in_sdf_file ¶

read_molecules_in_sdf_file(
    sdf_file: str | Path,
) -> list[dict]

Reads an SDF file containing one or more molecules, and for each molecule: - Extracts the SMILES string - Extracts all user-defined properties

Returns:

Type	Description
`list[dict]`	list[dict]: A list of dictionaries, where each dictionary has: - "smiles_string": str - "properties": dict

read_property_values ¶

read_property_values(sdf_file: str | Path, key: str)

Given a SDF file with more than 1 molecule, return the values of the properties for each molecule

Parameters:

Name	Type	Description	Default
`sdf_file`	`str \| Path`	Path to the SDF file.	required
`key`	`str`	The key of the property to read.	required

read_sdf_properties ¶

read_sdf_properties(sdf_file: str | Path) -> dict

Reads all user-defined properties from an SDF file (single molecule) and returns them as a dictionary.

Parameters:

Name	Type	Description	Default
`sdf_file`	`str \| Path`	Path to the SDF file.	required

sdf_to_smiles ¶

sdf_to_smiles(sdf_file: str | Path) -> list[str]

Extracts the SMILES strings of all valid molecules from an SDF file using RDKit.

Parameters:

Name	Type	Description	Default
`sdf_file`	`str \| Path`	Path to the SDF file.	required

Returns:

Type	Description
`list[str]`	list[str]: A list of SMILES strings for all valid molecules in the file.

show_ligands ¶

show_ligands(ligands: list[Ligand])

show ligands in the FEP object in a dataframe. This function visualizes the ligands using core-aligned 2D visualizations.

Parameters:

Name	Type	Description	Default
`ligands`	`list[Ligand]`	list of ligands	required

show_molecules_in_sdf_file ¶

show_molecules_in_sdf_file(sdf_file: str | Path)

show molecules in an SDF file in a Jupyter notebook using molstar

show_molecules_in_sdf_files ¶

show_molecules_in_sdf_files(sdf_files: list[str])

show molecules in an SDF file in a Jupyter notebook using molstar

smiles_list_to_base64_png_list ¶

smiles_list_to_base64_png_list(
    smiles_list: list[str],
    *,
    size: Tuple[int, int] = (300, 100),
    scale_factor: int = 2,
    reference_smiles: Optional[str] = None
) -> list[str]

Convert a list of SMILES strings to a list of base64-encoded PNG tags.

This aligns images so that they have consistent core orientation.

Parameters:

Name	Type	Description	Default
`smiles_list`	`list[str]`	List of SMILES strings.	required
`size`	`Tuple[int, int]`	(width, height) of the final rendered image in pixels (CSS downscaled).	`(300, 100)`
`scale_factor`	`int`	Factor to generate higher-resolution images internally.	`2`
`reference_smiles`	`Optional[str]`	If provided, all molecules will be oriented to match the 2D layout of this reference molecule.	`None`

smiles_to_base64_png ¶

smiles_to_base64_png(
    smiles: str, *, size=(300, 100), scale_factor: int = 2
) -> str

Convert a SMILES string to an inline base64 tag. Use this if you want to convert a single molecule into an image. If you want to convert a set of SMILES strings (corresponding to a set of related molecules) to images, use smiles_list_to_base64_png_list.

Parameters:

Name	Type	Description	Default
`smiles`	`str`	SMILES string.	required
`size`	`Tuple[int, int]`	(width, height) of the final rendered image in pixels (CSS downscaled).	`(300, 100)`
`scale_factor`	`int`	Factor to generate higher-resolution images internally.	`2`

smiles_to_sdf ¶

smiles_to_sdf(smiles: str, sdf_path: str) -> None

convert a SMILES string to a SDF file

Parameters:

Name	Type	Description	Default
`smiles`	`str`	SMILES string	required
`sdf_path`	`str`	Path to the SDF file	required

split_sdf_file ¶

split_sdf_file(
    *,
    input_sdf_path: str | Path,
    output_prefix: str = "ligand",
    output_dir: Optional[str | Path] = None,
    name_by_property: str = "_Name"
) -> list[Path]

Splits a multi-ligand SDF file into individual SDF files, optionally placing the output in a user-specified directory. Each output SDF is named using the molecule's name (if present) or a fallback prefix.

Parameters:

Name	Type	Description	Default
`input_sdf_path`	`str \| Path`	Path to the input SDF file containing multiple ligands.	required
`output_prefix`	`str`	Prefix for unnamed ligands. Defaults to "ligand".	`'ligand'`
`output_dir`	`Optional[str \| Path]`	Directory to write the output SDF files to. If None, output files are written to the same directory as input_sdf_path.	`None`

Returns:

Type	Description
`list[Path]`	list[Path]: A list of paths to the generated SDF files.

deeporigin.drug_discovery.chemistry¶

Classes¶

Ligand dataclass ¶

Attributes¶

file class-attribute instance-attribute ¶

properties class-attribute instance-attribute ¶

smiles_string class-attribute instance-attribute ¶

Functions¶

from_csv classmethod ¶

from_smiles classmethod ¶

show ¶

Protein dataclass ¶

Attributes¶

file class-attribute instance-attribute ¶

name class-attribute instance-attribute ¶

pdb_id class-attribute instance-attribute ¶

Functions¶

show ¶

Functions¶

canonicalize_smiles ¶

count_molecules_in_sdf_file ¶

download_protein ¶

filter_sdf_by_smiles ¶

get_properties_in_sdf_file ¶

ligands_to_dataframe ¶

merge_sdf_files ¶

read_molecules_in_sdf_file ¶

read_property_values ¶

read_sdf_properties ¶

sdf_to_smiles ¶

show_ligands ¶

show_molecules_in_sdf_file ¶

show_molecules_in_sdf_files ¶

smiles_list_to_base64_png_list ¶

smiles_to_base64_png ¶

smiles_to_sdf ¶

split_sdf_file ¶

`deeporigin.drug_discovery.chemistry`¶

Ligand `dataclass` ¶

file `class-attribute` `instance-attribute` ¶

properties `class-attribute` `instance-attribute` ¶

smiles_string `class-attribute` `instance-attribute` ¶

from_csv `classmethod` ¶

from_smiles `classmethod` ¶

Protein `dataclass` ¶

file `class-attribute` `instance-attribute` ¶

name `class-attribute` `instance-attribute` ¶

pdb_id `class-attribute` `instance-attribute` ¶