Work with ligands

This document describes how to work with ligands (molecules) and use them in Deep Origin tools.

The `Ligand` class¶

The Ligand class is the primary way to work with ligands in Deep Origin.

Constructing a ligand¶

From a file¶

A ligand can be constructed from a file:

from deeporigin.chemistry import Ligand
from deeporigin import drug_discovery as dd

ligand = Ligand(dd.EXAMPLE_DATA_DIR / "brd-2.sdf")

From a SMILES string¶

A ligand can also be constructed from a SMILES string:

from deeporigin.chemistry import Ligand
ligand = Ligand(smiles_string="[H]C1=C([H])C(C(=O)N(C([H])([H])[H])C([H])([H])[H])=C([H])C(C2=C([H])N(C([H])([H])[H])C(=O)C3=C2C([H])=C([H])N3[H])=C1[H]")

From a CSV file¶

You can create multiple ligands at once from a CSV file using the from_csv class method. This is useful when you have a dataset of molecules with their SMILES strings and associated properties. The from_csv method:

Requires a path to the CSV file and the name of the column containing SMILES strings
Optionally accepts a list of column names to extract as properties
Skips rows with empty or invalid SMILES
Returns a list of Ligand objects

This approach is ideal for processing large datasets of molecules where each row represents a different compound.

Basic Usage¶

For the simplest case, just specify the file path and which column contains the SMILES strings:

from deeporigin.chemistry import Ligand

# Basic usage - just extracting SMILES from a column
ligands = Ligand.from_csv(
    file="molecules.csv",
    smiles_column="SMILES"
)

Including Properties¶

You can also extract additional properties from other columns in the CSV file:

ligands = Ligand.from_csv(
    file="molecules.csv",
    smiles_column="SMILES",
    properties_columns=["Name", "MW", "LogP", "Activity"]
)

This will store all the specified column values as properties for each ligand, making it easy to keep track of important molecular characteristics alongside the structure information.

Visualizing a ligand¶

Browser support

These visualizations work best on Google Chrome. We are aware of issues on other browsers, especially Safari on macOS.

A ligand object can be visualized using show:

ligand.show()

If a ligand is backed by a SDF file, a 3D visualization will be shown, similar to:

A visualization such as this will be shown:

Jupyter notebook required

Visualizations such as these require this code to be run in a jupyter notebook. We recommend using these instructions to install Jupyter.

If a ligand is not backed by a SDF file, a 2D visualization will be shown: