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Chemistry

Chemistry and cheminformatics-oriented data cleaning functions.

maccs_keys_fingerprint(df, mols_column_name)

Convert a column of RDKIT mol objects into MACCS Keys Fingerprints.

Returns a new dataframe without any of the original data. This is intentional to leave the user with the data requested.

This method does not mutate the original DataFrame.

Functional usage example:

import pandas as pd import janitor.chemistry

df = pd.DataFrame({"smiles": ["O=C=O", "CCC(=O)O"]})

maccs = janitor.chemistry.maccs_keys_fingerprint( ... df=df.smiles2mol('smiles', 'mols'), ... mols_column_name='mols' ... )

len(maccs.columns) 167

Method chaining usage example:

import pandas as pd import janitor.chemistry

df = pd.DataFrame({"smiles": ["O=C=O", "CCC(=O)O"]})

maccs = ( ... df.smiles2mol('smiles', 'mols') ... .maccs_keys_fingerprint(mols_column_name='mols') ... )

len(maccs.columns) 167

If you wish to join the maccs keys fingerprints back into the original dataframe, this can be accomplished by doing a join, because the indices are preserved:

joined = df.join(maccs)

len(joined.columns) 169

Parameters:

Name Type Description Default
df DataFrame

A pandas DataFrame.

required
mols_column_name Hashable

The name of the column that has the RDKIT mol objects.

required

Returns:

Type Description
DataFrame

A new pandas DataFrame of MACCS keys fingerprints.

Source code in janitor/chemistry.py
@pf.register_dataframe_method
@deprecated_alias(mols_col="mols_column_name")
def maccs_keys_fingerprint(
    df: pd.DataFrame, mols_column_name: Hashable
) -> pd.DataFrame:
    """
    Convert a column of RDKIT mol objects into MACCS Keys Fingerprints.

    Returns a new dataframe without any of the original data.
    This is intentional to leave the user with the data requested.

    This method does not mutate the original DataFrame.

    Functional usage example:

    >>> import pandas as pd
    >>> import janitor.chemistry

    >>> df = pd.DataFrame({"smiles": ["O=C=O", "CCC(=O)O"]})

    >>> maccs = janitor.chemistry.maccs_keys_fingerprint(
    ...     df=df.smiles2mol('smiles', 'mols'),
    ...     mols_column_name='mols'
    ... )

    >>> len(maccs.columns)
    167

    Method chaining usage example:

    >>> import pandas as pd
    >>> import janitor.chemistry

    >>> df = pd.DataFrame({"smiles": ["O=C=O", "CCC(=O)O"]})

    >>> maccs = (
    ...     df.smiles2mol('smiles', 'mols')
    ...         .maccs_keys_fingerprint(mols_column_name='mols')
    ... )

    >>> len(maccs.columns)
    167

    If you wish to join the maccs keys fingerprints back into the
    original dataframe, this can be accomplished by doing a `join`,
    because the indices are preserved:

    >>> joined = df.join(maccs)

    >>> len(joined.columns)
    169

    :param df: A pandas DataFrame.
    :param mols_column_name: The name of the column that has the RDKIT mol
        objects.
    :returns: A new pandas DataFrame of MACCS keys fingerprints.
    """

    maccs = [GetMACCSKeysFingerprint(m) for m in df[mols_column_name]]

    np_maccs = []

    for macc in maccs:
        arr = np.zeros((1,))
        DataStructs.ConvertToNumpyArray(macc, arr)
        np_maccs.append(arr)
    np_maccs = np.vstack(np_maccs)
    fmaccs = pd.DataFrame(np_maccs)
    fmaccs.index = df.index
    return fmaccs

molecular_descriptors(df, mols_column_name)

Convert a column of RDKIT mol objects into a Pandas DataFrame of molecular descriptors.

Returns a new dataframe without any of the original data. This is intentional to leave the user only with the data requested.

This method does not mutate the original DataFrame.

The molecular descriptors are from the rdkit.Chem.rdMolDescriptors:

Chi0n, Chi0v, Chi1n, Chi1v, Chi2n, Chi2v, Chi3n, Chi3v,
Chi4n, Chi4v, ExactMolWt, FractionCSP3, HallKierAlpha, Kappa1,
Kappa2, Kappa3, LabuteASA, NumAliphaticCarbocycles,
NumAliphaticHeterocycles, NumAliphaticRings, NumAmideBonds,
NumAromaticCarbocycles, NumAromaticHeterocycles, NumAromaticRings,
NumAtomStereoCenters, NumBridgeheadAtoms, NumHBA, NumHBD,
NumHeteroatoms, NumHeterocycles, NumLipinskiHBA, NumLipinskiHBD,
NumRings, NumSaturatedCarbocycles, NumSaturatedHeterocycles,
NumSaturatedRings, NumSpiroAtoms, NumUnspecifiedAtomStereoCenters,
TPSA.

Functional usage example:

import pandas as pd import janitor.chemistry

df = pd.DataFrame({"smiles": ["O=C=O", "CCC(=O)O"]})

mol_desc = ( ... janitor.chemistry.molecular_descriptors( ... df=df.smiles2mol('smiles', 'mols'), ... mols_column_name='mols' ... ) ... )

mol_desc.TPSA 0 34.14 1 37.30 Name: TPSA, dtype: float64

Method chaining usage example:

import pandas as pd import janitor.chemistry

df = pd.DataFrame({"smiles": ["O=C=O", "CCC(=O)O"]})

mol_desc = ( ... df.smiles2mol('smiles', 'mols') ... .molecular_descriptors(mols_column_name='mols') ... )

mol_desc.TPSA 0 34.14 1 37.30 Name: TPSA, dtype: float64

If you wish to join the molecular descriptors back into the original dataframe, this can be accomplished by doing a join, because the indices are preserved:

joined = df.join(mol_desc) len(joined.columns) 41

Parameters:

Name Type Description Default
df DataFrame

A pandas DataFrame.

required
mols_column_name Hashable

The name of the column that has the RDKIT mol objects.

required

Returns:

Type Description
DataFrame

A new pandas DataFrame of molecular descriptors.

Source code in janitor/chemistry.py
@pf.register_dataframe_method
@deprecated_alias(mols_col="mols_column_name")
def molecular_descriptors(
    df: pd.DataFrame, mols_column_name: Hashable
) -> pd.DataFrame:
    """
    Convert a column of RDKIT mol objects into a Pandas DataFrame
    of molecular descriptors.

    Returns a new dataframe without any of the original data. This is
    intentional to leave the user only with the data requested.

    This method does not mutate the original DataFrame.

    The molecular descriptors are from the rdkit.Chem.rdMolDescriptors:

    ```
    Chi0n, Chi0v, Chi1n, Chi1v, Chi2n, Chi2v, Chi3n, Chi3v,
    Chi4n, Chi4v, ExactMolWt, FractionCSP3, HallKierAlpha, Kappa1,
    Kappa2, Kappa3, LabuteASA, NumAliphaticCarbocycles,
    NumAliphaticHeterocycles, NumAliphaticRings, NumAmideBonds,
    NumAromaticCarbocycles, NumAromaticHeterocycles, NumAromaticRings,
    NumAtomStereoCenters, NumBridgeheadAtoms, NumHBA, NumHBD,
    NumHeteroatoms, NumHeterocycles, NumLipinskiHBA, NumLipinskiHBD,
    NumRings, NumSaturatedCarbocycles, NumSaturatedHeterocycles,
    NumSaturatedRings, NumSpiroAtoms, NumUnspecifiedAtomStereoCenters,
    TPSA.
    ```

    Functional usage example:

    >>> import pandas as pd
    >>> import janitor.chemistry

    >>> df = pd.DataFrame({"smiles": ["O=C=O", "CCC(=O)O"]})

    >>> mol_desc = (
    ...     janitor.chemistry.molecular_descriptors(
    ...         df=df.smiles2mol('smiles', 'mols'),
    ...         mols_column_name='mols'
    ...     )
    ... )

    >>> mol_desc.TPSA
    0    34.14
    1    37.30
    Name: TPSA, dtype: float64

    Method chaining usage example:

    >>> import pandas as pd
    >>> import janitor.chemistry

    >>> df = pd.DataFrame({"smiles": ["O=C=O", "CCC(=O)O"]})

    >>> mol_desc = (
    ...     df.smiles2mol('smiles', 'mols')
    ...     .molecular_descriptors(mols_column_name='mols')
    ... )

    >>> mol_desc.TPSA
    0    34.14
    1    37.30
    Name: TPSA, dtype: float64

    If you wish to join the molecular descriptors back into the original
    dataframe, this can be accomplished by doing a `join`,
    because the indices are preserved:

    >>> joined = df.join(mol_desc)
    >>> len(joined.columns)
    41

    :param df: A pandas DataFrame.
    :param mols_column_name: The name of the column that has the RDKIT mol
        objects.
    :returns: A new pandas DataFrame of molecular descriptors.
    """
    descriptors = [
        CalcChi0n,
        CalcChi0v,
        CalcChi1n,
        CalcChi1v,
        CalcChi2n,
        CalcChi2v,
        CalcChi3n,
        CalcChi3v,
        CalcChi4n,
        CalcChi4v,
        CalcExactMolWt,
        CalcFractionCSP3,
        CalcHallKierAlpha,
        CalcKappa1,
        CalcKappa2,
        CalcKappa3,
        CalcLabuteASA,
        CalcNumAliphaticCarbocycles,
        CalcNumAliphaticHeterocycles,
        CalcNumAliphaticRings,
        CalcNumAmideBonds,
        CalcNumAromaticCarbocycles,
        CalcNumAromaticHeterocycles,
        CalcNumAromaticRings,
        CalcNumAtomStereoCenters,
        CalcNumBridgeheadAtoms,
        CalcNumHBA,
        CalcNumHBD,
        CalcNumHeteroatoms,
        CalcNumHeterocycles,
        CalcNumLipinskiHBA,
        CalcNumLipinskiHBD,
        CalcNumRings,
        CalcNumSaturatedCarbocycles,
        CalcNumSaturatedHeterocycles,
        CalcNumSaturatedRings,
        CalcNumSpiroAtoms,
        CalcNumUnspecifiedAtomStereoCenters,
        CalcTPSA,
    ]
    descriptors_mapping = {f.__name__.strip("Calc"): f for f in descriptors}

    feats = dict()
    for name, func in descriptors_mapping.items():
        feats[name] = [func(m) for m in df[mols_column_name]]
    return pd.DataFrame(feats)

morgan_fingerprint(df, mols_column_name, radius=3, nbits=2048, kind='counts')

Convert a column of RDKIT Mol objects into Morgan Fingerprints.

Returns a new dataframe without any of the original data. This is intentional, as Morgan fingerprints are usually high-dimensional features.

This method does not mutate the original DataFrame.

Functional usage example:

import pandas as pd import janitor.chemistry

df = pd.DataFrame({"smiles": ["O=C=O", "CCC(=O)O"]})

For "counts" kind

morgans = janitor.chemistry.morgan_fingerprint( ... df=df.smiles2mol('smiles', 'mols'), ... mols_column_name='mols', ... radius=3, # Defaults to 3 ... nbits=2048, # Defaults to 2048 ... kind='counts' # Defaults to "counts" ... )

set(morgans.iloc[0]) {0.0, 1.0, 2.0}

For "bits" kind

morgans = janitor.chemistry.morgan_fingerprint( ... df=df.smiles2mol('smiles', 'mols'), ... mols_column_name='mols', ... radius=3, # Defaults to 3 ... nbits=2048, # Defaults to 2048 ... kind='bits' # Defaults to "counts" ... )

set(morgans.iloc[0]) {0.0, 1.0}

Method chaining usage example:

import pandas as pd import janitor.chemistry

df = pd.DataFrame({"smiles": ["O=C=O", "CCC(=O)O"]})

For "counts" kind

morgans = ( ... df.smiles2mol('smiles', 'mols') ... .morgan_fingerprint( ... mols_column_name='mols', ... radius=3, # Defaults to 3 ... nbits=2048, # Defaults to 2048 ... kind='counts' # Defaults to "counts" ... ) ... )

set(morgans.iloc[0]) {0.0, 1.0, 2.0}

For "bits" kind

morgans = ( ... df ... .smiles2mol('smiles', 'mols') ... .morgan_fingerprint( ... mols_column_name='mols', ... radius=3, # Defaults to 3 ... nbits=2048, # Defaults to 2048 ... kind='bits' # Defaults to "counts" ... ) ... )

set(morgans.iloc[0]) {0.0, 1.0}

If you wish to join the morgan fingerprints back into the original dataframe, this can be accomplished by doing a join, because the indices are preserved:

joined = df.join(morgans) len(joined.columns) 2050

Parameters:

Name Type Description Default
df DataFrame

A pandas DataFrame.

required
mols_column_name str

The name of the column that has the RDKIT mol objects

required
radius int

Radius of Morgan fingerprints. Defaults to 3.

3
nbits int

The length of the fingerprints. Defaults to 2048.

2048
kind str

Whether to return counts or bits. Defaults to counts.

'counts'

Returns:

Type Description
DataFrame

A new pandas DataFrame of Morgan fingerprints.

Exceptions:

Type Description
ValueError

if kind is not one of "counts" or `"bits"``.

Source code in janitor/chemistry.py
@pf.register_dataframe_method
@deprecated_alias(mols_col="mols_column_name")
def morgan_fingerprint(
    df: pd.DataFrame,
    mols_column_name: str,
    radius: int = 3,
    nbits: int = 2048,
    kind: str = "counts",
) -> pd.DataFrame:
    """
    Convert a column of RDKIT Mol objects into Morgan Fingerprints.

    Returns a new dataframe without any of the original data. This is
    intentional, as Morgan fingerprints are usually high-dimensional
    features.

    This method does not mutate the original DataFrame.

    Functional usage example:

    >>> import pandas as pd
    >>> import janitor.chemistry

    >>> df = pd.DataFrame({"smiles": ["O=C=O", "CCC(=O)O"]})

    # For "counts" kind
    >>> morgans = janitor.chemistry.morgan_fingerprint(
    ...     df=df.smiles2mol('smiles', 'mols'),
    ...     mols_column_name='mols',
    ...     radius=3,      # Defaults to 3
    ...     nbits=2048,    # Defaults to 2048
    ...     kind='counts'  # Defaults to "counts"
    ... )

    >>> set(morgans.iloc[0])
    {0.0, 1.0, 2.0}

    # For "bits" kind
    >>> morgans = janitor.chemistry.morgan_fingerprint(
    ...     df=df.smiles2mol('smiles', 'mols'),
    ...     mols_column_name='mols',
    ...     radius=3,      # Defaults to 3
    ...     nbits=2048,    # Defaults to 2048
    ...     kind='bits'    # Defaults to "counts"
    ...  )

    >>> set(morgans.iloc[0])
    {0.0, 1.0}

    Method chaining usage example:

    >>> import pandas as pd
    >>> import janitor.chemistry

    >>> df = pd.DataFrame({"smiles": ["O=C=O", "CCC(=O)O"]})

    # For "counts" kind
    >>> morgans = (
    ...     df.smiles2mol('smiles', 'mols')
    ...     .morgan_fingerprint(
    ...         mols_column_name='mols',
    ...         radius=3,      # Defaults to 3
    ...         nbits=2048,    # Defaults to 2048
    ...         kind='counts'  # Defaults to "counts"
    ...     )
    ... )

    >>> set(morgans.iloc[0])
    {0.0, 1.0, 2.0}

    # For "bits" kind
    >>> morgans = (
    ...     df
    ...     .smiles2mol('smiles', 'mols')
    ...     .morgan_fingerprint(
    ...         mols_column_name='mols',
    ...         radius=3,    # Defaults to 3
    ...         nbits=2048,  # Defaults to 2048
    ...         kind='bits'  # Defaults to "counts"
    ...     )
    ... )

    >>> set(morgans.iloc[0])
    {0.0, 1.0}

    If you wish to join the morgan fingerprints back into the original
    dataframe, this can be accomplished by doing a `join`,
    because the indices are preserved:

    >>> joined = df.join(morgans)
    >>> len(joined.columns)
    2050

    :param df: A pandas DataFrame.
    :param mols_column_name: The name of the column that has the RDKIT
        mol objects
    :param radius: Radius of Morgan fingerprints. Defaults to 3.
    :param nbits: The length of the fingerprints. Defaults to 2048.
    :param kind: Whether to return counts or bits. Defaults to counts.
    :returns: A new pandas DataFrame of Morgan fingerprints.
    :raises ValueError: if `kind` is not one of
        `"counts"` or `"bits"``.
    """
    acceptable_kinds = ["counts", "bits"]
    if kind not in acceptable_kinds:
        raise ValueError(f"`kind` must be one of {acceptable_kinds}")

    if kind == "bits":
        fps = [
            GetMorganFingerprintAsBitVect(m, radius, nbits, useChirality=True)
            for m in df[mols_column_name]
        ]
    elif kind == "counts":
        fps = [
            GetHashedMorganFingerprint(m, radius, nbits, useChirality=True)
            for m in df[mols_column_name]
        ]

    np_fps = []
    for fp in fps:
        arr = np.zeros((1,))
        DataStructs.ConvertToNumpyArray(fp, arr)
        np_fps.append(arr)
    np_fps = np.vstack(np_fps)
    fpdf = pd.DataFrame(np_fps)
    fpdf.index = df.index
    return fpdf

smiles2mol(df, smiles_column_name, mols_column_name, drop_nulls=True, progressbar=None)

Convert a column of SMILES strings into RDKit Mol objects.

Automatically drops invalid SMILES, as determined by RDKIT.

This method mutates the original DataFrame.

Functional usage example:

import pandas as pd import janitor.chemistry

df = pd.DataFrame({"smiles": ["O=C=O", "CCC(=O)O"]})

df = janitor.chemistry.smiles2mol( ... df=df, ... smiles_column_name='smiles', ... mols_column_name='mols' ... )

df.mols[0].GetNumAtoms(), df.mols[0].GetNumBonds() (3, 2) df.mols[1].GetNumAtoms(), df.mols[1].GetNumBonds() (5, 4)

Method chaining usage example:

import pandas as pd import janitor.chemistry

df = df.smiles2mol( ... smiles_column_name='smiles', ... mols_column_name='rdkmol' ... )

df.rdkmol[0].GetNumAtoms(), df.rdkmol[0].GetNumBonds() (3, 2)

A progressbar can be optionally used.

  • Pass in "notebook" to show a tqdm notebook progressbar. (ipywidgets must be enabled with your Jupyter installation.)
  • Pass in "terminal" to show a tqdm progressbar. Better suited for use with scripts.
  • "none" is the default value - progress bar will be not be shown.

Parameters:

Name Type Description Default
df DataFrame

pandas DataFrame.

required
smiles_column_name Hashable

Name of column that holds the SMILES strings.

required
mols_column_name Hashable

Name to be given to the new mols column.

required
drop_nulls bool

Whether to drop rows whose mols failed to be constructed.

True
progressbar Optional[str]

Whether to show a progressbar or not.

None

Returns:

Type Description
DataFrame

A pandas DataFrame with new RDKIT Mol objects column.

Exceptions:

Type Description
ValueError

if progressbar is not one of "notebook"``,"terminal", or `None.

Source code in janitor/chemistry.py
@pf.register_dataframe_method
@deprecated_alias(smiles_col="smiles_column_name", mols_col="mols_column_name")
def smiles2mol(
    df: pd.DataFrame,
    smiles_column_name: Hashable,
    mols_column_name: Hashable,
    drop_nulls: bool = True,
    progressbar: Optional[str] = None,
) -> pd.DataFrame:
    """
    Convert a column of SMILES strings into RDKit Mol objects.

    Automatically drops invalid SMILES, as determined by RDKIT.

    This method mutates the original DataFrame.

    Functional usage example:

    >>> import pandas as pd
    >>> import janitor.chemistry

    >>> df = pd.DataFrame({"smiles": ["O=C=O", "CCC(=O)O"]})

    >>> df = janitor.chemistry.smiles2mol(
    ...    df=df,
    ...    smiles_column_name='smiles',
    ...    mols_column_name='mols'
    ... )

    >>> df.mols[0].GetNumAtoms(), df.mols[0].GetNumBonds()
    (3, 2)
    >>> df.mols[1].GetNumAtoms(), df.mols[1].GetNumBonds()
    (5, 4)

    Method chaining usage example:

    >>> import pandas as pd
    >>> import janitor.chemistry

    >>> df = df.smiles2mol(
    ...     smiles_column_name='smiles',
    ...     mols_column_name='rdkmol'
    ... )

    >>> df.rdkmol[0].GetNumAtoms(), df.rdkmol[0].GetNumBonds()
    (3, 2)

    A progressbar can be optionally used.

    - Pass in "notebook" to show a `tqdm` notebook progressbar.
      (`ipywidgets` must be enabled with your Jupyter installation.)
    - Pass in "terminal" to show a `tqdm` progressbar. Better suited for use
      with scripts.
    - "none" is the default value - progress bar will be not be shown.

    :param df: pandas DataFrame.
    :param smiles_column_name: Name of column that holds the SMILES strings.
    :param mols_column_name: Name to be given to the new mols column.
    :param drop_nulls: Whether to drop rows whose mols failed to be
        constructed.
    :param progressbar: Whether to show a progressbar or not.
    :returns: A pandas DataFrame with new RDKIT Mol objects column.
    :raises ValueError: if `progressbar` is not one of
        `"notebook"``, `"terminal"``, or `None``.
    """
    valid_progress = ["notebook", "terminal", None]
    if progressbar not in valid_progress:
        raise ValueError(f"progressbar kwarg must be one of {valid_progress}")

    if progressbar is None:
        df[mols_column_name] = df[smiles_column_name].apply(
            lambda x: Chem.MolFromSmiles(x)
        )
    else:
        if progressbar == "notebook":
            tqdmn().pandas(desc="mols")
        elif progressbar == "terminal":
            tqdm.pandas(desc="mols")
        df[mols_column_name] = df[smiles_column_name].progress_apply(
            lambda x: Chem.MolFromSmiles(x)
        )

    if drop_nulls:
        df = df.dropna(subset=[mols_column_name])
    df = df.reset_index(drop=True)
    return df