SMARTSFilter

Complexity

Total Complexity

5

Size/Duplication

Total Lines	54
Duplicated Lines	0 %

Importance

Changes	1
Bugs	0	Features	1

3 Methods

Rating	Name	Duplication	Size	Complexity
A	__init__()	0	12	3
A	read_smarts()	0	5	2
A	func()	0	3	2

#! /usr/bin/env python
#
# Copyright (C) 2007-2009 Rich Lewis <rl403@cam.ac.uk>
# License: 3-clause BSD

"""
# skchem.filters.smarts

Module defines SMARTS filters.
"""

from rdkit import RDConfig

The import rdkit could not be resolved.

import pandas as pd

The import pandas could not be resolved.

import os
import pandas as pd

The import pandas could not be resolved.

from .base import Filter
from ..core import Mol

class SMARTSFilter(Filter):

    """ Filter a molecule based on smarts.

    Args:
        smarts (pd.Series):
            A series of SMARTS to use in the filter.
        agg (function):
            Option specifying the mode of the filter.

            - None : No filtering takes place
            - any: If any of the substructures are in molecule return True.
            - all: If all of the substructures are in molecule.

    Examples:

        >>> import skchem
        >>> m1 = skchem.Mol.from_smiles('CC')
        >>> m2 = skchem.Mol.from_smiles('c1ccccc1')
        >>> m3 = skchem.Mol.from_smiles('c1ccccc1-c2c(C=O)ccnc2')
        >>> ms = pd.Series({'ethane': m1, 'benzene': m2, 'big': m3})
        >>> f = skchem.filters.SMARTSFilter({'benzene': 'c1ccccc1', 'pyridine': 'c1ccccn1', 'acetyl': 'C=O'})

This line is too long as per the coding-style (109/100).

        >>> f.apply(ms)
                acetyl benzene pyridine
        benzene  False    True    False
        big       True    True     True
        ethane   False   False    False

        >>> f.filter(ms, agg=any)
        benzene                <Mol: c1ccccc1>
        big        <Mol: O=Cc1ccncc1-c1ccccc1>
        dtype: object

        >>> f.filter(ms, agg=all)
        big    <Mol: O=Cc1ccncc1-c1ccccc1>
        dtype: object
    """

    def __init__(self, smarts, **kwargs):

        def read_smarts(s):

The name s does not conform to the argument naming conventions ([a-z_][a-z0-9_]{2,30}$).

This function should have a docstring.

            if isinstance(s, str):
                return Mol.from_smarts(s, mergeHs=True)

The Class Mol does not seem to have a member named from_smarts.

            else:
                return s

        self.smarts = pd.Series(smarts).apply(read_smarts)

        self.index = self.smarts.index
        super(SMARTSFilter, self).__init__(self.func, **kwargs)

    def func(self, mol):

This method should have a docstring.

This method seems to be hidden by an attribute defined in skchem.filters.base on line 69.

        return self.smarts.apply(lambda smarts: smarts in mol)



class PAINSFilter(SMARTSFilter):

    """ Whether a molecule passes the Pan Assay INterference (PAINS) filters.

    These are supplied with RDKit, and were originally proposed by Baell et al.

    References:
        [The original paper](http://dx.doi.org/10.1021/jm901137j)

    Examples:

        Basic usage as a function on molecules:

        >>> import skchem
        >>> m1 = skchem.Mol.from_smiles('c1ccccc1', name='benzene')
        >>> no_pains = PAINSFilter()
        >>> no_pains(m1)
        True
        >>> m2 = skchem.Mol.from_smiles('Oc1c(O)cccc1', name='catechol')
        >>> no_pains(m2)
        False

        More useful in combination with pandas DataFrames:

        >>> import gzip
        >>> sdf = gzip.open(skchem.data.resource('ames_mutagenicity.sdf.gz'))
        >>> data = skchem.read_sdf(sdf)
        >>> no_pains.apply(data).value_counts()
        True     3855
        False     482
        dtype: int64

        >>> len(no_pains.filter(data))
        3855
    """

    def __init__(self):

        super(PAINSFilter, self).__init__(self._load_pains(), agg=any, neg=True)

    def _load_pains(cls):

Methods should have self as first argument.

        """ Load PAINS included in rdkit into a pandas dataframe and cache as class attribute. """

        if not hasattr(cls, '_pains'):
            path = os.path.join(RDConfig.RDDataDir, 'Pains', 'wehi_pains.csv')
            pains = pd.read_csv(path, names=['pains', 'names'])
            pains['names'] = pains.names.str.lstrip('<regId=').str.rstrip('>')
            pains = pains.set_index('names').pains.apply(Mol.from_smarts, mergeHs=True)

The Class Mol does not seem to have a member named from_smarts.

            cls._pains = pains
        return cls._pains