Added documentation examples for featurizers

  Otherwise, it is assumed to be a SMILES string.  RDKit is used to generate a

  3D conformation and to compute formal and partial charges.

  Examples

  --------

  >>> import deepchem as dc

  >>> smiles = ['CCC']

  >>> featurizer = dc.feat.AtomicConformationFeaturizer()

  >>> features = featurizer.featurize()

  >>> features[0].num_atoms

  11

  >>> sum(features[0].atomic_number == 6)

  3

  >>> sum(features[0].atmoic_number == 1)

  8

  >>> features[0].formal_charge

  array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)

  >>> features[0].partial_charge

  array([-0.06564544, -0.06564544, -0.05903836,  0.02302528,  0.02302528,

        0.02302528,  0.02302528,  0.02302528,  0.02302528,  0.0260888 ,

        0.0260888 ], dtype=float32)

  """

  def _featurize(self, datapoint: str) -> AtomicConformation:

  """

  try:

    return l.index(e)

  except:

  except ValueError:

    return len(l)

  Extended Connectivity Circular Fingerprints compute a bag-of-words style

  representation of a molecule by breaking it into local neighborhoods and

  hashing into a bit vector of the specified size. See [1]_ for more details.

  hashing into a bit vector of the specified size. It is used specifically

  for structure-activity modelling. See [1]_ for more details.

  References

  ----------

  Note

  ----

  This class requires RDKit to be installed.

  Examples

  --------

  >>> import deepchem as dc

  >>> from rdkit import Chem

  >>> smiles = 'C1=CC=CC=C1'

  >>> mol = Chem.MolFromSmiles(smiles)

  >>> # Example 1: (size = 2048, radius = 4)

  >>> featurizer = dc.feat.CircularFingerprint(size=2048, radius=4)

  >>> fingerprint = featurizer([mol])

  >>> fingerprint.shape

  (1, 2048)

  >>> # Example 2: (size = 2048, radius = 4, sparse = True, smiles = True)

  >>> featurizer = dc.feat.CircularFingerprint(size=2048, radius=8,

  ...                                          sparse=True, smiles=True)

  >>> featurizer([mol])

  array([{98513984: {'smiles': 'ccc', 'count': 6}, 2763854213: {'smiles':

    'ccccc', 'count': 6}, 3218693969: {'smiles': '', 'count': 6}, 3741631696:

    {'smiles': 'c1ccccc1', 'count': 1}}], dtype=object)

  """

  def __init__(self,

  Featurizer for MolGAN de-novo molecular generation [1]_.

  The default representation is in form of GraphMatrix object.

  It is wrapper for two matrices containing atom and bond type information.

  The class also provides reverse capabilities."""

  The class also provides reverse capabilities.

  Examples

  --------

  >>> import deepchem as dc

  >>> from rdkit import Chem

  >>> rdkit_mol, smiles_mol = Chem.MolFromSmiles('CCC'), 'C1=CC=CC=C1'

  >>> molecules = [rdkit_mol, smiles_mol]

  >>> featurizer = dc.feat.MolGanFeaturizer()

  >>> features = featurizer.featurize(molecules)

  >>> type(features[0])

  deepchem.feat.molecule_featurizers.molgan_featurizer.GraphMatrix

  >>> molecules = featurizer.defeaturize(features)

  >>> type(molecules[0])

  rdkit.Chem.rdchem.Mol

  """

  def __init__(

      self,

    References

    ---------

    .. [1] Nicola De Cao et al. "MolGAN: An implicit generative model

    for small molecular graphs`<https://arxiv.org/abs/1805.11973>`"

    for small molecular graphs`<https://arxiv.org/abs/1805.11973>`_"

    """

    self.max_atom_count = max_atom_count