Merge branch 'master' of https://github.com/patrickhop/deepchem

# TODO: Get rid of this when there is a stable release of deepchem.

RUN git clone https://github.com/deepchem/deepchem.git && \

    cd deepchem && \

    git checkout tags/1.1.0 && \

    git checkout tags/1.2.0 && \

    sed -i -- 's/tensorflow$/tensorflow-gpu/g' scripts/install_deepchem_conda.sh && \

    bash scripts/install_deepchem_conda.sh root && \

    pip install tensorflow-gpu==1.0.1 && \

[![Coverage Status](https://coveralls.io/repos/github/deepchem/deepchem/badge.svg?branch=master)](https://coveralls.io/github/deepchem/deepchem?branch=master)

DeepChem aims to provide a high quality open-source toolchain that

democratizes the use of deep-learning in drug discovery, materials science, and quantum

chemistry. DeepChem is a package developed by the [Pande group](https://pande.stanford.edu/) at

Stanford and originally created by [Bharath Ramsundar](http://rbharath.github.io/).

democratizes the use of deep-learning in drug discovery, materials science, and quantum chemistry.

### Table of contents:

4. [Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity](https://arxiv.org/abs/1703.10603)

## About Us

DeepChem is a package from the [Pande group](https://pande.stanford.edu/) at Stanford with significant contributions from many academic and industrial collaborators. In particular, DeepChem is possible due to notable contributions from many people including Peter Eastman, Evan Feinberg, Joe Gomes, Karl Leswing, Vijay Pande, Aneesh Pappu, Bharath Ramsundar and Michael Wu (alphabetical ordering).  DeepChem was originally created by [Bharath Ramsundar](http://rbharath.github.io/) with encouragement and guidance from [Vijay Pande](https://pande.stanford.edu/).

DeepChem is possible due to notable contributions from many people including Peter Eastman, Evan Feinberg, Joe Gomes, Karl Leswing, Vijay Pande, Aneesh Pappu, Bharath Ramsundar and Michael Wu (alphabetical ordering).  DeepChem was originally created by [Bharath Ramsundar](http://rbharath.github.io/) with encouragement and guidance from [Vijay Pande](https://pande.stanford.edu/).

DeepChem started as a [Pande group](https://pande.stanford.edu/) project at Stanford, and is now developed by many academic and industrial collaborators. DeepChem actively encourages new academic and industrial groups to contribute!

## Corporate Supporters

DeepChem is supported by a number of corporate partners who use DeepChem to solve interesting problems.

from deepchem.feat.basic import RDKitDescriptors

from deepchem.feat.coulomb_matrices import CoulombMatrix

from deepchem.feat.coulomb_matrices import CoulombMatrixEig

from deepchem.feat.coulomb_matrices import BPSymmetryFunction

from deepchem.feat.rdkit_grid_featurizer import RdkitGridFeaturizer

from deepchem.feat.nnscore_utils import hydrogenate_and_compute_partial_charges

from deepchem.feat.binding_pocket_features import BindingPocketFeaturizer

from rdkit import Chem

from deepchem.feat import Featurizer

from deepchem.utils import pad_array

from deepchem.feat.atomic_coordinates import AtomicCoordinates

class BPSymmetryFunction(Featurizer):

  """

  Calculate Symmetry Function for each atom in the molecules

  Methods described in https://journals.aps.org/prl/pdf/10.1103/PhysRevLett.98.146401

  """

  def __init__(self, max_atoms):

    self.max_atoms = max_atoms

  def _featurize(self, mol):

    coordfeat = AtomicCoordinates()

    coordinates = coordfeat._featurize(mol)[0]

    atom_numbers = np.array([atom.GetAtomicNum() for atom in mol.GetAtoms()])

    atom_numbers = np.expand_dims(atom_numbers, axis=1)

    assert atom_numbers.shape[0] == coordinates.shape[0]

    n_atoms = atom_numbers.shape[0]

    features = np.concatenate([atom_numbers, coordinates], axis=1)

    return np.pad(features, ((0, self.max_atoms - n_atoms), (0, 0)), 'constant')

class CoulombMatrix(Featurizer):

from deepchem.models.sklearn_models import SklearnModel

from deepchem.models.xgboost_models import XGBoostModel

from deepchem.models.tf_new_models.multitask_classifier import MultitaskGraphClassifier

from deepchem.models.tf_new_models.multitask_regressor import MultitaskGraphRegressor

from deepchem.models.tf_new_models.multitask_regressor import MultitaskGraphRegressor, DTNNMultitaskGraphRegressor

from deepchem.models.tf_new_models.support_classifier import SupportGraphClassifier

from deepchem.models.multitask import SingletaskToMultitask

from deepchem.models.tensorflow_models.IRV import TensorflowMultiTaskIRVClassifier

from deepchem.models.tensorgraph.tensor_graph import TensorGraph

from deepchem.models.tensorgraph.models.graph_models import WeaveTensorGraph, DTNNTensorGraph, DAGTensorGraph, GraphConvTensorGraph

from deepchem.models.tensorgraph.models.symmetry_function_regression import BPSymmetryFunctionRegression, ANIRegression