Loading deepchem/feat/graph_features.py +152 −0 Original line number Diff line number Diff line Loading @@ -4,8 +4,18 @@ from __future__ import unicode_literals import numpy as np from rdkit import Chem import deepchem as dc from deepchem.feat import Featurizer from deepchem.feat.atomic_coordinates import ComplexNeighborListFragmentAtomicCoordinates from deepchem.feat.mol_graphs import ConvMol, WeaveMol from deepchem.data import DiskDataset import multiprocessing import logging def _featurize_complex(featurizer, mol_pdb_file, protein_pdb_file, log_message): logging.info(log_message) return featurizer._featurize_complex(mol_pdb_file, protein_pdb_file) def one_of_k_encoding(x, allowable_set): Loading Loading @@ -424,3 +434,145 @@ class WeaveFeaturizer(Featurizer): graph_distance=self.graph_distance) return WeaveMol(nodes, pairs) class AtomicConvFeaturizer(ComplexNeighborListFragmentAtomicCoordinates): """This class computes the Atomic Convolution features""" # TODO (VIGS25): Complete the description name = ['atomic_conv'] def __init__(self, labels, neighbor_cutoff, frag1_num_atoms=70, frag2_num_atoms=634, complex_num_atoms=701, max_num_neighbors=12, batch_size=24, atom_types=[ 6, 7., 8., 9., 11., 12., 15., 16., 17., 20., 25., 30., 35., 53., -1. ], radial=[[ 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0, 11.5, 12.0 ], [0.0, 4.0, 8.0], [0.4]], layer_sizes=[32, 32, 16], strip_hydrogens=True, learning_rate=0.001, epochs=10): """ Parameters labels: numpy.ndarray Labels which we want to predict using the model neighbor_cutoff: int TODO (VIGS25): Add description frag1_num_atoms: int Number of atoms in first fragment frag2_num_atoms: int Number of atoms in second fragment complex_num_atoms: int TODO (VIGS25) : Add description max_num_neighbors: int Maximum number of neighbors possible for an atom batch_size: int Batch size used for training and evaluation atom_types: list List of atoms recognized by model. Atoms are indicated by their nuclear numbers. radial: list TODO (VIGS25): Add description layer_sizes: list List of layer sizes for the AtomicConvolutional Network strip_hydrogens: bool Whether to remove hydrogens while computing neighbor features learning_rate: float Learning rate for training the model epochs: int Number of epochs to train the model for """ self.atomic_conv_model = dc.models.tensorgraph.models.atomic_conv.AtomicConvModel( frag1_num_atoms=frag1_num_atoms, frag2_num_atoms=frag2_num_atoms, complex_num_atoms=complex_num_atoms, max_num_neighbors=max_num_neighbors, batch_size=batch_size, atom_types=atom_types, radial=radial, layer_sizes=layer_sizes, learning_rate=learning_rate) super(AtomicConvFeaturizer, self).__init__( frag1_num_atoms=frag1_num_atoms, frag2_num_atoms=frag2_num_atoms, complex_num_atoms=complex_num_atoms, max_num_neighbors=max_num_neighbors, neighbor_cutoff=neighbor_cutoff, strip_hydrogens=strip_hydrogens) self.epochs = epochs self.labels = labels def featurize_complexes(self, mol_files, protein_files): pool = multiprocessing.Pool() results = [] for i, (mol_file, protein_pdb) in enumerate(zip(mol_files, protein_files)): log_message = "Featurizing %d / %d" % (i, len(mol_files)) results.append( pool.apply_async(_featurize_complex, (self, mol_file, protein_pdb, log_message))) pool.close() features = [] failures = [] for ind, result in enumerate(results): new_features = result.get() # Handle loading failures which return None if new_features is not None: features.append(new_features) else: failures.append(ind) features = np.asarray(features) labels = np.delete(self.labels, failures) dataset = DiskDataset.from_numpy(features, labels) # Fit atomic conv model self.atomic_conv_model.fit(dataset, nb_epoch=self.epochs) # Add the Atomic Convolution layers to fetches layers_to_fetch = list() for layer in self.atomic_conv_model.layers.values(): if isinstance(layer, dc.models.tensorgraph.models.atomic_conv.AtomicConvolution): layers_to_fetch.append(layer) # Extract the atomic convolution features atomic_conv_features = list() feed_dict_generator = self.atomic_conv_model.default_generator( dataset=dataset, epochs=1) for feed_dict in self.atomic_conv_model._create_feed_dicts( feed_dict_generator, training=False): frag1_conv, frag2_conv, complex_conv = self.atomic_conv_model._run_graph( outputs=layers_to_fetch, feed_dict=feed_dict, training=False) concatenated = np.concatenate( [frag1_conv, frag2_conv, complex_conv], axis=1) atomic_conv_features.append(concatenated) batch_size = self.atomic_conv_model.batch_size if len(features) % batch_size != 0: num_batches = (len(features) // batch_size) + 1 num_to_skip = num_batches * batch_size - len(features) else: num_to_skip = 0 atomic_conv_features = np.asarray(atomic_conv_features) atomic_conv_features = atomic_conv_features[-num_to_skip:] atomic_conv_features = np.squeeze(atomic_conv_features) return atomic_conv_features, failures deepchem/feat/tests/test_graph_features.py +34 −4 Original line number Diff line number Diff line Loading @@ -10,11 +10,8 @@ __license__ = "MIT" import unittest import os import sys import numpy as np from deepchem.feat.mol_graphs import ConvMol from deepchem.feat.mol_graphs import MultiConvMol from deepchem.feat.graph_features import ConvMolFeaturizer from deepchem.feat.graph_features import ConvMolFeaturizer, AtomicConvFeaturizer class TestConvMolFeaturizer(unittest.TestCase): Loading Loading @@ -95,3 +92,36 @@ class TestConvMolFeaturizer(unittest.TestCase): assert np.array_equal(deg_adj_lists[4], np.zeros([0, 4], dtype=np.int32)) assert np.array_equal(deg_adj_lists[5], np.zeros([0, 5], dtype=np.int32)) assert np.array_equal(deg_adj_lists[6], np.zeros([0, 6], dtype=np.int32)) class TestAtomicConvFeaturizer(unittest.TestCase): def test_feature_generation(self): """Test if featurization works using AtomicConvFeaturizer.""" dir_path = os.path.dirname(os.path.realpath(__file__)) ligand_file = os.path.join(dir_path, "data/3zso_ligand_hyd.pdb") protein_file = os.path.join(dir_path, "data/3zso_protein.pdb") # Pulled from PDB files. For larger datasets with more PDBs, would use # max num atoms instead of exact. frag1_num_atoms = 44 # for ligand atoms frag2_num_atoms = 2336 # for protein atoms complex_num_atoms = 2380 # in total max_num_neighbors = 4 # Cutoff in angstroms neighbor_cutoff = 4 labels = np.array([0, 0]) featurizer = AtomicConvFeaturizer( labels=labels, batch_size=1, epochs=1, frag1_num_atoms=frag1_num_atoms, frag2_num_atoms=frag2_num_atoms, complex_num_atoms=complex_num_atoms, max_num_neighbors=max_num_neighbors, neighbor_cutoff=neighbor_cutoff) features, _ = featurizer.featurize_complexes([ligand_file, ligand_file], [protein_file, protein_file]) deepchem/molnet/load_function/pdbbind_datasets.py +18 −0 Original line number Diff line number Diff line Loading @@ -18,6 +18,7 @@ import logging import tarfile from deepchem.feat import rdkit_grid_featurizer as rgf from deepchem.feat.atomic_coordinates import ComplexNeighborListFragmentAtomicCoordinates from deepchem.feat.graph_features import AtomicConvFeaturizer logger = logging.getLogger(__name__) Loading Loading @@ -233,6 +234,22 @@ def load_pdbbind(featurizer="grid", split="random", subset="core", reload=True): frag1_num_atoms, frag2_num_atoms, complex_num_atoms, max_num_neighbors, neighbor_cutoff) elif featurizer == "atomic_conv": frag1_num_atoms = 70 # for ligand atoms frag2_num_atoms = 24000 # for protein atoms complex_num_atoms = 24070 # in total max_num_neighbors = 4 # Cutoff in angstroms neighbor_cutoff = 4 featurizer = AtomicConvFeaturizer( labels=labels, frag1_num_atoms=frag1_num_atoms, frag2_num_atoms=frag2_num_atoms, complex_num_atoms=complex_num_atoms, neighbor_cutoff=neighbor_cutoff, max_num_neighbors=max_num_neighbors, batch_size=64) else: raise ValueError("Featurizer not supported") print("Featurizing Complexes") Loading @@ -241,6 +258,7 @@ def load_pdbbind(featurizer="grid", split="random", subset="core", reload=True): # Delete labels for failing elements labels = np.delete(labels, failures) dataset = deepchem.data.DiskDataset.from_numpy(features, labels) print('Featurization complete.') # No transformations of data transformers = [] if split == None: Loading Loading
deepchem/feat/graph_features.py +152 −0 Original line number Diff line number Diff line Loading @@ -4,8 +4,18 @@ from __future__ import unicode_literals import numpy as np from rdkit import Chem import deepchem as dc from deepchem.feat import Featurizer from deepchem.feat.atomic_coordinates import ComplexNeighborListFragmentAtomicCoordinates from deepchem.feat.mol_graphs import ConvMol, WeaveMol from deepchem.data import DiskDataset import multiprocessing import logging def _featurize_complex(featurizer, mol_pdb_file, protein_pdb_file, log_message): logging.info(log_message) return featurizer._featurize_complex(mol_pdb_file, protein_pdb_file) def one_of_k_encoding(x, allowable_set): Loading Loading @@ -424,3 +434,145 @@ class WeaveFeaturizer(Featurizer): graph_distance=self.graph_distance) return WeaveMol(nodes, pairs) class AtomicConvFeaturizer(ComplexNeighborListFragmentAtomicCoordinates): """This class computes the Atomic Convolution features""" # TODO (VIGS25): Complete the description name = ['atomic_conv'] def __init__(self, labels, neighbor_cutoff, frag1_num_atoms=70, frag2_num_atoms=634, complex_num_atoms=701, max_num_neighbors=12, batch_size=24, atom_types=[ 6, 7., 8., 9., 11., 12., 15., 16., 17., 20., 25., 30., 35., 53., -1. ], radial=[[ 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0, 11.5, 12.0 ], [0.0, 4.0, 8.0], [0.4]], layer_sizes=[32, 32, 16], strip_hydrogens=True, learning_rate=0.001, epochs=10): """ Parameters labels: numpy.ndarray Labels which we want to predict using the model neighbor_cutoff: int TODO (VIGS25): Add description frag1_num_atoms: int Number of atoms in first fragment frag2_num_atoms: int Number of atoms in second fragment complex_num_atoms: int TODO (VIGS25) : Add description max_num_neighbors: int Maximum number of neighbors possible for an atom batch_size: int Batch size used for training and evaluation atom_types: list List of atoms recognized by model. Atoms are indicated by their nuclear numbers. radial: list TODO (VIGS25): Add description layer_sizes: list List of layer sizes for the AtomicConvolutional Network strip_hydrogens: bool Whether to remove hydrogens while computing neighbor features learning_rate: float Learning rate for training the model epochs: int Number of epochs to train the model for """ self.atomic_conv_model = dc.models.tensorgraph.models.atomic_conv.AtomicConvModel( frag1_num_atoms=frag1_num_atoms, frag2_num_atoms=frag2_num_atoms, complex_num_atoms=complex_num_atoms, max_num_neighbors=max_num_neighbors, batch_size=batch_size, atom_types=atom_types, radial=radial, layer_sizes=layer_sizes, learning_rate=learning_rate) super(AtomicConvFeaturizer, self).__init__( frag1_num_atoms=frag1_num_atoms, frag2_num_atoms=frag2_num_atoms, complex_num_atoms=complex_num_atoms, max_num_neighbors=max_num_neighbors, neighbor_cutoff=neighbor_cutoff, strip_hydrogens=strip_hydrogens) self.epochs = epochs self.labels = labels def featurize_complexes(self, mol_files, protein_files): pool = multiprocessing.Pool() results = [] for i, (mol_file, protein_pdb) in enumerate(zip(mol_files, protein_files)): log_message = "Featurizing %d / %d" % (i, len(mol_files)) results.append( pool.apply_async(_featurize_complex, (self, mol_file, protein_pdb, log_message))) pool.close() features = [] failures = [] for ind, result in enumerate(results): new_features = result.get() # Handle loading failures which return None if new_features is not None: features.append(new_features) else: failures.append(ind) features = np.asarray(features) labels = np.delete(self.labels, failures) dataset = DiskDataset.from_numpy(features, labels) # Fit atomic conv model self.atomic_conv_model.fit(dataset, nb_epoch=self.epochs) # Add the Atomic Convolution layers to fetches layers_to_fetch = list() for layer in self.atomic_conv_model.layers.values(): if isinstance(layer, dc.models.tensorgraph.models.atomic_conv.AtomicConvolution): layers_to_fetch.append(layer) # Extract the atomic convolution features atomic_conv_features = list() feed_dict_generator = self.atomic_conv_model.default_generator( dataset=dataset, epochs=1) for feed_dict in self.atomic_conv_model._create_feed_dicts( feed_dict_generator, training=False): frag1_conv, frag2_conv, complex_conv = self.atomic_conv_model._run_graph( outputs=layers_to_fetch, feed_dict=feed_dict, training=False) concatenated = np.concatenate( [frag1_conv, frag2_conv, complex_conv], axis=1) atomic_conv_features.append(concatenated) batch_size = self.atomic_conv_model.batch_size if len(features) % batch_size != 0: num_batches = (len(features) // batch_size) + 1 num_to_skip = num_batches * batch_size - len(features) else: num_to_skip = 0 atomic_conv_features = np.asarray(atomic_conv_features) atomic_conv_features = atomic_conv_features[-num_to_skip:] atomic_conv_features = np.squeeze(atomic_conv_features) return atomic_conv_features, failures
deepchem/feat/tests/test_graph_features.py +34 −4 Original line number Diff line number Diff line Loading @@ -10,11 +10,8 @@ __license__ = "MIT" import unittest import os import sys import numpy as np from deepchem.feat.mol_graphs import ConvMol from deepchem.feat.mol_graphs import MultiConvMol from deepchem.feat.graph_features import ConvMolFeaturizer from deepchem.feat.graph_features import ConvMolFeaturizer, AtomicConvFeaturizer class TestConvMolFeaturizer(unittest.TestCase): Loading Loading @@ -95,3 +92,36 @@ class TestConvMolFeaturizer(unittest.TestCase): assert np.array_equal(deg_adj_lists[4], np.zeros([0, 4], dtype=np.int32)) assert np.array_equal(deg_adj_lists[5], np.zeros([0, 5], dtype=np.int32)) assert np.array_equal(deg_adj_lists[6], np.zeros([0, 6], dtype=np.int32)) class TestAtomicConvFeaturizer(unittest.TestCase): def test_feature_generation(self): """Test if featurization works using AtomicConvFeaturizer.""" dir_path = os.path.dirname(os.path.realpath(__file__)) ligand_file = os.path.join(dir_path, "data/3zso_ligand_hyd.pdb") protein_file = os.path.join(dir_path, "data/3zso_protein.pdb") # Pulled from PDB files. For larger datasets with more PDBs, would use # max num atoms instead of exact. frag1_num_atoms = 44 # for ligand atoms frag2_num_atoms = 2336 # for protein atoms complex_num_atoms = 2380 # in total max_num_neighbors = 4 # Cutoff in angstroms neighbor_cutoff = 4 labels = np.array([0, 0]) featurizer = AtomicConvFeaturizer( labels=labels, batch_size=1, epochs=1, frag1_num_atoms=frag1_num_atoms, frag2_num_atoms=frag2_num_atoms, complex_num_atoms=complex_num_atoms, max_num_neighbors=max_num_neighbors, neighbor_cutoff=neighbor_cutoff) features, _ = featurizer.featurize_complexes([ligand_file, ligand_file], [protein_file, protein_file])
deepchem/molnet/load_function/pdbbind_datasets.py +18 −0 Original line number Diff line number Diff line Loading @@ -18,6 +18,7 @@ import logging import tarfile from deepchem.feat import rdkit_grid_featurizer as rgf from deepchem.feat.atomic_coordinates import ComplexNeighborListFragmentAtomicCoordinates from deepchem.feat.graph_features import AtomicConvFeaturizer logger = logging.getLogger(__name__) Loading Loading @@ -233,6 +234,22 @@ def load_pdbbind(featurizer="grid", split="random", subset="core", reload=True): frag1_num_atoms, frag2_num_atoms, complex_num_atoms, max_num_neighbors, neighbor_cutoff) elif featurizer == "atomic_conv": frag1_num_atoms = 70 # for ligand atoms frag2_num_atoms = 24000 # for protein atoms complex_num_atoms = 24070 # in total max_num_neighbors = 4 # Cutoff in angstroms neighbor_cutoff = 4 featurizer = AtomicConvFeaturizer( labels=labels, frag1_num_atoms=frag1_num_atoms, frag2_num_atoms=frag2_num_atoms, complex_num_atoms=complex_num_atoms, neighbor_cutoff=neighbor_cutoff, max_num_neighbors=max_num_neighbors, batch_size=64) else: raise ValueError("Featurizer not supported") print("Featurizing Complexes") Loading @@ -241,6 +258,7 @@ def load_pdbbind(featurizer="grid", split="random", subset="core", reload=True): # Delete labels for failing elements labels = np.delete(labels, failures) dataset = deepchem.data.DiskDataset.from_numpy(features, labels) print('Featurization complete.') # No transformations of data transformers = [] if split == None: Loading
