Loading deepchem/feat/__init__.py +6 −1 Original line number Diff line number Diff line Loading @@ -25,6 +25,7 @@ from deepchem.feat.molecule_featurizers import MACCSKeysFingerprint from deepchem.feat.molecule_featurizers import MordredDescriptors from deepchem.feat.molecule_featurizers import Mol2VecFingerprint from deepchem.feat.molecule_featurizers import MolGraphConvFeaturizer from deepchem.feat.molecule_featurizers import MolGanFeaturizer from deepchem.feat.molecule_featurizers import OneHotFeaturizer from deepchem.feat.molecule_featurizers import PubChemFingerprint from deepchem.feat.molecule_featurizers import RawFeaturizer Loading @@ -36,7 +37,8 @@ from deepchem.feat.molecule_featurizers import SmilesToSeq, create_char_to_idx from deepchem.feat.complex_featurizers import RdkitGridFeaturizer from deepchem.feat.complex_featurizers import NeighborListAtomicCoordinates from deepchem.feat.complex_featurizers import NeighborListComplexAtomicCoordinates from deepchem.feat.complex_featurizers import ComplexNeighborListFragmentAtomicCoordinates from deepchem.feat.complex_featurizers import ( ComplexNeighborListFragmentAtomicCoordinates,) from deepchem.feat.complex_featurizers import ContactCircularFingerprint from deepchem.feat.complex_featurizers import ContactCircularVoxelizer from deepchem.feat.complex_featurizers import SplifFingerprint Loading Loading @@ -65,3 +67,6 @@ try: from deepchem.feat.smiles_tokenizer import BasicSmilesTokenizer except ModuleNotFoundError: pass # support classes from deepchem.feat.molecule_featurizers import GraphMatrix No newline at end of file deepchem/feat/molecule_featurizers/__init__.py +2 −0 Original line number Diff line number Diff line Loading @@ -4,6 +4,7 @@ from deepchem.feat.molecule_featurizers.bp_symmetry_function_input import BPSymm from deepchem.feat.molecule_featurizers.circular_fingerprint import CircularFingerprint from deepchem.feat.molecule_featurizers.coulomb_matrices import CoulombMatrix from deepchem.feat.molecule_featurizers.coulomb_matrices import CoulombMatrixEig from deepchem.feat.molecule_featurizers.molgan_featurizer import GraphMatrix from deepchem.feat.molecule_featurizers.maccs_keys_fingerprint import MACCSKeysFingerprint from deepchem.feat.molecule_featurizers.mordred_descriptors import MordredDescriptors from deepchem.feat.molecule_featurizers.mol2vec_fingerprint import Mol2VecFingerprint Loading @@ -14,3 +15,4 @@ from deepchem.feat.molecule_featurizers.rdkit_descriptors import RDKitDescriptor from deepchem.feat.molecule_featurizers.smiles_to_image import SmilesToImage from deepchem.feat.molecule_featurizers.smiles_to_seq import SmilesToSeq, create_char_to_idx from deepchem.feat.molecule_featurizers.mol_graph_conv_featurizer import MolGraphConvFeaturizer from deepchem.feat.molecule_featurizers.molgan_featurizer import MolGanFeaturizer deepchem/feat/molecule_featurizers/molgan_featurizer.py 0 → 100644 +254 −0 Original line number Diff line number Diff line import logging import numpy as np from deepchem.utils.typing import RDKitBond, RDKitMol, List from deepchem.feat.base_classes import MolecularFeaturizer logger = logging.getLogger(__name__) class GraphMatrix: """ This is class used to store data for MolGAN neural networks. Parameters ---------- node_features: np.ndarray Node feature matrix with shape [num_nodes, num_node_features] edge_features: np.ndarray, Edge feature matrix with shape [num_nodes, num_nodes] Returns ------- graph: GraphMatrix A molecule graph with some features. """ def __init__(self, adjacency_matrix: np.ndarray, node_features: np.ndarray): self.adjacency_matrix = adjacency_matrix self.node_features = node_features class MolGanFeaturizer(MolecularFeaturizer): """ 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.""" def __init__( self, max_atom_count: int = 9, kekulize: bool = True, bond_labels: List[RDKitBond] = None, atom_labels: List[int] = None, ): """ Parameters ---------- max_atom_count: int, default 9 Maximum number of atoms used for creation of adjacency matrix. Molecules cannot have more atoms than this number Implicit hydrogens do not count. kekulize: bool, default True Should molecules be kekulized. Solves number of issues with defeaturization when used. bond_labels: List[RDKitBond] List of types of bond used for generation of adjacency matrix atom_labels: List[int] List of atomic numbers used for generation of node features References --------- .. [1] Nicola De Cao et al. "MolGAN: An implicit generative model for small molecular graphs`<https://arxiv.org/abs/1805.11973>`" """ self.max_atom_count = max_atom_count self.kekulize = kekulize try: from rdkit import Chem except ModuleNotFoundError: raise ImportError("This class requires RDKit to be installed.") # bond labels if bond_labels is None: self.bond_labels = [ Chem.rdchem.BondType.ZERO, Chem.rdchem.BondType.SINGLE, Chem.rdchem.BondType.DOUBLE, Chem.rdchem.BondType.TRIPLE, Chem.rdchem.BondType.AROMATIC, ] else: self.bond_labels = bond_labels # atom labels if atom_labels is None: self.atom_labels = [0, 6, 7, 8, 9] # C,N,O,F else: self.atom_labels = atom_labels # create bond encoders and decoders self.bond_encoder = {l: i for i, l in enumerate(self.bond_labels)} self.bond_decoder = {i: l for i, l in enumerate(self.bond_labels)} # create atom encoders and decoders self.atom_encoder = {l: i for i, l in enumerate(self.atom_labels)} self.atom_decoder = {i: l for i, l in enumerate(self.atom_labels)} def _featurize(self, mol: RDKitMol) -> GraphMatrix: """ Calculate adjacency matrix and nodes features for RDKitMol. It strips any chirality and charges Parameters ---------- mol: rdkit.Chem.rdchem.Mol RDKit mol object. Returns ------- graph: GraphMatrix A molecule graph with some features. """ try: from rdkit import Chem except ModuleNotFoundError: raise ImportError("This method requires RDKit to be installed.") if self.kekulize: Chem.Kekulize(mol) A = np.zeros( shape=(self.max_atom_count, self.max_atom_count), dtype=np.float32) bonds = mol.GetBonds() begin, end = [b.GetBeginAtomIdx() for b in bonds], [ b.GetEndAtomIdx() for b in bonds ] bond_type = [self.bond_encoder[b.GetBondType()] for b in bonds] A[begin, end] = bond_type A[end, begin] = bond_type degree = np.sum(A[:mol.GetNumAtoms(), :mol.GetNumAtoms()], axis=-1) X = np.array( [self.atom_encoder[atom.GetAtomicNum()] for atom in mol.GetAtoms()] + [0] * (self.max_atom_count - mol.GetNumAtoms()), dtype=np.int32, ) graph = GraphMatrix(A, X) return graph if (degree > 0).all() else None def _defeaturize(self, graph_matrix: GraphMatrix, sanitize: bool = True, cleanup: bool = True) -> RDKitMol: """ Recreate RDKitMol from GraphMatrix object. Same featurizer need to be used for featurization and defeaturization. It only recreates bond and atom types, any kind of additional features like chirality or charge are not included. Therefore, any checks of type: original_smiles == defeaturized_smiles will fail on chiral or charged compounds. Parameters ---------- graph_matrix: GraphMatrix GraphMatrix object. sanitize: bool, default True Should RDKit sanitization be included in the process. cleanup: bool, default True Splits salts and removes compounds with "*" atom types Returns ------- mol: RDKitMol object RDKitMol object representing molecule. """ try: from rdkit import Chem except ModuleNotFoundError: raise ImportError("This method requires RDKit to be installed.") if not isinstance(graph_matrix, GraphMatrix): return None node_labels = graph_matrix.node_features edge_labels = graph_matrix.adjacency_matrix mol = Chem.RWMol() for node_label in node_labels: mol.AddAtom(Chem.Atom(self.atom_decoder[node_label])) for start, end in zip(*np.nonzero(edge_labels)): if start > end: mol.AddBond( int(start), int(end), self.bond_decoder[edge_labels[start, end]]) if sanitize: try: Chem.SanitizeMol(mol) except Exception: mol = None if cleanup: try: smiles = Chem.MolToSmiles(mol) smiles = max(smiles.split("."), key=len) if "*" not in smiles: mol = Chem.MolFromSmiles(smiles) else: mol = None except Exception: mol = None return mol def defeaturize(self, graphs: GraphMatrix, log_every_n: int = 1000) -> np.ndarray: """ Calculates molecules from corresponding GraphMatrix objects. Parameters ---------- graphs: GraphMatrix / iterable GraphMatrix object or corresponding iterable log_every_n: int, default 1000 Logging messages reported every `log_every_n` samples. Returns ------- features: np.ndarray A numpy array containing RDKitMol objext. """ # Special case handling of single molecule if isinstance(graphs, GraphMatrix): graphs = [graphs] else: # Convert iterables to list graphs = list(graphs) molecules = [] for i, gr in enumerate(graphs): if i % log_every_n == 0: logger.info("Featurizing datapoint %i" % i) try: molecules.append(self._defeaturize(gr)) except Exception as e: logger.warning( "Failed to defeaturize datapoint %d, %s. Appending empty array", i, gr, ) logger.warning("Exception message: {}".format(e)) molecules.append(np.array([])) molecules = np.asarray(molecules) return molecules deepchem/feat/tests/test_graph_matrix.py 0 → 100644 +26 −0 Original line number Diff line number Diff line import unittest import numpy as np from deepchem.feat.molecule_featurizers import GraphMatrix class TestGraphMatrix(unittest.TestCase): def test_graph_matrix(self): max_atom_count = 5 atom_array = [7, 7, 7, 8, 8, 8, 9, 6] A = np.zeros(shape=(max_atom_count, max_atom_count), dtype=np.float32) X = np.array(atom_array, dtype=np.int32) graph_matrix = GraphMatrix(adjacency_matrix=A, node_features=X) assert isinstance(graph_matrix.adjacency_matrix, np.ndarray) assert isinstance(graph_matrix.node_features, np.ndarray) assert graph_matrix.adjacency_matrix.dtype == np.float32 assert graph_matrix.node_features.dtype == np.int32 assert graph_matrix.adjacency_matrix.shape == A.shape assert graph_matrix.node_features.shape == X.shape if __name__ == '__main__': unittest.main() deepchem/feat/tests/test_molgan_featurizer.py 0 → 100644 +101 −0 Original line number Diff line number Diff line import unittest from deepchem.feat.molecule_featurizers import MolGanFeaturizer from deepchem.feat.molecule_featurizers import GraphMatrix class TestMolganFeaturizer(unittest.TestCase): def test_featurizer_smiles(self): try: from rdkit import Chem except ModuleNotFoundError: raise ImportError("This method requires RDKit to be installed.") smiles = [ 'Cc1ccccc1CO', 'CC1CCC(C)C(N)C1', 'CCC(N)=O', 'Fc1cccc(F)c1', 'CC(C)F', 'C1COC2NCCC2C1', 'C1=NCc2ccccc21' ] invalid_smiles = ['axa', 'xyz', 'inv'] featurizer = MolGanFeaturizer() valid_data = featurizer.featurize(smiles) invalid_data = featurizer.featurize(invalid_smiles) # test featurization valid_graphs = list( filter(lambda x: isinstance(x, GraphMatrix), valid_data)) invalid_graphs = list( filter(lambda x: not isinstance(x, GraphMatrix), invalid_data)) assert len(valid_graphs) == len(smiles) assert len(invalid_graphs) == len(invalid_smiles) # test defeaturization valid_mols = featurizer.defeaturize(valid_graphs) invalid_mols = featurizer.defeaturize(invalid_graphs) valid_mols = list( filter(lambda x: isinstance(x, Chem.rdchem.Mol), valid_mols)) invalid_mols = list( filter(lambda x: not isinstance(x, Chem.rdchem.Mol), invalid_mols)) assert len(valid_graphs) == len(valid_mols) assert len(invalid_graphs) == len(invalid_mols) mols = list(map(Chem.MolFromSmiles, smiles)) redone_smiles = list(map(Chem.MolToSmiles, mols)) # sanity check; see if something weird does not happen with rdkit assert redone_smiles == smiles # check if original smiles match defeaturized smiles defe_smiles = list(map(Chem.MolToSmiles, valid_mols)) assert defe_smiles == smiles def test_featurizer_rdkit(self): try: from rdkit import Chem except ModuleNotFoundError: raise ImportError("This method requires RDKit to be installed.") smiles = [ 'Cc1ccccc1CO', 'CC1CCC(C)C(N)C1', 'CCC(N)=O', 'Fc1cccc(F)c1', 'CC(C)F', 'C1COC2NCCC2C1', 'C1=NCc2ccccc21' ] invalid_smiles = ['axa', 'xyz', 'inv'] valid_molecules = list(map(Chem.MolFromSmiles, smiles)) invalid_molecules = list(map(Chem.MolFromSmiles, invalid_smiles)) redone_smiles = list(map(Chem.MolToSmiles, valid_molecules)) # sanity check; see if something weird does not happen with rdkit assert redone_smiles == smiles featurizer = MolGanFeaturizer() valid_data = featurizer.featurize(valid_molecules) invalid_data = featurizer.featurize(invalid_molecules) # test featurization valid_graphs = list( filter(lambda x: isinstance(x, GraphMatrix), valid_data)) invalid_graphs = list( filter(lambda x: not isinstance(x, GraphMatrix), invalid_data)) assert len(valid_graphs) == len(valid_molecules) assert len(invalid_graphs) == len(invalid_molecules) # test defeaturization valid_mols = featurizer.defeaturize(valid_graphs) invalid_mols = featurizer.defeaturize(invalid_graphs) valid_mols = list( filter(lambda x: isinstance(x, Chem.rdchem.Mol), valid_mols)) invalid_mols = list( filter(lambda x: not isinstance(x, Chem.rdchem.Mol), invalid_mols)) assert len(valid_mols) == len(valid_graphs) assert len(invalid_mols) == len(invalid_graphs) # check if original smiles match defeaturized smiles defe_smiles = list(map(Chem.MolToSmiles, valid_mols)) assert defe_smiles == smiles if __name__ == '__main__': unittest.main() Loading
deepchem/feat/__init__.py +6 −1 Original line number Diff line number Diff line Loading @@ -25,6 +25,7 @@ from deepchem.feat.molecule_featurizers import MACCSKeysFingerprint from deepchem.feat.molecule_featurizers import MordredDescriptors from deepchem.feat.molecule_featurizers import Mol2VecFingerprint from deepchem.feat.molecule_featurizers import MolGraphConvFeaturizer from deepchem.feat.molecule_featurizers import MolGanFeaturizer from deepchem.feat.molecule_featurizers import OneHotFeaturizer from deepchem.feat.molecule_featurizers import PubChemFingerprint from deepchem.feat.molecule_featurizers import RawFeaturizer Loading @@ -36,7 +37,8 @@ from deepchem.feat.molecule_featurizers import SmilesToSeq, create_char_to_idx from deepchem.feat.complex_featurizers import RdkitGridFeaturizer from deepchem.feat.complex_featurizers import NeighborListAtomicCoordinates from deepchem.feat.complex_featurizers import NeighborListComplexAtomicCoordinates from deepchem.feat.complex_featurizers import ComplexNeighborListFragmentAtomicCoordinates from deepchem.feat.complex_featurizers import ( ComplexNeighborListFragmentAtomicCoordinates,) from deepchem.feat.complex_featurizers import ContactCircularFingerprint from deepchem.feat.complex_featurizers import ContactCircularVoxelizer from deepchem.feat.complex_featurizers import SplifFingerprint Loading Loading @@ -65,3 +67,6 @@ try: from deepchem.feat.smiles_tokenizer import BasicSmilesTokenizer except ModuleNotFoundError: pass # support classes from deepchem.feat.molecule_featurizers import GraphMatrix No newline at end of file
deepchem/feat/molecule_featurizers/__init__.py +2 −0 Original line number Diff line number Diff line Loading @@ -4,6 +4,7 @@ from deepchem.feat.molecule_featurizers.bp_symmetry_function_input import BPSymm from deepchem.feat.molecule_featurizers.circular_fingerprint import CircularFingerprint from deepchem.feat.molecule_featurizers.coulomb_matrices import CoulombMatrix from deepchem.feat.molecule_featurizers.coulomb_matrices import CoulombMatrixEig from deepchem.feat.molecule_featurizers.molgan_featurizer import GraphMatrix from deepchem.feat.molecule_featurizers.maccs_keys_fingerprint import MACCSKeysFingerprint from deepchem.feat.molecule_featurizers.mordred_descriptors import MordredDescriptors from deepchem.feat.molecule_featurizers.mol2vec_fingerprint import Mol2VecFingerprint Loading @@ -14,3 +15,4 @@ from deepchem.feat.molecule_featurizers.rdkit_descriptors import RDKitDescriptor from deepchem.feat.molecule_featurizers.smiles_to_image import SmilesToImage from deepchem.feat.molecule_featurizers.smiles_to_seq import SmilesToSeq, create_char_to_idx from deepchem.feat.molecule_featurizers.mol_graph_conv_featurizer import MolGraphConvFeaturizer from deepchem.feat.molecule_featurizers.molgan_featurizer import MolGanFeaturizer
deepchem/feat/molecule_featurizers/molgan_featurizer.py 0 → 100644 +254 −0 Original line number Diff line number Diff line import logging import numpy as np from deepchem.utils.typing import RDKitBond, RDKitMol, List from deepchem.feat.base_classes import MolecularFeaturizer logger = logging.getLogger(__name__) class GraphMatrix: """ This is class used to store data for MolGAN neural networks. Parameters ---------- node_features: np.ndarray Node feature matrix with shape [num_nodes, num_node_features] edge_features: np.ndarray, Edge feature matrix with shape [num_nodes, num_nodes] Returns ------- graph: GraphMatrix A molecule graph with some features. """ def __init__(self, adjacency_matrix: np.ndarray, node_features: np.ndarray): self.adjacency_matrix = adjacency_matrix self.node_features = node_features class MolGanFeaturizer(MolecularFeaturizer): """ 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.""" def __init__( self, max_atom_count: int = 9, kekulize: bool = True, bond_labels: List[RDKitBond] = None, atom_labels: List[int] = None, ): """ Parameters ---------- max_atom_count: int, default 9 Maximum number of atoms used for creation of adjacency matrix. Molecules cannot have more atoms than this number Implicit hydrogens do not count. kekulize: bool, default True Should molecules be kekulized. Solves number of issues with defeaturization when used. bond_labels: List[RDKitBond] List of types of bond used for generation of adjacency matrix atom_labels: List[int] List of atomic numbers used for generation of node features References --------- .. [1] Nicola De Cao et al. "MolGAN: An implicit generative model for small molecular graphs`<https://arxiv.org/abs/1805.11973>`" """ self.max_atom_count = max_atom_count self.kekulize = kekulize try: from rdkit import Chem except ModuleNotFoundError: raise ImportError("This class requires RDKit to be installed.") # bond labels if bond_labels is None: self.bond_labels = [ Chem.rdchem.BondType.ZERO, Chem.rdchem.BondType.SINGLE, Chem.rdchem.BondType.DOUBLE, Chem.rdchem.BondType.TRIPLE, Chem.rdchem.BondType.AROMATIC, ] else: self.bond_labels = bond_labels # atom labels if atom_labels is None: self.atom_labels = [0, 6, 7, 8, 9] # C,N,O,F else: self.atom_labels = atom_labels # create bond encoders and decoders self.bond_encoder = {l: i for i, l in enumerate(self.bond_labels)} self.bond_decoder = {i: l for i, l in enumerate(self.bond_labels)} # create atom encoders and decoders self.atom_encoder = {l: i for i, l in enumerate(self.atom_labels)} self.atom_decoder = {i: l for i, l in enumerate(self.atom_labels)} def _featurize(self, mol: RDKitMol) -> GraphMatrix: """ Calculate adjacency matrix and nodes features for RDKitMol. It strips any chirality and charges Parameters ---------- mol: rdkit.Chem.rdchem.Mol RDKit mol object. Returns ------- graph: GraphMatrix A molecule graph with some features. """ try: from rdkit import Chem except ModuleNotFoundError: raise ImportError("This method requires RDKit to be installed.") if self.kekulize: Chem.Kekulize(mol) A = np.zeros( shape=(self.max_atom_count, self.max_atom_count), dtype=np.float32) bonds = mol.GetBonds() begin, end = [b.GetBeginAtomIdx() for b in bonds], [ b.GetEndAtomIdx() for b in bonds ] bond_type = [self.bond_encoder[b.GetBondType()] for b in bonds] A[begin, end] = bond_type A[end, begin] = bond_type degree = np.sum(A[:mol.GetNumAtoms(), :mol.GetNumAtoms()], axis=-1) X = np.array( [self.atom_encoder[atom.GetAtomicNum()] for atom in mol.GetAtoms()] + [0] * (self.max_atom_count - mol.GetNumAtoms()), dtype=np.int32, ) graph = GraphMatrix(A, X) return graph if (degree > 0).all() else None def _defeaturize(self, graph_matrix: GraphMatrix, sanitize: bool = True, cleanup: bool = True) -> RDKitMol: """ Recreate RDKitMol from GraphMatrix object. Same featurizer need to be used for featurization and defeaturization. It only recreates bond and atom types, any kind of additional features like chirality or charge are not included. Therefore, any checks of type: original_smiles == defeaturized_smiles will fail on chiral or charged compounds. Parameters ---------- graph_matrix: GraphMatrix GraphMatrix object. sanitize: bool, default True Should RDKit sanitization be included in the process. cleanup: bool, default True Splits salts and removes compounds with "*" atom types Returns ------- mol: RDKitMol object RDKitMol object representing molecule. """ try: from rdkit import Chem except ModuleNotFoundError: raise ImportError("This method requires RDKit to be installed.") if not isinstance(graph_matrix, GraphMatrix): return None node_labels = graph_matrix.node_features edge_labels = graph_matrix.adjacency_matrix mol = Chem.RWMol() for node_label in node_labels: mol.AddAtom(Chem.Atom(self.atom_decoder[node_label])) for start, end in zip(*np.nonzero(edge_labels)): if start > end: mol.AddBond( int(start), int(end), self.bond_decoder[edge_labels[start, end]]) if sanitize: try: Chem.SanitizeMol(mol) except Exception: mol = None if cleanup: try: smiles = Chem.MolToSmiles(mol) smiles = max(smiles.split("."), key=len) if "*" not in smiles: mol = Chem.MolFromSmiles(smiles) else: mol = None except Exception: mol = None return mol def defeaturize(self, graphs: GraphMatrix, log_every_n: int = 1000) -> np.ndarray: """ Calculates molecules from corresponding GraphMatrix objects. Parameters ---------- graphs: GraphMatrix / iterable GraphMatrix object or corresponding iterable log_every_n: int, default 1000 Logging messages reported every `log_every_n` samples. Returns ------- features: np.ndarray A numpy array containing RDKitMol objext. """ # Special case handling of single molecule if isinstance(graphs, GraphMatrix): graphs = [graphs] else: # Convert iterables to list graphs = list(graphs) molecules = [] for i, gr in enumerate(graphs): if i % log_every_n == 0: logger.info("Featurizing datapoint %i" % i) try: molecules.append(self._defeaturize(gr)) except Exception as e: logger.warning( "Failed to defeaturize datapoint %d, %s. Appending empty array", i, gr, ) logger.warning("Exception message: {}".format(e)) molecules.append(np.array([])) molecules = np.asarray(molecules) return molecules
deepchem/feat/tests/test_graph_matrix.py 0 → 100644 +26 −0 Original line number Diff line number Diff line import unittest import numpy as np from deepchem.feat.molecule_featurizers import GraphMatrix class TestGraphMatrix(unittest.TestCase): def test_graph_matrix(self): max_atom_count = 5 atom_array = [7, 7, 7, 8, 8, 8, 9, 6] A = np.zeros(shape=(max_atom_count, max_atom_count), dtype=np.float32) X = np.array(atom_array, dtype=np.int32) graph_matrix = GraphMatrix(adjacency_matrix=A, node_features=X) assert isinstance(graph_matrix.adjacency_matrix, np.ndarray) assert isinstance(graph_matrix.node_features, np.ndarray) assert graph_matrix.adjacency_matrix.dtype == np.float32 assert graph_matrix.node_features.dtype == np.int32 assert graph_matrix.adjacency_matrix.shape == A.shape assert graph_matrix.node_features.shape == X.shape if __name__ == '__main__': unittest.main()
deepchem/feat/tests/test_molgan_featurizer.py 0 → 100644 +101 −0 Original line number Diff line number Diff line import unittest from deepchem.feat.molecule_featurizers import MolGanFeaturizer from deepchem.feat.molecule_featurizers import GraphMatrix class TestMolganFeaturizer(unittest.TestCase): def test_featurizer_smiles(self): try: from rdkit import Chem except ModuleNotFoundError: raise ImportError("This method requires RDKit to be installed.") smiles = [ 'Cc1ccccc1CO', 'CC1CCC(C)C(N)C1', 'CCC(N)=O', 'Fc1cccc(F)c1', 'CC(C)F', 'C1COC2NCCC2C1', 'C1=NCc2ccccc21' ] invalid_smiles = ['axa', 'xyz', 'inv'] featurizer = MolGanFeaturizer() valid_data = featurizer.featurize(smiles) invalid_data = featurizer.featurize(invalid_smiles) # test featurization valid_graphs = list( filter(lambda x: isinstance(x, GraphMatrix), valid_data)) invalid_graphs = list( filter(lambda x: not isinstance(x, GraphMatrix), invalid_data)) assert len(valid_graphs) == len(smiles) assert len(invalid_graphs) == len(invalid_smiles) # test defeaturization valid_mols = featurizer.defeaturize(valid_graphs) invalid_mols = featurizer.defeaturize(invalid_graphs) valid_mols = list( filter(lambda x: isinstance(x, Chem.rdchem.Mol), valid_mols)) invalid_mols = list( filter(lambda x: not isinstance(x, Chem.rdchem.Mol), invalid_mols)) assert len(valid_graphs) == len(valid_mols) assert len(invalid_graphs) == len(invalid_mols) mols = list(map(Chem.MolFromSmiles, smiles)) redone_smiles = list(map(Chem.MolToSmiles, mols)) # sanity check; see if something weird does not happen with rdkit assert redone_smiles == smiles # check if original smiles match defeaturized smiles defe_smiles = list(map(Chem.MolToSmiles, valid_mols)) assert defe_smiles == smiles def test_featurizer_rdkit(self): try: from rdkit import Chem except ModuleNotFoundError: raise ImportError("This method requires RDKit to be installed.") smiles = [ 'Cc1ccccc1CO', 'CC1CCC(C)C(N)C1', 'CCC(N)=O', 'Fc1cccc(F)c1', 'CC(C)F', 'C1COC2NCCC2C1', 'C1=NCc2ccccc21' ] invalid_smiles = ['axa', 'xyz', 'inv'] valid_molecules = list(map(Chem.MolFromSmiles, smiles)) invalid_molecules = list(map(Chem.MolFromSmiles, invalid_smiles)) redone_smiles = list(map(Chem.MolToSmiles, valid_molecules)) # sanity check; see if something weird does not happen with rdkit assert redone_smiles == smiles featurizer = MolGanFeaturizer() valid_data = featurizer.featurize(valid_molecules) invalid_data = featurizer.featurize(invalid_molecules) # test featurization valid_graphs = list( filter(lambda x: isinstance(x, GraphMatrix), valid_data)) invalid_graphs = list( filter(lambda x: not isinstance(x, GraphMatrix), invalid_data)) assert len(valid_graphs) == len(valid_molecules) assert len(invalid_graphs) == len(invalid_molecules) # test defeaturization valid_mols = featurizer.defeaturize(valid_graphs) invalid_mols = featurizer.defeaturize(invalid_graphs) valid_mols = list( filter(lambda x: isinstance(x, Chem.rdchem.Mol), valid_mols)) invalid_mols = list( filter(lambda x: not isinstance(x, Chem.rdchem.Mol), invalid_mols)) assert len(valid_mols) == len(valid_graphs) assert len(invalid_mols) == len(invalid_graphs) # check if original smiles match defeaturized smiles defe_smiles = list(map(Chem.MolToSmiles, valid_mols)) assert defe_smiles == smiles if __name__ == '__main__': unittest.main()
