Loading deepchem/feat/molecule_featurizers/mol2vec_fingerprint.py +39 −2 Original line number Diff line number Diff line Loading @@ -58,13 +58,12 @@ class Mol2VecFingerprint(MolecularFeaturizer): """ try: from gensim.models import word2vec from mol2vec.features import mol2alt_sentence, sentences2vec from mol2vec.features import mol2alt_sentence except ModuleNotFoundError: raise ImportError("This class requires mol2vec to be installed.") self.radius = radius self.unseen = unseen self.sentences2vec = sentences2vec self.mol2alt_sentence = mol2alt_sentence if pretrain_model_path is None: data_dir = get_data_dir() Loading @@ -78,6 +77,44 @@ class Mol2VecFingerprint(MolecularFeaturizer): # load pretrained models self.model = word2vec.Word2Vec.load(pretrain_model_path) def sentences2vec(self, sentences: list, model, unseen=None) -> np.ndarray: """Generate vectors for each sentence (list) in a list of sentences. Vector is simply a sum of vectors for individual words. Parameters ---------- sentences : list, array List with sentences model : word2vec.Word2Vec Gensim word2vec model unseen : None, str Keyword for unseen words. If None, those words are skipped. https://stats.stackexchange.com/questions/163005/how-to-set-the-dictionary-for-text-analysis-using-neural-networks/163032#163032 Returns ------- np.array """ keys = set(model.wv.key_to_index.keys()) vec = [] if unseen: unseen_vec = model.wv.get_vector(unseen) for sentence in sentences: if unseen: vec.append( sum([ model.wv.get_vector(y) if y in set(sentence) & keys else unseen_vec for y in sentence ])) else: vec.append( sum([ model.wv.get_vector(y) for y in sentence if y in set(sentence) & keys ])) return np.array(vec) def _featurize(self, mol: RDKitMol) -> np.ndarray: """ Calculate Mordred descriptors. Loading deepchem/utils/molecule_feature_utils.py +56 −0 Original line number Diff line number Diff line Loading @@ -35,6 +35,8 @@ DEFAULT_RING_SIZE_SET = [3, 4, 5, 6, 7, 8] DEFAULT_BOND_TYPE_SET = ["SINGLE", "DOUBLE", "TRIPLE", "AROMATIC"] DEFAULT_BOND_STEREO_SET = ["STEREONONE", "STEREOANY", "STEREOZ", "STEREOE"] DEFAULT_GRAPH_DISTANCE_SET = [1, 2, 3, 4, 5, 6, 7] DEFAULT_ATOM_IMPLICIT_VALENCE_SET = [0, 1, 2, 3, 4, 5, 6] DEFAULT_ATOM_EXPLICIT_VALENCE_SET = [1, 2, 3, 4, 5, 6] class _ChemicalFeaturesFactory: Loading Loading @@ -356,6 +358,60 @@ def get_atom_total_degree_one_hot( include_unknown_set) def get_atom_implicit_valence_one_hot( atom: RDKitAtom, allowable_set: List[int] = DEFAULT_ATOM_IMPLICIT_VALENCE_SET, include_unknown_set: bool = True) -> List[float]: """Get an one-hot feature of implicit valence of an atom. Parameters --------- atom: rdkit.Chem.rdchem.Atom RDKit atom object allowable_set: List[int] Atom implicit valence to consider. The default set is `[0, 1, ..., 6]` include_unknown_set: bool, default True If true, the index of all types not in `allowable_set` is `len(allowable_set)`. Returns ------- List[float] A one-hot vector of implicit valence an atom has. If `include_unknown_set` is False, the length is `len(allowable_set)`. If `include_unknown_set` is True, the length is `len(allowable_set) + 1`. """ return one_hot_encode(atom.GetImplicitValence(), allowable_set, include_unknown_set) def get_atom_explicit_valence_one_hot( atom: RDKitAtom, allowable_set: List[int] = DEFAULT_ATOM_EXPLICIT_VALENCE_SET, include_unknown_set: bool = True) -> List[float]: """Get an one-hot feature of explicit valence of an atom. Parameters --------- atom: rdkit.Chem.rdchem.Atom RDKit atom object allowable_set: List[int] Atom explicit valence to consider. The default set is `[1, ..., 6]` include_unknown_set: bool, default True If true, the index of all types not in `allowable_set` is `len(allowable_set)`. Returns ------- List[float] A one-hot vector of explicit valence an atom has. If `include_unknown_set` is False, the length is `len(allowable_set)`. If `include_unknown_set` is True, the length is `len(allowable_set) + 1`. """ return one_hot_encode(atom.GetExplicitValence(), allowable_set, include_unknown_set) ################################################################# # bond (edge) featurization ################################################################# Loading deepchem/utils/rdkit_utils.py +68 −0 Original line number Diff line number Diff line Loading @@ -626,3 +626,71 @@ def compute_ring_normal(mol, ring_indices): v2 = points[2] - points[0] normal = np.cross(v1, v2) return normal def compute_all_pairs_shortest_path( mol) -> Dict[Tuple[int, int], Tuple[int, int]]: """Computes the All pair shortest between every pair of nodes in terms of Rdkit Atom indexes. Parameters: ----------- mol: rdkit.rdchem.Mol Molecule containing a ring Returns: -------- paths_dict: Dict representing every atom-atom pair as key in Rdkit index and value as the shortest path between each atom pair in terms of Atom index. """ try: from rdkit import Chem except: raise ImportError("This class requires RDkit installed") n_atoms = mol.GetNumAtoms() paths_dict = {(i, j): Chem.rdmolops.GetShortestPath(mol, i, j) for i in range(n_atoms) for j in range(n_atoms) if i < j} return paths_dict def compute_pairwise_ring_info(mol): """ Computes all atom-atom pair belong to same ring with its ring size and its aromaticity. Parameters: ----------- mol: rdkit.rdchem.Mol Molecule containing a ring Returns: -------- rings_dict: Key consisting of all node-node pair sharing the same ring and value as a tuple of size of ring and its aromaticity. """ try: from rdkit import Chem except: raise ImportError("This class requires RDkit installed") rings_dict = {} def ordered_pair(a, b): return (a, b) if a < b else (b, a) ssr = [list(x) for x in Chem.GetSymmSSSR(mol)] for ring in ssr: ring_sz = len(ring) is_aromatic = True for atom_idx in ring: if not mol.GetAtoms()[atom_idx].GetIsAromatic(): is_aromatic = False break for ring_idx, atom_idx in enumerate(ring): for other_idx in ring[ring_idx:]: atom_pair = ordered_pair(atom_idx, other_idx) if atom_pair not in rings_dict: rings_dict[atom_pair] = [(ring_sz, is_aromatic)] else: if (ring_sz, is_aromatic) not in rings_dict[atom_pair]: rings_dict[atom_pair].append((ring_sz, is_aromatic)) return rings_dict deepchem/utils/test/test_molecule_feature_utils.py +22 −0 Original line number Diff line number Diff line Loading @@ -11,6 +11,8 @@ from deepchem.utils.molecule_feature_utils import get_atom_chirality_one_hot from deepchem.utils.molecule_feature_utils import get_atom_formal_charge from deepchem.utils.molecule_feature_utils import get_atom_partial_charge from deepchem.utils.molecule_feature_utils import get_atom_total_degree_one_hot from deepchem.utils.molecule_feature_utils import get_atom_implicit_valence_one_hot from deepchem.utils.molecule_feature_utils import get_atom_explicit_valence_one_hot from deepchem.utils.molecule_feature_utils import get_bond_type_one_hot from deepchem.utils.molecule_feature_utils import get_bond_is_in_same_ring_one_hot from deepchem.utils.molecule_feature_utils import get_bond_is_conjugated_one_hot Loading Loading @@ -140,6 +142,26 @@ class TestGraphConvUtils(unittest.TestCase): one_hot = get_atom_total_degree_one_hot(atoms[3]) assert one_hot == [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0] def test_get_atom_implicit_valence_one_hot(self): atoms = self.mol.GetAtoms() assert atoms[0].GetSymbol() == "C" one_hot = get_atom_implicit_valence_one_hot(atoms[0]) assert one_hot == [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0] assert atoms[3].GetSymbol() == "O" one_hot = get_atom_implicit_valence_one_hot(atoms[3]) assert one_hot == [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] def test_get_atom_explicit_valence_one_hot(self): atoms = self.mol.GetAtoms() assert atoms[0].GetSymbol() == "C" one_hot = get_atom_explicit_valence_one_hot(atoms[0]) assert one_hot == [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] assert atoms[3].GetSymbol() == "O" one_hot = get_atom_explicit_valence_one_hot(atoms[3]) assert one_hot == [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0] def test_get_bond_type_one_hot(self): bonds = self.mol.GetBonds() one_hot = get_bond_type_one_hot(bonds[0]) Loading deepchem/utils/test/test_rdkit_utils.py +25 −0 Original line number Diff line number Diff line Loading @@ -162,3 +162,28 @@ class TestRdkitUtil(unittest.TestCase): def test_strip_hydrogens(self): pass def test_all_shortest_pairs(self): from rdkit import Chem mol = Chem.MolFromSmiles("CN=C=O") valid_dict = { (0, 1): (0, 1), (0, 2): (0, 1, 2), (0, 3): (0, 1, 2, 3), (1, 2): (1, 2), (1, 3): (1, 2, 3), (2, 3): (2, 3) } assert rdkit_utils.compute_all_pairs_shortest_path(mol) == valid_dict def test_pairwise_ring_info(self): from rdkit import Chem mol = Chem.MolFromSmiles("c1ccccc1") predict_dict = rdkit_utils.compute_pairwise_ring_info(mol) assert all(pair == [(6, True)] for pair in predict_dict.values()) mol = Chem.MolFromSmiles("c1c2ccccc2ccc1") predict_dict = rdkit_utils.compute_pairwise_ring_info(mol) assert all(pair == [(6, True)] for pair in predict_dict.values()) mol = Chem.MolFromSmiles("CN=C=O") predict_dict = rdkit_utils.compute_pairwise_ring_info(mol) assert not predict_dict Loading
deepchem/feat/molecule_featurizers/mol2vec_fingerprint.py +39 −2 Original line number Diff line number Diff line Loading @@ -58,13 +58,12 @@ class Mol2VecFingerprint(MolecularFeaturizer): """ try: from gensim.models import word2vec from mol2vec.features import mol2alt_sentence, sentences2vec from mol2vec.features import mol2alt_sentence except ModuleNotFoundError: raise ImportError("This class requires mol2vec to be installed.") self.radius = radius self.unseen = unseen self.sentences2vec = sentences2vec self.mol2alt_sentence = mol2alt_sentence if pretrain_model_path is None: data_dir = get_data_dir() Loading @@ -78,6 +77,44 @@ class Mol2VecFingerprint(MolecularFeaturizer): # load pretrained models self.model = word2vec.Word2Vec.load(pretrain_model_path) def sentences2vec(self, sentences: list, model, unseen=None) -> np.ndarray: """Generate vectors for each sentence (list) in a list of sentences. Vector is simply a sum of vectors for individual words. Parameters ---------- sentences : list, array List with sentences model : word2vec.Word2Vec Gensim word2vec model unseen : None, str Keyword for unseen words. If None, those words are skipped. https://stats.stackexchange.com/questions/163005/how-to-set-the-dictionary-for-text-analysis-using-neural-networks/163032#163032 Returns ------- np.array """ keys = set(model.wv.key_to_index.keys()) vec = [] if unseen: unseen_vec = model.wv.get_vector(unseen) for sentence in sentences: if unseen: vec.append( sum([ model.wv.get_vector(y) if y in set(sentence) & keys else unseen_vec for y in sentence ])) else: vec.append( sum([ model.wv.get_vector(y) for y in sentence if y in set(sentence) & keys ])) return np.array(vec) def _featurize(self, mol: RDKitMol) -> np.ndarray: """ Calculate Mordred descriptors. Loading
deepchem/utils/molecule_feature_utils.py +56 −0 Original line number Diff line number Diff line Loading @@ -35,6 +35,8 @@ DEFAULT_RING_SIZE_SET = [3, 4, 5, 6, 7, 8] DEFAULT_BOND_TYPE_SET = ["SINGLE", "DOUBLE", "TRIPLE", "AROMATIC"] DEFAULT_BOND_STEREO_SET = ["STEREONONE", "STEREOANY", "STEREOZ", "STEREOE"] DEFAULT_GRAPH_DISTANCE_SET = [1, 2, 3, 4, 5, 6, 7] DEFAULT_ATOM_IMPLICIT_VALENCE_SET = [0, 1, 2, 3, 4, 5, 6] DEFAULT_ATOM_EXPLICIT_VALENCE_SET = [1, 2, 3, 4, 5, 6] class _ChemicalFeaturesFactory: Loading Loading @@ -356,6 +358,60 @@ def get_atom_total_degree_one_hot( include_unknown_set) def get_atom_implicit_valence_one_hot( atom: RDKitAtom, allowable_set: List[int] = DEFAULT_ATOM_IMPLICIT_VALENCE_SET, include_unknown_set: bool = True) -> List[float]: """Get an one-hot feature of implicit valence of an atom. Parameters --------- atom: rdkit.Chem.rdchem.Atom RDKit atom object allowable_set: List[int] Atom implicit valence to consider. The default set is `[0, 1, ..., 6]` include_unknown_set: bool, default True If true, the index of all types not in `allowable_set` is `len(allowable_set)`. Returns ------- List[float] A one-hot vector of implicit valence an atom has. If `include_unknown_set` is False, the length is `len(allowable_set)`. If `include_unknown_set` is True, the length is `len(allowable_set) + 1`. """ return one_hot_encode(atom.GetImplicitValence(), allowable_set, include_unknown_set) def get_atom_explicit_valence_one_hot( atom: RDKitAtom, allowable_set: List[int] = DEFAULT_ATOM_EXPLICIT_VALENCE_SET, include_unknown_set: bool = True) -> List[float]: """Get an one-hot feature of explicit valence of an atom. Parameters --------- atom: rdkit.Chem.rdchem.Atom RDKit atom object allowable_set: List[int] Atom explicit valence to consider. The default set is `[1, ..., 6]` include_unknown_set: bool, default True If true, the index of all types not in `allowable_set` is `len(allowable_set)`. Returns ------- List[float] A one-hot vector of explicit valence an atom has. If `include_unknown_set` is False, the length is `len(allowable_set)`. If `include_unknown_set` is True, the length is `len(allowable_set) + 1`. """ return one_hot_encode(atom.GetExplicitValence(), allowable_set, include_unknown_set) ################################################################# # bond (edge) featurization ################################################################# Loading
deepchem/utils/rdkit_utils.py +68 −0 Original line number Diff line number Diff line Loading @@ -626,3 +626,71 @@ def compute_ring_normal(mol, ring_indices): v2 = points[2] - points[0] normal = np.cross(v1, v2) return normal def compute_all_pairs_shortest_path( mol) -> Dict[Tuple[int, int], Tuple[int, int]]: """Computes the All pair shortest between every pair of nodes in terms of Rdkit Atom indexes. Parameters: ----------- mol: rdkit.rdchem.Mol Molecule containing a ring Returns: -------- paths_dict: Dict representing every atom-atom pair as key in Rdkit index and value as the shortest path between each atom pair in terms of Atom index. """ try: from rdkit import Chem except: raise ImportError("This class requires RDkit installed") n_atoms = mol.GetNumAtoms() paths_dict = {(i, j): Chem.rdmolops.GetShortestPath(mol, i, j) for i in range(n_atoms) for j in range(n_atoms) if i < j} return paths_dict def compute_pairwise_ring_info(mol): """ Computes all atom-atom pair belong to same ring with its ring size and its aromaticity. Parameters: ----------- mol: rdkit.rdchem.Mol Molecule containing a ring Returns: -------- rings_dict: Key consisting of all node-node pair sharing the same ring and value as a tuple of size of ring and its aromaticity. """ try: from rdkit import Chem except: raise ImportError("This class requires RDkit installed") rings_dict = {} def ordered_pair(a, b): return (a, b) if a < b else (b, a) ssr = [list(x) for x in Chem.GetSymmSSSR(mol)] for ring in ssr: ring_sz = len(ring) is_aromatic = True for atom_idx in ring: if not mol.GetAtoms()[atom_idx].GetIsAromatic(): is_aromatic = False break for ring_idx, atom_idx in enumerate(ring): for other_idx in ring[ring_idx:]: atom_pair = ordered_pair(atom_idx, other_idx) if atom_pair not in rings_dict: rings_dict[atom_pair] = [(ring_sz, is_aromatic)] else: if (ring_sz, is_aromatic) not in rings_dict[atom_pair]: rings_dict[atom_pair].append((ring_sz, is_aromatic)) return rings_dict
deepchem/utils/test/test_molecule_feature_utils.py +22 −0 Original line number Diff line number Diff line Loading @@ -11,6 +11,8 @@ from deepchem.utils.molecule_feature_utils import get_atom_chirality_one_hot from deepchem.utils.molecule_feature_utils import get_atom_formal_charge from deepchem.utils.molecule_feature_utils import get_atom_partial_charge from deepchem.utils.molecule_feature_utils import get_atom_total_degree_one_hot from deepchem.utils.molecule_feature_utils import get_atom_implicit_valence_one_hot from deepchem.utils.molecule_feature_utils import get_atom_explicit_valence_one_hot from deepchem.utils.molecule_feature_utils import get_bond_type_one_hot from deepchem.utils.molecule_feature_utils import get_bond_is_in_same_ring_one_hot from deepchem.utils.molecule_feature_utils import get_bond_is_conjugated_one_hot Loading Loading @@ -140,6 +142,26 @@ class TestGraphConvUtils(unittest.TestCase): one_hot = get_atom_total_degree_one_hot(atoms[3]) assert one_hot == [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0] def test_get_atom_implicit_valence_one_hot(self): atoms = self.mol.GetAtoms() assert atoms[0].GetSymbol() == "C" one_hot = get_atom_implicit_valence_one_hot(atoms[0]) assert one_hot == [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0] assert atoms[3].GetSymbol() == "O" one_hot = get_atom_implicit_valence_one_hot(atoms[3]) assert one_hot == [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] def test_get_atom_explicit_valence_one_hot(self): atoms = self.mol.GetAtoms() assert atoms[0].GetSymbol() == "C" one_hot = get_atom_explicit_valence_one_hot(atoms[0]) assert one_hot == [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] assert atoms[3].GetSymbol() == "O" one_hot = get_atom_explicit_valence_one_hot(atoms[3]) assert one_hot == [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0] def test_get_bond_type_one_hot(self): bonds = self.mol.GetBonds() one_hot = get_bond_type_one_hot(bonds[0]) Loading
deepchem/utils/test/test_rdkit_utils.py +25 −0 Original line number Diff line number Diff line Loading @@ -162,3 +162,28 @@ class TestRdkitUtil(unittest.TestCase): def test_strip_hydrogens(self): pass def test_all_shortest_pairs(self): from rdkit import Chem mol = Chem.MolFromSmiles("CN=C=O") valid_dict = { (0, 1): (0, 1), (0, 2): (0, 1, 2), (0, 3): (0, 1, 2, 3), (1, 2): (1, 2), (1, 3): (1, 2, 3), (2, 3): (2, 3) } assert rdkit_utils.compute_all_pairs_shortest_path(mol) == valid_dict def test_pairwise_ring_info(self): from rdkit import Chem mol = Chem.MolFromSmiles("c1ccccc1") predict_dict = rdkit_utils.compute_pairwise_ring_info(mol) assert all(pair == [(6, True)] for pair in predict_dict.values()) mol = Chem.MolFromSmiles("c1c2ccccc2ccc1") predict_dict = rdkit_utils.compute_pairwise_ring_info(mol) assert all(pair == [(6, True)] for pair in predict_dict.values()) mol = Chem.MolFromSmiles("CN=C=O") predict_dict = rdkit_utils.compute_pairwise_ring_info(mol) assert not predict_dict
