Loading deepchem/feat/molecule_featurizers/mol_graph_conv_featurizer.py +123 −29 Original line number Diff line number Diff line Loading @@ -226,8 +226,72 @@ class MolGraphConvFeaturizer(MolecularFeaturizer): class PagtnMolGraphFeaturizer(MolecularFeaturizer): """This class is a featuriser of PAGTN graph networks for molecules. The featurization is based on `PAGTN model <https://arxiv.org/abs/1905.12712>`_. It is slightly more computationally intensive than default Graph Convolution Featuriser, but it builds a Molecular Graph connecting all tom pairs accounting for interactions of atom with every other atom in the Molecule. According to the paper, interactions between two pairs of an atom are dependent on the relative distance between them and calculating the shortest path between them. The default node representation is constructed by concatenating the following values, and the feature length is 94. - Atom type: One hot encoding of the atom type. It consists of the most possible elements in a chemical compound. - Formal charge: One hot encoding of formal charge of the atom. - Degree: One hot encoding of the atom degree - Explicit Valence: One hot encoding of explicit valence of an atom. The supported possibilities include ``0 - 6``. - Implicit Valence: One hot encoding of implicit valence of an atom. The supported possibilities include ``0 - 5``. - Aromaticity: Boolean representing if an atom is aromatic. The default edge representation are constructed by concatenating the following values, and the feature length is 42. It builds a complete graph where each node is connected to every other node. The edge representations are calculated the shortest path between two nodes (choose any one if multiple exist). Each bond encountered in the shortest path is used to calculate edge features. - Bond type: A one-hot vector of the bond type, "single", "double", "triple", or "aromatic". - Conjugated: A one-hot vector of whether this bond is conjugated or not. - Same ring: A one-hot vector of whether the atoms in the pair are in the same ring. - Ring Size and Aromaticity: One hot encoding of atoms in pair based on ring size and aromaticity. - Distance: One hot encoding of the distance between pair of atoms. Examples -------- >>> from deepchem.feat import PagtnMolGraphFeaturizer >>> smiles = ["C1CCC1", "C1=CC=CN=C1"] >>> featurizer = PagtnMolGraphFeaturizer(max_length=5) >>> out = featurizer.featurize(smiles) >>> type(out[0]) <class 'deepchem.feat.graph_data.GraphData'> >>> out[0].num_node_features 94 >>> out[0].num_edge_features 42 References ---------- .. [1] Chen, Barzilay, Jaakkola "Path-Augmented Graph Transformer Network" 10.26434/chemrxiv.8214422. Note ---- This class requires RDKit to be installed. """ def __init__(self, max_length=5): """ Parameters ---------- max_length : int Maximum distance up to which shortest paths must be considered. Paths shorter than max_length will be padded and longer will be truncated, default to ``5``. """ self.SYMBOLS = [ 'C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca', 'Fe', Loading @@ -242,7 +306,19 @@ class PagtnMolGraphFeaturizer(MolecularFeaturizer): self.ordered_pair = lambda a, b: (a, b) if a < b else (b, a) self.max_length = max_length def PagtnAtomFeaturizer(self, atom): def _pagtn_atom_featurizer(self, atom: RDKitAtom) -> np.ndarray: """Calculate Atom features from RDKit atom object. Parameters ---------- mol: rdkit.Chem.rdchem.Mol RDKit mol object. Returns ------- atom_feat: np.ndarray numpy vector of atom features. """ atom_type = get_atom_type_one_hot(atom, self.SYMBOLS, False) formal_charge = get_atom_formal_charge_one_hot( atom, include_unknown_set=False) Loading @@ -255,11 +331,13 @@ class PagtnMolGraphFeaturizer(MolecularFeaturizer): ]) return atom_feat def _bond_features(self, mol, path_atoms, ring_info): def _edge_features(self, mol: RDKitMol, path_atoms: Tuple[int, ...], ring_info) -> np.ndarray: """Computes the edge features for a given pair of nodes. Parameters ---------- mol : rdkit.Chem.rdchem.Mol mol : : RDKitMol RDKit molecule instance. path_atoms: tuple Shortest path between the given pair of nodes. Loading Loading @@ -295,27 +373,31 @@ class PagtnMolGraphFeaturizer(MolecularFeaturizer): rfeat = [ one_hot_encode(r, allowable_set=self.RING_TYPES) for r in ring_info ] rfeat = [True] + np.any(rfeat, axis=0).tolist() # The 1.0 float value represents True Boolean rfeat = [1.0] + np.any(rfeat, axis=0).tolist() features.append(rfeat) else: # This will return a boolean vector with all entries False features.append([False] + features.append([0.0] + one_hot_encode(ring_info, allowable_set=self.RING_TYPES)) return np.concatenate(features, axis=0) def PagtnBondFeaturizer(self, mol): """Featurizes the input molecule. def _pagtn_edge_featurizer(self, mol: RDKitMol) -> Tuple[np.ndarray, np.ndarray]: """Calculate bond features from RDKit mol object. Parameters ---------- mol: rdkit.Chem.rdchem.Mol RDKit molecule instance. RDKit mol object. Returns ------- dict Mapping _edge_data_field to a float32 tensor of shape (N, M), where N is the number of atom pairs and M is the feature size depending on max_length. np.ndarray Source and Destination node indexes of each bond. np.ndarray numpy vector of bond features. """ n_atoms = mol.GetNumAtoms() # To get the shortest paths between two nodes. paths_dict = compute_all_pairs_shortest_path(mol) Loading @@ -334,14 +416,26 @@ class PagtnMolGraphFeaturizer(MolecularFeaturizer): feats.append(np.zeros(7 * self.max_length + 7)) continue ring_info = rings_dict.get(self.ordered_pair(i, j), []) feats.append(self._bond_features(mol, paths_dict[(i, j)], ring_info)) feats.append(self._edge_features(mol, paths_dict[(i, j)], ring_info)) return np.array([src, dest], dtype=np.int), np.array(feats, dtype=np.float) def _featurize(self, mol): def _featurize(self, mol: RDKitMol) -> GraphData: """Calculate molecule graph features from RDKit mol object. Parameters ---------- mol: rdkit.Chem.rdchem.Mol RDKit mol object. Returns ------- graph: GraphData A molecule graph with some features. """ node_features = np.asarray( [self.PagtnAtomFeaturizer(atom) for atom in mol.GetAtoms()], [self._pagtn_atom_featurizer(atom) for atom in mol.GetAtoms()], dtype=np.float) edge_index, edge_features = self.PagtnBondFeaturizer(mol) edge_index, edge_features = self._pagtn_edge_featurizer(mol) graph = GraphData(node_features, edge_index, edge_features) return graph docs/source/api_reference/featurizers.rst +7 −0 Original line number Diff line number Diff line Loading @@ -74,6 +74,13 @@ MolGraphConvFeaturizer :members: :inherited-members: PagtnMolGraphFeaturizer ********************** .. autoclass:: deepchem.feat.PagtnMolGraphFeaturizer :members: :inherited-members: Utilities ********* Loading Loading
deepchem/feat/molecule_featurizers/mol_graph_conv_featurizer.py +123 −29 Original line number Diff line number Diff line Loading @@ -226,8 +226,72 @@ class MolGraphConvFeaturizer(MolecularFeaturizer): class PagtnMolGraphFeaturizer(MolecularFeaturizer): """This class is a featuriser of PAGTN graph networks for molecules. The featurization is based on `PAGTN model <https://arxiv.org/abs/1905.12712>`_. It is slightly more computationally intensive than default Graph Convolution Featuriser, but it builds a Molecular Graph connecting all tom pairs accounting for interactions of atom with every other atom in the Molecule. According to the paper, interactions between two pairs of an atom are dependent on the relative distance between them and calculating the shortest path between them. The default node representation is constructed by concatenating the following values, and the feature length is 94. - Atom type: One hot encoding of the atom type. It consists of the most possible elements in a chemical compound. - Formal charge: One hot encoding of formal charge of the atom. - Degree: One hot encoding of the atom degree - Explicit Valence: One hot encoding of explicit valence of an atom. The supported possibilities include ``0 - 6``. - Implicit Valence: One hot encoding of implicit valence of an atom. The supported possibilities include ``0 - 5``. - Aromaticity: Boolean representing if an atom is aromatic. The default edge representation are constructed by concatenating the following values, and the feature length is 42. It builds a complete graph where each node is connected to every other node. The edge representations are calculated the shortest path between two nodes (choose any one if multiple exist). Each bond encountered in the shortest path is used to calculate edge features. - Bond type: A one-hot vector of the bond type, "single", "double", "triple", or "aromatic". - Conjugated: A one-hot vector of whether this bond is conjugated or not. - Same ring: A one-hot vector of whether the atoms in the pair are in the same ring. - Ring Size and Aromaticity: One hot encoding of atoms in pair based on ring size and aromaticity. - Distance: One hot encoding of the distance between pair of atoms. Examples -------- >>> from deepchem.feat import PagtnMolGraphFeaturizer >>> smiles = ["C1CCC1", "C1=CC=CN=C1"] >>> featurizer = PagtnMolGraphFeaturizer(max_length=5) >>> out = featurizer.featurize(smiles) >>> type(out[0]) <class 'deepchem.feat.graph_data.GraphData'> >>> out[0].num_node_features 94 >>> out[0].num_edge_features 42 References ---------- .. [1] Chen, Barzilay, Jaakkola "Path-Augmented Graph Transformer Network" 10.26434/chemrxiv.8214422. Note ---- This class requires RDKit to be installed. """ def __init__(self, max_length=5): """ Parameters ---------- max_length : int Maximum distance up to which shortest paths must be considered. Paths shorter than max_length will be padded and longer will be truncated, default to ``5``. """ self.SYMBOLS = [ 'C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca', 'Fe', Loading @@ -242,7 +306,19 @@ class PagtnMolGraphFeaturizer(MolecularFeaturizer): self.ordered_pair = lambda a, b: (a, b) if a < b else (b, a) self.max_length = max_length def PagtnAtomFeaturizer(self, atom): def _pagtn_atom_featurizer(self, atom: RDKitAtom) -> np.ndarray: """Calculate Atom features from RDKit atom object. Parameters ---------- mol: rdkit.Chem.rdchem.Mol RDKit mol object. Returns ------- atom_feat: np.ndarray numpy vector of atom features. """ atom_type = get_atom_type_one_hot(atom, self.SYMBOLS, False) formal_charge = get_atom_formal_charge_one_hot( atom, include_unknown_set=False) Loading @@ -255,11 +331,13 @@ class PagtnMolGraphFeaturizer(MolecularFeaturizer): ]) return atom_feat def _bond_features(self, mol, path_atoms, ring_info): def _edge_features(self, mol: RDKitMol, path_atoms: Tuple[int, ...], ring_info) -> np.ndarray: """Computes the edge features for a given pair of nodes. Parameters ---------- mol : rdkit.Chem.rdchem.Mol mol : : RDKitMol RDKit molecule instance. path_atoms: tuple Shortest path between the given pair of nodes. Loading Loading @@ -295,27 +373,31 @@ class PagtnMolGraphFeaturizer(MolecularFeaturizer): rfeat = [ one_hot_encode(r, allowable_set=self.RING_TYPES) for r in ring_info ] rfeat = [True] + np.any(rfeat, axis=0).tolist() # The 1.0 float value represents True Boolean rfeat = [1.0] + np.any(rfeat, axis=0).tolist() features.append(rfeat) else: # This will return a boolean vector with all entries False features.append([False] + features.append([0.0] + one_hot_encode(ring_info, allowable_set=self.RING_TYPES)) return np.concatenate(features, axis=0) def PagtnBondFeaturizer(self, mol): """Featurizes the input molecule. def _pagtn_edge_featurizer(self, mol: RDKitMol) -> Tuple[np.ndarray, np.ndarray]: """Calculate bond features from RDKit mol object. Parameters ---------- mol: rdkit.Chem.rdchem.Mol RDKit molecule instance. RDKit mol object. Returns ------- dict Mapping _edge_data_field to a float32 tensor of shape (N, M), where N is the number of atom pairs and M is the feature size depending on max_length. np.ndarray Source and Destination node indexes of each bond. np.ndarray numpy vector of bond features. """ n_atoms = mol.GetNumAtoms() # To get the shortest paths between two nodes. paths_dict = compute_all_pairs_shortest_path(mol) Loading @@ -334,14 +416,26 @@ class PagtnMolGraphFeaturizer(MolecularFeaturizer): feats.append(np.zeros(7 * self.max_length + 7)) continue ring_info = rings_dict.get(self.ordered_pair(i, j), []) feats.append(self._bond_features(mol, paths_dict[(i, j)], ring_info)) feats.append(self._edge_features(mol, paths_dict[(i, j)], ring_info)) return np.array([src, dest], dtype=np.int), np.array(feats, dtype=np.float) def _featurize(self, mol): def _featurize(self, mol: RDKitMol) -> GraphData: """Calculate molecule graph features from RDKit mol object. Parameters ---------- mol: rdkit.Chem.rdchem.Mol RDKit mol object. Returns ------- graph: GraphData A molecule graph with some features. """ node_features = np.asarray( [self.PagtnAtomFeaturizer(atom) for atom in mol.GetAtoms()], [self._pagtn_atom_featurizer(atom) for atom in mol.GetAtoms()], dtype=np.float) edge_index, edge_features = self.PagtnBondFeaturizer(mol) edge_index, edge_features = self._pagtn_edge_featurizer(mol) graph = GraphData(node_features, edge_index, edge_features) return graph
docs/source/api_reference/featurizers.rst +7 −0 Original line number Diff line number Diff line Loading @@ -74,6 +74,13 @@ MolGraphConvFeaturizer :members: :inherited-members: PagtnMolGraphFeaturizer ********************** .. autoclass:: deepchem.feat.PagtnMolGraphFeaturizer :members: :inherited-members: Utilities ********* Loading
