Delete deepscreen/data/featurizers/monn.py

deepscreen/data/featurizers/monn.py

@@ -1,106 +0,0 @@
-import numpy as np
-from rdkit.Chem import MolFromSmiles
-
-from deepscreen.data.featurizers.categorical import FASTA_VOCAB, fasta_to_label
-from deepscreen.data.featurizers.graph import atom_features, bond_features
-
-
-def get_mask(arr):
-    a = np.zeros(1, len(arr))
-    a[1, :arr.shape[0]] = 1
-    return a
-
-
-def add_index(input_array, ebd_size):
-    batch_size, n_vertex, n_nbs = np.shape(input_array)
-    add_idx = np.array(range(0, ebd_size * batch_size, ebd_size) * (n_nbs * n_vertex))
-    add_idx = np.transpose(add_idx.reshape(-1, batch_size))
-    add_idx = add_idx.reshape(-1)
-    new_array = input_array.reshape(-1) + add_idx
-    return new_array
-
-
-# TODO fix padding and masking
-def drug_featurizer(smiles, max_neighbors=6):
-    mol = MolFromSmiles(smiles)
-
-    # convert molecule to GNN input
-    n_atoms = mol.GetNumAtoms()
-    assert mol.GetNumBonds() >= 0
-
-    n_bonds = max(mol.GetNumBonds(), 1)
-    feat_atoms = np.zeros((n_atoms,))  # atom feature ID
-    feat_bonds = np.zeros((n_bonds,))  # bond feature ID
-    atom_adj = np.zeros((n_atoms, max_neighbors))
-    bond_adj = np.zeros((n_atoms, max_neighbors))
-    n_neighbors = np.zeros((n_atoms,))
-    neighbor_mask = np.zeros((n_atoms, max_neighbors))
-
-    for atom in mol.GetAtoms():
-        idx = atom.GetIdx()
-        feat_atoms[idx] = atom_features(atom)
-
-    for bond in mol.GetBonds():
-        a1 = bond.GetBeginAtom().GetIdx()
-        a2 = bond.GetEndAtom().GetIdx()
-        idx = bond.GetIdx()
-        feat_bonds[idx] = bond_features(bond)
-        try:
-            atom_adj[a1, n_neighbors[a1]] = a2
-            atom_adj[a2, n_neighbors[a2]] = a1
-        except:
-            return [], [], [], [], []
-        bond_adj[a1, n_neighbors[a1]] = idx
-        bond_adj[a2, n_neighbors[a2]] = idx
-        n_neighbors[a1] += 1
-        n_neighbors[a2] += 1
-
-    for i in range(len(n_neighbors)):
-        neighbor_mask[i, :n_neighbors[i]] = 1
-
-    vertex_mask = get_mask(feat_atoms)
-    # vertex = pack_1d(feat_atoms)
-    # edge = pack_1d(feat_bonds)
-    # atom_adj = pack_2d(atom_adj)
-    # bond_adj = pack_2d(bond_adj)
-    # nbs_mask = pack_2d(n_neighbors_mat)
-
-    atom_adj = add_index(atom_adj, np.shape(atom_adj)[1])
-    bond_adj = add_index(bond_adj, np.shape(feat_bonds)[1])
-
-    return vertex_mask, feat_atoms, feat_bonds, atom_adj, bond_adj, neighbor_mask
-
-
-# TODO WIP the pairwise_label matrix probably should be generated beforehand and stored as an extra label in the dataset
-def get_pairwise_label(pdbid, interaction_dict, mol):
-    if pdbid in interaction_dict:
-        sdf_element = np.array([atom.GetSymbol().upper() for atom in mol.GetAtoms()])
-        atom_element = np.array(interaction_dict[pdbid]['atom_element'], dtype=str)
-        atom_name_list = np.array(interaction_dict[pdbid]['atom_name'], dtype=str)
-        atom_interact = np.array(interaction_dict[pdbid]['atom_interact'], dtype=int)
-        nonH_position = np.where(atom_element != 'H')[0]
-        assert sum(atom_element[nonH_position] != sdf_element) == 0
-
-        atom_name_list = atom_name_list[nonH_position].tolist()
-        pairwise_mat = np.zeros((len(nonH_position), len(interaction_dict[pdbid]['uniprot_seq'])), dtype=np.int32)
-        for atom_name, bond_type in interaction_dict[pdbid]['atom_bond_type']:
-            atom_idx = atom_name_list.index(str(atom_name))
-            assert atom_idx < len(nonH_position)
-
-            seq_idx_list = []
-            for seq_idx, bond_type_seq in interaction_dict[pdbid]['residue_bond_type']:
-                if bond_type == bond_type_seq:
-                    seq_idx_list.append(seq_idx)
-                    pairwise_mat[atom_idx, seq_idx] = 1
-        if len(np.where(pairwise_mat != 0)[0]) != 0:
-            pairwise_mask = True
-            return True, pairwise_mat
-    return False, np.zeros((1, 1))
-
-
-def protein_featurizer(fasta):
-    sequence = fasta_to_label(fasta)
-    # pad proteins and make masks
-    seq_mask = get_mask(sequence)
-
-    return seq_mask, sequence