Loading deepchem/feat/rdkit_grid_featurizer.py +2 −2 Original line number Diff line number Diff line Loading @@ -14,8 +14,8 @@ from collections import Counter from deepchem.feat.fingerprints import CircularFingerprint from deepchem.feat.contact_fingerprints import ContactCircularFingerprint from deepchem.feat.contact_fingerprints import ContactCircularVoxelizer from deepchem.feat.splif_fingerprint import SplifFingerprint from deepchem.feat.splif_fingerprint import SplifVoxelizer from deepchem.feat.splif_fingerprints import SplifFingerprint from deepchem.feat.splif_fingerprints import SplifVoxelizer from deepchem.feat.grid_featurizers import compute_charge_dictionary from deepchem.feat.grid_featurizers import ChargeVoxelizer from deepchem.feat.grid_featurizers import SaltBridgeVoxelizer Loading deepchem/feat/splif_fingerprint.py→deepchem/feat/splif_fingerprints.py +55 −24 Original line number Diff line number Diff line Loading @@ -43,20 +43,37 @@ def compute_splif_features_in_range(protein, } return (splif_dict) def featurize_splif(protein_xyz, protein, ligand_xyz, ligand, contact_bins, def featurize_splif(frag1, frag1, contact_bins, pairwise_distances, ecfp_degree): """Computes SPLIF featurization of protein-ligand binding pocket. """Computes SPLIF featurization of fragment interactions binding pocket. For each contact range (i.e. 1 A to 2 A, 2 A to 3 A, etc.) compute a dictionary mapping (protein_index_i, ligand_index_j) tuples --> (protein_ecfp_i, ligand_ecfp_j) tuples. Return a compute a dictionary mapping (frag1_index_i, frag2_index_j) tuples --> (frag1_ecfp_i, frag2_ecfp_j) tuples. Return a list of such splif dictionaries. Parameters ---------- frag1: Tuple A tuple of (coords, mol) returned by `rdkit_util.load_molecule`. frag2: Tuple A tuple of (coords, mol) returned by `rdkit_util.load_molecule`. contact_bins: np.ndarray TODO pairwise_distances: np.ndarray Array of pairwise fragment-fragment distances (Angstroms) ecfp_degree: int ECFP radius Returns ------- Dictionaries of SPLIF interactions suitable for `vectorize` or `voxelize`. """ splif_dicts = [] for i, contact_bin in enumerate(contact_bins): splif_dicts.append( compute_splif_features_in_range(protein, ligand, pairwise_distances, compute_splif_features_in_range(frag1, frag2, pairwise_distances, contact_bin, ecfp_degree)) return (splif_dicts) Loading Loading @@ -190,7 +207,7 @@ class SplifVoxelizer(ComplexFeaturizer): self.voxel_width = voxel_width self.voxels_per_edge = int(self.box_width / self.voxel_width) def _featurize_complex(self, mol, protein): def _featurize_complex(self, molecular_complex): """ Compute featurization for a single mol/protein complex Loading @@ -204,21 +221,35 @@ class SplifVoxelizer(ComplexFeaturizer): Parameters ---------- mol: object Representation of the molecule protein: object Representation of the protein molecular_complex: Object A representation of a molecular complex, produced by `rdkit_util.load_complex`. """ (lig_xyz, lig_rdk), (prot_xyz, prot_rdk) = mol, protein distances = compute_pairwise_distances(prot_xyz, lig_xyz) return [ try: fragments = rdkit_util.load_complex(molecular_complex, add_hydrogens=False) except MoleculeLoadException: logger.warning("This molecule cannot be loaded by Rdkit. Returning None") return None pairwise_features = [] # We compute pairwise contact fingerprints centroid = compute_contact_centroid(fragments, cutoff=self.cutoff) for (frag1, frag2) in itertools.combinations(fragments, 2): distances = compute_pairwise_distances(frag1[0], frag2[0]) frag1_xyz = subtract_centroid(frag1[0], centroid) frag2_xyz = subtract_centroid(frag2[0], centroid) xyzs = [frag1_xyz, frag2_xyz] #(lig_xyz, lig_rdk), (prot_xyz, prot_rdk) = mol, protein #distances = compute_pairwise_distances(prot_xyz, lig_xyz) pairwise_features.append(np.concatenate([ voxelize( convert_atom_pair_to_voxel, self.voxels_per_edge, self.box_width, self.voxel_width, hash_ecfp_pair, (prot_xyz, lig_xyz), hash_ecfp_pair, xyzs, feature_dict=splif_dict, nb_channel=self.size) for splif_dict in featurize_splif( prot_xyz, prot_rdk, lig_xyz, lig_rdk, self.contact_bins, distances, self.radius) ] ], axis=-1)) # Features are of shape (voxels_per_edge, voxels_per_edge, voxels_per_edge, 1) so we should concatenate on the last axis. return np.concatenate(pairwise_features, axis=-1) deepchem/feat/tests/test_splif_fingerprints.py 0 → 100644 +14 −0 Original line number Diff line number Diff line import unittest import deepchem as dc class TestSplifFingerprints(unittest.TestCase): """Test Splif Fingerprint and Voxelizer.""" def setUp(self): # TODO test more formats for ligand current_dir = os.path.dirname(os.path.realpath(__file__)) self.protein_file = os.path.join(current_dir, '3ws9_protein_fixer_rdkit.pdb') self.ligand_file = os.path.join(current_dir, '3ws9_ligand.sdf') self.complex_files = [(self.protein_file, self.ligand_file)] Loading
deepchem/feat/rdkit_grid_featurizer.py +2 −2 Original line number Diff line number Diff line Loading @@ -14,8 +14,8 @@ from collections import Counter from deepchem.feat.fingerprints import CircularFingerprint from deepchem.feat.contact_fingerprints import ContactCircularFingerprint from deepchem.feat.contact_fingerprints import ContactCircularVoxelizer from deepchem.feat.splif_fingerprint import SplifFingerprint from deepchem.feat.splif_fingerprint import SplifVoxelizer from deepchem.feat.splif_fingerprints import SplifFingerprint from deepchem.feat.splif_fingerprints import SplifVoxelizer from deepchem.feat.grid_featurizers import compute_charge_dictionary from deepchem.feat.grid_featurizers import ChargeVoxelizer from deepchem.feat.grid_featurizers import SaltBridgeVoxelizer Loading
deepchem/feat/splif_fingerprint.py→deepchem/feat/splif_fingerprints.py +55 −24 Original line number Diff line number Diff line Loading @@ -43,20 +43,37 @@ def compute_splif_features_in_range(protein, } return (splif_dict) def featurize_splif(protein_xyz, protein, ligand_xyz, ligand, contact_bins, def featurize_splif(frag1, frag1, contact_bins, pairwise_distances, ecfp_degree): """Computes SPLIF featurization of protein-ligand binding pocket. """Computes SPLIF featurization of fragment interactions binding pocket. For each contact range (i.e. 1 A to 2 A, 2 A to 3 A, etc.) compute a dictionary mapping (protein_index_i, ligand_index_j) tuples --> (protein_ecfp_i, ligand_ecfp_j) tuples. Return a compute a dictionary mapping (frag1_index_i, frag2_index_j) tuples --> (frag1_ecfp_i, frag2_ecfp_j) tuples. Return a list of such splif dictionaries. Parameters ---------- frag1: Tuple A tuple of (coords, mol) returned by `rdkit_util.load_molecule`. frag2: Tuple A tuple of (coords, mol) returned by `rdkit_util.load_molecule`. contact_bins: np.ndarray TODO pairwise_distances: np.ndarray Array of pairwise fragment-fragment distances (Angstroms) ecfp_degree: int ECFP radius Returns ------- Dictionaries of SPLIF interactions suitable for `vectorize` or `voxelize`. """ splif_dicts = [] for i, contact_bin in enumerate(contact_bins): splif_dicts.append( compute_splif_features_in_range(protein, ligand, pairwise_distances, compute_splif_features_in_range(frag1, frag2, pairwise_distances, contact_bin, ecfp_degree)) return (splif_dicts) Loading Loading @@ -190,7 +207,7 @@ class SplifVoxelizer(ComplexFeaturizer): self.voxel_width = voxel_width self.voxels_per_edge = int(self.box_width / self.voxel_width) def _featurize_complex(self, mol, protein): def _featurize_complex(self, molecular_complex): """ Compute featurization for a single mol/protein complex Loading @@ -204,21 +221,35 @@ class SplifVoxelizer(ComplexFeaturizer): Parameters ---------- mol: object Representation of the molecule protein: object Representation of the protein molecular_complex: Object A representation of a molecular complex, produced by `rdkit_util.load_complex`. """ (lig_xyz, lig_rdk), (prot_xyz, prot_rdk) = mol, protein distances = compute_pairwise_distances(prot_xyz, lig_xyz) return [ try: fragments = rdkit_util.load_complex(molecular_complex, add_hydrogens=False) except MoleculeLoadException: logger.warning("This molecule cannot be loaded by Rdkit. Returning None") return None pairwise_features = [] # We compute pairwise contact fingerprints centroid = compute_contact_centroid(fragments, cutoff=self.cutoff) for (frag1, frag2) in itertools.combinations(fragments, 2): distances = compute_pairwise_distances(frag1[0], frag2[0]) frag1_xyz = subtract_centroid(frag1[0], centroid) frag2_xyz = subtract_centroid(frag2[0], centroid) xyzs = [frag1_xyz, frag2_xyz] #(lig_xyz, lig_rdk), (prot_xyz, prot_rdk) = mol, protein #distances = compute_pairwise_distances(prot_xyz, lig_xyz) pairwise_features.append(np.concatenate([ voxelize( convert_atom_pair_to_voxel, self.voxels_per_edge, self.box_width, self.voxel_width, hash_ecfp_pair, (prot_xyz, lig_xyz), hash_ecfp_pair, xyzs, feature_dict=splif_dict, nb_channel=self.size) for splif_dict in featurize_splif( prot_xyz, prot_rdk, lig_xyz, lig_rdk, self.contact_bins, distances, self.radius) ] ], axis=-1)) # Features are of shape (voxels_per_edge, voxels_per_edge, voxels_per_edge, 1) so we should concatenate on the last axis. return np.concatenate(pairwise_features, axis=-1)
deepchem/feat/tests/test_splif_fingerprints.py 0 → 100644 +14 −0 Original line number Diff line number Diff line import unittest import deepchem as dc class TestSplifFingerprints(unittest.TestCase): """Test Splif Fingerprint and Voxelizer.""" def setUp(self): # TODO test more formats for ligand current_dir = os.path.dirname(os.path.realpath(__file__)) self.protein_file = os.path.join(current_dir, '3ws9_protein_fixer_rdkit.pdb') self.ligand_file = os.path.join(current_dir, '3ws9_ligand.sdf') self.complex_files = [(self.protein_file, self.ligand_file)]
