Loading deepchem/feat/contact_fingerprints.py +2 −2 Original line number Diff line number Diff line Loading @@ -32,7 +32,7 @@ def featurize_contacts_ecfp(frag1, frag2: Tuple A tuple of (coords, mol) returned by `rdkit_util.load_molecule`. pairwise_distances: np.ndarray Array of pairwise protein-ligand distances (Angstroms) Array of pairwise fragment-fragment distances (Angstroms) cutoff: float Cutoff distance for contact consideration ecfp_degree: int Loading Loading @@ -90,7 +90,7 @@ class ContactCircularFingerprint(ComplexFeaturizer): def _featurize_complex(self, molecular_complex): """ Compute featurization for a single mol/protein complex Compute featurization for a molecular complex Parameters ---------- Loading deepchem/feat/splif_fingerprints.py +65 −39 Original line number Diff line number Diff line """ SPLIF Fingeprints for protein-ligand complexes. SPLIF Fingerprints for molecular complexes. """ import logging import numpy as np Loading @@ -16,29 +16,42 @@ logger = logging.getLogger(__name__) SPLIF_CONTACT_BINS = [(0, 2.0), (2.0, 3.0), (3.0, 4.5)] def compute_splif_features_in_range(protein, ligand, def compute_splif_features_in_range(frag1, frag2, pairwise_distances, contact_bin, ecfp_degree=2): """Computes SPLIF features for protein atoms close to ligand atoms. """Computes SPLIF features for close atoms in molecular complexes. Finds all protein atoms that are > contact_bin[0] and < contact_bin[1] away from ligand atoms. Then, finds the ECFP Finds all frag1 atoms that are > contact_bin[0] and < contact_bin[1] away from frag2 atoms. Then, finds the ECFP fingerprints for the contacting atoms. Returns a dictionary mapping (protein_index_i, ligand_index_j) --> (protein_ecfp_i, ligand_ecfp_j) mapping (frag1_index_i, frag2_index_j) --> (frag1_ecfp_i, frag2_ecfp_j) 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 """ contacts = np.nonzero((pairwise_distances > contact_bin[0]) & (pairwise_distances < contact_bin[1])) protein_atoms = set([int(c) for c in contacts[0].tolist()]) frag1_atoms = set([int(c) for c in contacts[0].tolist()]) contacts = zip(contacts[0], contacts[1]) protein_ecfp_dict = compute_all_ecfp( protein, indices=protein_atoms, degree=ecfp_degree) ligand_ecfp_dict = compute_all_ecfp(ligand, degree=ecfp_degree) frag1_ecfp_dict = compute_all_ecfp( frag1, indices=frag1_atoms, degree=ecfp_degree) frag2_ecfp_dict = compute_all_ecfp(frag2, degree=ecfp_degree) splif_dict = { contact: (protein_ecfp_dict[contact[0]], ligand_ecfp_dict[contact[1]]) contact: (frag1_ecfp_dict[contact[0]], frag2_ecfp_dict[contact[1]]) for contact in contacts } return (splif_dict) Loading Loading @@ -92,10 +105,12 @@ class SplifFingerprint(ComplexFeaturizer): and modeling 54.9 (2014): 2555-2561. SPLIF fingerprints are a subclass of `ComplexFeaturizer`. It requires 3D coordinates for a protein-ligand complex. For each ligand atom, it identifies close protein atoms. These atom pairs are expanded to 2D circular fragments and a fingerprint for the union is turned on in the bit vector. requires 3D coordinates for a molecular complex. For each ligand atom, it identifies close pairs of atoms from different molecules. These atom pairs are expanded to 2D circular fragments and a fingerprint for the union is turned on in the bit vector. Note that we slightly generalize the original paper by not requiring the interacting molecules to be proteins or ligands. This is conceptually pretty similar to `ContactCircularFingerprint` but computes ECFP fragments only Loading Loading @@ -127,32 +142,36 @@ class SplifFingerprint(ComplexFeaturizer): self.size = size self.radius = radius def _featurize_complex(self, mol, protein): def _featurize_complex(self, molecular_complex): """ Compute featurization for a single mol/protein complex TODO(rbharath): This is very not ergonomic. I'd much prefer returning an vector instead of a list of two vectors. In addition, there's a question of efficiency. RdkitGridFeaturizer caches rotated versions etc internally. To make things work out of box, we are accepting that kludgey input. This needs to be cleaned up before full merge. Compute featurization for a molecular complex Parameters ---------- mol: object Representation of the molecule protein: object Representation of the protein molecular_complex: Object Some representation of a molecular 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 for (frag1, frag2) in itertools.combinations(fragments, 2): # Get coordinates distances = compute_pairwise_distances(frag1[0], frag2[0]) #(lig_xyz, lig_rdk), (prot_xyz, prot_rdk) = mol, protein #distances = compute_pairwise_distances(prot_xyz, lig_xyz) vectors = [ vectorize(hash_ecfp_pair, feature_dict=splif_dict, size=self.size) for splif_dict in featurize_splif( prot_xyz, prot_rdk, lig_xyz, lig_rdk, self.contact_bins, distances, self.radius) ] pairwse_features += vector pairwise_features = np.concatenate(pairwise_features) return pairwise_features class SplifVoxelizer(ComplexFeaturizer): """Computes SPLIF voxel grid for a macromolecular complex. Loading Loading @@ -180,7 +199,8 @@ class SplifVoxelizer(ComplexFeaturizer): radius=2, size=8, box_width=16.0, voxel_width=1.0): voxel_width=1.0, reduce_to_contacts=True): """ Parameters ---------- Loading @@ -196,6 +216,9 @@ class SplifVoxelizer(ComplexFeaturizer): is centered on a ligand centroid. voxel_width: float, optional (default 1.0) Size of a 3D voxel in a grid. reduce_to_contacts: bool, optional If True, reduce the atoms in the complex to those near a contact region. """ if contact_bins is None: self.contact_bins = SPLIF_CONTACT_BINS Loading @@ -206,6 +229,7 @@ class SplifVoxelizer(ComplexFeaturizer): self.box_width = box_width self.voxel_width = voxel_width self.voxels_per_edge = int(self.box_width / self.voxel_width) self.reduce_to_contacts = reduce_to_contacts def _featurize_complex(self, molecular_complex): """ Loading Loading @@ -234,6 +258,8 @@ class SplifVoxelizer(ComplexFeaturizer): pairwise_features = [] # We compute pairwise contact fingerprints centroid = compute_contact_centroid(fragments, cutoff=self.cutoff) if self.reduce_to_contacts: fragments = reduce_molecular_complex_to_contacts(fragments, 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) Loading Loading
deepchem/feat/contact_fingerprints.py +2 −2 Original line number Diff line number Diff line Loading @@ -32,7 +32,7 @@ def featurize_contacts_ecfp(frag1, frag2: Tuple A tuple of (coords, mol) returned by `rdkit_util.load_molecule`. pairwise_distances: np.ndarray Array of pairwise protein-ligand distances (Angstroms) Array of pairwise fragment-fragment distances (Angstroms) cutoff: float Cutoff distance for contact consideration ecfp_degree: int Loading Loading @@ -90,7 +90,7 @@ class ContactCircularFingerprint(ComplexFeaturizer): def _featurize_complex(self, molecular_complex): """ Compute featurization for a single mol/protein complex Compute featurization for a molecular complex Parameters ---------- Loading
deepchem/feat/splif_fingerprints.py +65 −39 Original line number Diff line number Diff line """ SPLIF Fingeprints for protein-ligand complexes. SPLIF Fingerprints for molecular complexes. """ import logging import numpy as np Loading @@ -16,29 +16,42 @@ logger = logging.getLogger(__name__) SPLIF_CONTACT_BINS = [(0, 2.0), (2.0, 3.0), (3.0, 4.5)] def compute_splif_features_in_range(protein, ligand, def compute_splif_features_in_range(frag1, frag2, pairwise_distances, contact_bin, ecfp_degree=2): """Computes SPLIF features for protein atoms close to ligand atoms. """Computes SPLIF features for close atoms in molecular complexes. Finds all protein atoms that are > contact_bin[0] and < contact_bin[1] away from ligand atoms. Then, finds the ECFP Finds all frag1 atoms that are > contact_bin[0] and < contact_bin[1] away from frag2 atoms. Then, finds the ECFP fingerprints for the contacting atoms. Returns a dictionary mapping (protein_index_i, ligand_index_j) --> (protein_ecfp_i, ligand_ecfp_j) mapping (frag1_index_i, frag2_index_j) --> (frag1_ecfp_i, frag2_ecfp_j) 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 """ contacts = np.nonzero((pairwise_distances > contact_bin[0]) & (pairwise_distances < contact_bin[1])) protein_atoms = set([int(c) for c in contacts[0].tolist()]) frag1_atoms = set([int(c) for c in contacts[0].tolist()]) contacts = zip(contacts[0], contacts[1]) protein_ecfp_dict = compute_all_ecfp( protein, indices=protein_atoms, degree=ecfp_degree) ligand_ecfp_dict = compute_all_ecfp(ligand, degree=ecfp_degree) frag1_ecfp_dict = compute_all_ecfp( frag1, indices=frag1_atoms, degree=ecfp_degree) frag2_ecfp_dict = compute_all_ecfp(frag2, degree=ecfp_degree) splif_dict = { contact: (protein_ecfp_dict[contact[0]], ligand_ecfp_dict[contact[1]]) contact: (frag1_ecfp_dict[contact[0]], frag2_ecfp_dict[contact[1]]) for contact in contacts } return (splif_dict) Loading Loading @@ -92,10 +105,12 @@ class SplifFingerprint(ComplexFeaturizer): and modeling 54.9 (2014): 2555-2561. SPLIF fingerprints are a subclass of `ComplexFeaturizer`. It requires 3D coordinates for a protein-ligand complex. For each ligand atom, it identifies close protein atoms. These atom pairs are expanded to 2D circular fragments and a fingerprint for the union is turned on in the bit vector. requires 3D coordinates for a molecular complex. For each ligand atom, it identifies close pairs of atoms from different molecules. These atom pairs are expanded to 2D circular fragments and a fingerprint for the union is turned on in the bit vector. Note that we slightly generalize the original paper by not requiring the interacting molecules to be proteins or ligands. This is conceptually pretty similar to `ContactCircularFingerprint` but computes ECFP fragments only Loading Loading @@ -127,32 +142,36 @@ class SplifFingerprint(ComplexFeaturizer): self.size = size self.radius = radius def _featurize_complex(self, mol, protein): def _featurize_complex(self, molecular_complex): """ Compute featurization for a single mol/protein complex TODO(rbharath): This is very not ergonomic. I'd much prefer returning an vector instead of a list of two vectors. In addition, there's a question of efficiency. RdkitGridFeaturizer caches rotated versions etc internally. To make things work out of box, we are accepting that kludgey input. This needs to be cleaned up before full merge. Compute featurization for a molecular complex Parameters ---------- mol: object Representation of the molecule protein: object Representation of the protein molecular_complex: Object Some representation of a molecular 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 for (frag1, frag2) in itertools.combinations(fragments, 2): # Get coordinates distances = compute_pairwise_distances(frag1[0], frag2[0]) #(lig_xyz, lig_rdk), (prot_xyz, prot_rdk) = mol, protein #distances = compute_pairwise_distances(prot_xyz, lig_xyz) vectors = [ vectorize(hash_ecfp_pair, feature_dict=splif_dict, size=self.size) for splif_dict in featurize_splif( prot_xyz, prot_rdk, lig_xyz, lig_rdk, self.contact_bins, distances, self.radius) ] pairwse_features += vector pairwise_features = np.concatenate(pairwise_features) return pairwise_features class SplifVoxelizer(ComplexFeaturizer): """Computes SPLIF voxel grid for a macromolecular complex. Loading Loading @@ -180,7 +199,8 @@ class SplifVoxelizer(ComplexFeaturizer): radius=2, size=8, box_width=16.0, voxel_width=1.0): voxel_width=1.0, reduce_to_contacts=True): """ Parameters ---------- Loading @@ -196,6 +216,9 @@ class SplifVoxelizer(ComplexFeaturizer): is centered on a ligand centroid. voxel_width: float, optional (default 1.0) Size of a 3D voxel in a grid. reduce_to_contacts: bool, optional If True, reduce the atoms in the complex to those near a contact region. """ if contact_bins is None: self.contact_bins = SPLIF_CONTACT_BINS Loading @@ -206,6 +229,7 @@ class SplifVoxelizer(ComplexFeaturizer): self.box_width = box_width self.voxel_width = voxel_width self.voxels_per_edge = int(self.box_width / self.voxel_width) self.reduce_to_contacts = reduce_to_contacts def _featurize_complex(self, molecular_complex): """ Loading Loading @@ -234,6 +258,8 @@ class SplifVoxelizer(ComplexFeaturizer): pairwise_features = [] # We compute pairwise contact fingerprints centroid = compute_contact_centroid(fragments, cutoff=self.cutoff) if self.reduce_to_contacts: fragments = reduce_molecular_complex_to_contacts(fragments, 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) Loading
