Loading deepchem/feat/__init__.py +2 −0 Original line number Diff line number Diff line Loading @@ -39,6 +39,8 @@ from deepchem.feat.complex_featurizers import NeighborListComplexAtomicCoordinat from deepchem.feat.complex_featurizers import ComplexNeighborListFragmentAtomicCoordinates from deepchem.feat.complex_featurizers import ContactCircularFingerprint from deepchem.feat.complex_featurizers import ContactCircularVoxelizer from deepchem.feat.complex_featurizers import SplifFingerprint from deepchem.feat.complex_featurizers import SplifVoxelizer from deepchem.feat.complex_featurizers import ChargeVoxelizer from deepchem.feat.complex_featurizers import SaltBridgeVoxelizer from deepchem.feat.complex_featurizers import CationPiVoxelizer Loading deepchem/feat/complex_featurizers/__init__.py +2 −0 Original line number Diff line number Diff line Loading @@ -14,3 +14,5 @@ from deepchem.feat.complex_featurizers.grid_featurizers import CationPiVoxelizer from deepchem.feat.complex_featurizers.grid_featurizers import PiStackVoxelizer from deepchem.feat.complex_featurizers.grid_featurizers import HydrogenBondVoxelizer from deepchem.feat.complex_featurizers.grid_featurizers import HydrogenBondCounter from deepchem.feat.complex_featurizers.splif_fingerprints import SplifFingerprint from deepchem.feat.complex_featurizers.splif_fingerprints import SplifVoxelizer deepchem/feat/complex_featurizers/splif_fingerprints.py +55 −59 Original line number Diff line number Diff line Loading @@ -2,26 +2,34 @@ SPLIF Fingerprints for molecular complexes. """ import logging import itertools import numpy as np from deepchem.utils.hash_utils import hash_ecfp_pair from deepchem.utils.rdkit_util import compute_all_ecfp from deepchem.utils.rdkit_utils import load_complex from deepchem.utils.rdkit_utils import compute_all_ecfp from deepchem.utils.rdkit_utils import MoleculeLoadException from deepchem.utils.rdkit_utils import compute_contact_centroid from deepchem.utils.rdkit_utils import reduce_molecular_complex_to_contacts from deepchem.feat import ComplexFeaturizer from deepchem.utils.hash_utils import vectorize from deepchem.utils.voxel_utils import voxelize from deepchem.utils.voxel_utils import convert_atom_to_voxel from deepchem.utils.voxel_utils import convert_atom_pair_to_voxel from deepchem.utils.geometry_utils import compute_pairwise_distances from deepchem.utils.geometry_utils import subtract_centroid from typing import Tuple, Dict, List logger = logging.getLogger(__name__) SPLIF_CONTACT_BINS = [(0, 2.0), (2.0, 3.0), (3.0, 4.5)] def compute_splif_features_in_range(frag1, frag2, pairwise_distances, contact_bin, ecfp_degree=2): def compute_splif_features_in_range(frag1: Tuple, frag2: Tuple, pairwise_distances: np.ndarray, contact_bin: List, ecfp_degree: int = 2) -> Dict: """Computes SPLIF features for close atoms in molecular complexes. Finds all frag1 atoms that are > contact_bin[0] and < Loading @@ -33,11 +41,11 @@ def compute_splif_features_in_range(frag1, Parameters ---------- frag1: Tuple A tuple of (coords, mol) returned by `rdkit_util.load_molecule`. A tuple of (coords, mol) returned by `load_molecule`. frag2: Tuple A tuple of (coords, mol) returned by `rdkit_util.load_molecule`. A tuple of (coords, mol) returned by `load_molecule`. contact_bins: np.ndarray TODO Ranges of pair distances which are placed in separate bins. pairwise_distances: np.ndarray Array of pairwise fragment-fragment distances (Angstroms) ecfp_degree: int Loading @@ -49,13 +57,13 @@ def compute_splif_features_in_range(frag1, contacts = zip(contacts[0], contacts[1]) frag1_ecfp_dict = compute_all_ecfp( frag1, indices=frag1_atoms, degree=ecfp_degree) frag2_ecfp_dict = compute_all_ecfp(frag2, degree=ecfp_degree) frag1[1], indices=frag1_atoms, degree=ecfp_degree) frag2_ecfp_dict = compute_all_ecfp(frag2[1], degree=ecfp_degree) splif_dict = { contact: (frag1_ecfp_dict[contact[0]], frag2_ecfp_dict[contact[1]]) for contact in contacts } return (splif_dict) return splif_dict def featurize_splif(frag1, frag2, contact_bins, pairwise_distances, Loading @@ -70,15 +78,15 @@ def featurize_splif(frag1, frag2, contact_bins, pairwise_distances, Parameters ---------- frag1: Tuple A tuple of (coords, mol) returned by `rdkit_util.load_molecule`. A tuple of (coords, mol) returned by `load_molecule`. frag2: Tuple A tuple of (coords, mol) returned by `rdkit_util.load_molecule`. A tuple of (coords, mol) returned by `load_molecule`. contact_bins: np.ndarray TODO Ranges of pair distances which are placed in separate bins. pairwise_distances: np.ndarray Array of pairwise fragment-fragment distances (Angstroms) ecfp_degree: int ECFP radius ECFP radius, the graph distance at which fragments are computed. Returns ------- Loading @@ -91,7 +99,7 @@ def featurize_splif(frag1, frag2, contact_bins, pairwise_distances, compute_splif_features_in_range(frag1, frag2, pairwise_distances, contact_bin, ecfp_degree)) return (splif_dicts) return splif_dicts class SplifFingerprint(ComplexFeaturizer): Loading @@ -118,7 +126,7 @@ class SplifFingerprint(ComplexFeaturizer): for direct contacts instead of the entire contact region. For a macromolecular complex, returns a vector of shape `(2*size,)` `(len(contact_bins)*size,)` """ def __init__(self, contact_bins=None, radius=2, size=8): Loading @@ -140,18 +148,20 @@ class SplifFingerprint(ComplexFeaturizer): self.size = size self.radius = radius def _featurize_complex(self, molecular_complex): def _featurize(self, mol_pdb: str, complex_pdb: str): """ Compute featurization for a molecular complex Parameters ---------- molecular_complex: Object Some representation of a molecular complex. mol_pdb: str Filename for ligand molecule complex_pdb: str Filename for protein molecule """ molecular_complex = (mol_pdb, complex_pdb) try: fragments = rdkit_util.load_complex( molecular_complex, add_hydrogens=False) fragments = load_complex(molecular_complex, add_hydrogens=False) except MoleculeLoadException: logger.warning("This molecule cannot be loaded by Rdkit. Returning None") Loading @@ -161,15 +171,13 @@ class SplifFingerprint(ComplexFeaturizer): 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) frag1, frag2, self.contact_bins, distances, self.radius) ] pairwse_features += vector pairwise_features += vectors pairwise_features = np.concatenate(pairwise_features) return pairwise_features Loading @@ -196,15 +204,17 @@ class SplifVoxelizer(ComplexFeaturizer): """ def __init__(self, contact_bins=None, radius=2, size=8, box_width=16.0, voxel_width=1.0, reduce_to_contacts=True): cutoff: float = 4.5, contact_bins: List = None, radius: int = 2, size: int = 8, box_width: float = 16.0, voxel_width: float = 1.0): """ Parameters ---------- cutoff: float (default 4.5) Distance cutoff in angstroms for molecules in complex. contact_bins: list[tuple] List of contact bins. If not specified is set to default `[(0, 2.0), (2.0, 3.0), (3.0, 4.5)]`. Loading @@ -217,10 +227,8 @@ 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. """ self.cutoff = cutoff if contact_bins is None: self.contact_bins = SPLIF_CONTACT_BINS else: Loading @@ -230,29 +238,21 @@ 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): def _featurize(self, mol_pdb: str, complex_pdb: str): """ 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 ---------- molecular_complex: Object A representation of a molecular complex, produced by `rdkit_util.load_complex`. mol_pdb: str Filename for ligand molecule complex_pdb: str Filename for protein molecule """ molecular_complex = (mol_pdb, complex_pdb) try: fragments = rdkit_util.load_complex( molecular_complex, add_hydrogens=False) fragments = load_complex(molecular_complex, add_hydrogens=False) except MoleculeLoadException: logger.warning("This molecule cannot be loaded by Rdkit. Returning None") Loading @@ -260,15 +260,11 @@ 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) 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( [ Loading @@ -279,9 +275,9 @@ class SplifVoxelizer(ComplexFeaturizer): 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) nb_channel=self.size) for splif_dict in featurize_splif( frag1, frag2, 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. Loading deepchem/feat/tests/test_splif_fingerprints.py +22 −2 Original line number Diff line number Diff line import unittest import os import deepchem as dc Loading @@ -8,7 +9,26 @@ class TestSplifFingerprints(unittest.TestCase): 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, self.protein_file = os.path.join(current_dir, 'data', '3ws9_protein_fixer_rdkit.pdb') self.ligand_file = os.path.join(current_dir, '3ws9_ligand.sdf') self.ligand_file = os.path.join(current_dir, 'data', '3ws9_ligand.sdf') self.complex_files = [(self.protein_file, self.ligand_file)] def test_splif_shape(self): size = 8 featurizer = dc.feat.SplifFingerprint(size=size) features, failures = featurizer.featurize([self.ligand_file], [self.protein_file]) assert features.shape == (1, 3 * size) def test_splif_voxels_shape(self): box_width = 48 voxel_width = 2 voxels_per_edge = box_width / voxel_width size = 8 voxelizer = dc.feat.SplifVoxelizer( box_width=box_width, voxel_width=voxel_width, size=size) features, failures = voxelizer._featurize(self.ligand_file, self.protein_file) assert features.shape == (1, voxels_per_edge, voxels_per_edge, voxels_per_edge, size) deepchem/utils/fragment_utils.py +10 −0 Original line number Diff line number Diff line Loading @@ -119,6 +119,16 @@ class MolecularFragment(object): """ return self.atoms def GetNumAtoms(self) -> int: """Returns the number of atoms Returns ------- int Number of atoms in this fragment. """ return len(self.atoms) def GetCoords(self) -> np.ndarray: """Returns 3D coordinates for this fragment as numpy array. Loading Loading
deepchem/feat/__init__.py +2 −0 Original line number Diff line number Diff line Loading @@ -39,6 +39,8 @@ from deepchem.feat.complex_featurizers import NeighborListComplexAtomicCoordinat from deepchem.feat.complex_featurizers import ComplexNeighborListFragmentAtomicCoordinates from deepchem.feat.complex_featurizers import ContactCircularFingerprint from deepchem.feat.complex_featurizers import ContactCircularVoxelizer from deepchem.feat.complex_featurizers import SplifFingerprint from deepchem.feat.complex_featurizers import SplifVoxelizer from deepchem.feat.complex_featurizers import ChargeVoxelizer from deepchem.feat.complex_featurizers import SaltBridgeVoxelizer from deepchem.feat.complex_featurizers import CationPiVoxelizer Loading
deepchem/feat/complex_featurizers/__init__.py +2 −0 Original line number Diff line number Diff line Loading @@ -14,3 +14,5 @@ from deepchem.feat.complex_featurizers.grid_featurizers import CationPiVoxelizer from deepchem.feat.complex_featurizers.grid_featurizers import PiStackVoxelizer from deepchem.feat.complex_featurizers.grid_featurizers import HydrogenBondVoxelizer from deepchem.feat.complex_featurizers.grid_featurizers import HydrogenBondCounter from deepchem.feat.complex_featurizers.splif_fingerprints import SplifFingerprint from deepchem.feat.complex_featurizers.splif_fingerprints import SplifVoxelizer
deepchem/feat/complex_featurizers/splif_fingerprints.py +55 −59 Original line number Diff line number Diff line Loading @@ -2,26 +2,34 @@ SPLIF Fingerprints for molecular complexes. """ import logging import itertools import numpy as np from deepchem.utils.hash_utils import hash_ecfp_pair from deepchem.utils.rdkit_util import compute_all_ecfp from deepchem.utils.rdkit_utils import load_complex from deepchem.utils.rdkit_utils import compute_all_ecfp from deepchem.utils.rdkit_utils import MoleculeLoadException from deepchem.utils.rdkit_utils import compute_contact_centroid from deepchem.utils.rdkit_utils import reduce_molecular_complex_to_contacts from deepchem.feat import ComplexFeaturizer from deepchem.utils.hash_utils import vectorize from deepchem.utils.voxel_utils import voxelize from deepchem.utils.voxel_utils import convert_atom_to_voxel from deepchem.utils.voxel_utils import convert_atom_pair_to_voxel from deepchem.utils.geometry_utils import compute_pairwise_distances from deepchem.utils.geometry_utils import subtract_centroid from typing import Tuple, Dict, List logger = logging.getLogger(__name__) SPLIF_CONTACT_BINS = [(0, 2.0), (2.0, 3.0), (3.0, 4.5)] def compute_splif_features_in_range(frag1, frag2, pairwise_distances, contact_bin, ecfp_degree=2): def compute_splif_features_in_range(frag1: Tuple, frag2: Tuple, pairwise_distances: np.ndarray, contact_bin: List, ecfp_degree: int = 2) -> Dict: """Computes SPLIF features for close atoms in molecular complexes. Finds all frag1 atoms that are > contact_bin[0] and < Loading @@ -33,11 +41,11 @@ def compute_splif_features_in_range(frag1, Parameters ---------- frag1: Tuple A tuple of (coords, mol) returned by `rdkit_util.load_molecule`. A tuple of (coords, mol) returned by `load_molecule`. frag2: Tuple A tuple of (coords, mol) returned by `rdkit_util.load_molecule`. A tuple of (coords, mol) returned by `load_molecule`. contact_bins: np.ndarray TODO Ranges of pair distances which are placed in separate bins. pairwise_distances: np.ndarray Array of pairwise fragment-fragment distances (Angstroms) ecfp_degree: int Loading @@ -49,13 +57,13 @@ def compute_splif_features_in_range(frag1, contacts = zip(contacts[0], contacts[1]) frag1_ecfp_dict = compute_all_ecfp( frag1, indices=frag1_atoms, degree=ecfp_degree) frag2_ecfp_dict = compute_all_ecfp(frag2, degree=ecfp_degree) frag1[1], indices=frag1_atoms, degree=ecfp_degree) frag2_ecfp_dict = compute_all_ecfp(frag2[1], degree=ecfp_degree) splif_dict = { contact: (frag1_ecfp_dict[contact[0]], frag2_ecfp_dict[contact[1]]) for contact in contacts } return (splif_dict) return splif_dict def featurize_splif(frag1, frag2, contact_bins, pairwise_distances, Loading @@ -70,15 +78,15 @@ def featurize_splif(frag1, frag2, contact_bins, pairwise_distances, Parameters ---------- frag1: Tuple A tuple of (coords, mol) returned by `rdkit_util.load_molecule`. A tuple of (coords, mol) returned by `load_molecule`. frag2: Tuple A tuple of (coords, mol) returned by `rdkit_util.load_molecule`. A tuple of (coords, mol) returned by `load_molecule`. contact_bins: np.ndarray TODO Ranges of pair distances which are placed in separate bins. pairwise_distances: np.ndarray Array of pairwise fragment-fragment distances (Angstroms) ecfp_degree: int ECFP radius ECFP radius, the graph distance at which fragments are computed. Returns ------- Loading @@ -91,7 +99,7 @@ def featurize_splif(frag1, frag2, contact_bins, pairwise_distances, compute_splif_features_in_range(frag1, frag2, pairwise_distances, contact_bin, ecfp_degree)) return (splif_dicts) return splif_dicts class SplifFingerprint(ComplexFeaturizer): Loading @@ -118,7 +126,7 @@ class SplifFingerprint(ComplexFeaturizer): for direct contacts instead of the entire contact region. For a macromolecular complex, returns a vector of shape `(2*size,)` `(len(contact_bins)*size,)` """ def __init__(self, contact_bins=None, radius=2, size=8): Loading @@ -140,18 +148,20 @@ class SplifFingerprint(ComplexFeaturizer): self.size = size self.radius = radius def _featurize_complex(self, molecular_complex): def _featurize(self, mol_pdb: str, complex_pdb: str): """ Compute featurization for a molecular complex Parameters ---------- molecular_complex: Object Some representation of a molecular complex. mol_pdb: str Filename for ligand molecule complex_pdb: str Filename for protein molecule """ molecular_complex = (mol_pdb, complex_pdb) try: fragments = rdkit_util.load_complex( molecular_complex, add_hydrogens=False) fragments = load_complex(molecular_complex, add_hydrogens=False) except MoleculeLoadException: logger.warning("This molecule cannot be loaded by Rdkit. Returning None") Loading @@ -161,15 +171,13 @@ class SplifFingerprint(ComplexFeaturizer): 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) frag1, frag2, self.contact_bins, distances, self.radius) ] pairwse_features += vector pairwise_features += vectors pairwise_features = np.concatenate(pairwise_features) return pairwise_features Loading @@ -196,15 +204,17 @@ class SplifVoxelizer(ComplexFeaturizer): """ def __init__(self, contact_bins=None, radius=2, size=8, box_width=16.0, voxel_width=1.0, reduce_to_contacts=True): cutoff: float = 4.5, contact_bins: List = None, radius: int = 2, size: int = 8, box_width: float = 16.0, voxel_width: float = 1.0): """ Parameters ---------- cutoff: float (default 4.5) Distance cutoff in angstroms for molecules in complex. contact_bins: list[tuple] List of contact bins. If not specified is set to default `[(0, 2.0), (2.0, 3.0), (3.0, 4.5)]`. Loading @@ -217,10 +227,8 @@ 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. """ self.cutoff = cutoff if contact_bins is None: self.contact_bins = SPLIF_CONTACT_BINS else: Loading @@ -230,29 +238,21 @@ 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): def _featurize(self, mol_pdb: str, complex_pdb: str): """ 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 ---------- molecular_complex: Object A representation of a molecular complex, produced by `rdkit_util.load_complex`. mol_pdb: str Filename for ligand molecule complex_pdb: str Filename for protein molecule """ molecular_complex = (mol_pdb, complex_pdb) try: fragments = rdkit_util.load_complex( molecular_complex, add_hydrogens=False) fragments = load_complex(molecular_complex, add_hydrogens=False) except MoleculeLoadException: logger.warning("This molecule cannot be loaded by Rdkit. Returning None") Loading @@ -260,15 +260,11 @@ 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) 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( [ Loading @@ -279,9 +275,9 @@ class SplifVoxelizer(ComplexFeaturizer): 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) nb_channel=self.size) for splif_dict in featurize_splif( frag1, frag2, 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. Loading
deepchem/feat/tests/test_splif_fingerprints.py +22 −2 Original line number Diff line number Diff line import unittest import os import deepchem as dc Loading @@ -8,7 +9,26 @@ class TestSplifFingerprints(unittest.TestCase): 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, self.protein_file = os.path.join(current_dir, 'data', '3ws9_protein_fixer_rdkit.pdb') self.ligand_file = os.path.join(current_dir, '3ws9_ligand.sdf') self.ligand_file = os.path.join(current_dir, 'data', '3ws9_ligand.sdf') self.complex_files = [(self.protein_file, self.ligand_file)] def test_splif_shape(self): size = 8 featurizer = dc.feat.SplifFingerprint(size=size) features, failures = featurizer.featurize([self.ligand_file], [self.protein_file]) assert features.shape == (1, 3 * size) def test_splif_voxels_shape(self): box_width = 48 voxel_width = 2 voxels_per_edge = box_width / voxel_width size = 8 voxelizer = dc.feat.SplifVoxelizer( box_width=box_width, voxel_width=voxel_width, size=size) features, failures = voxelizer._featurize(self.ligand_file, self.protein_file) assert features.shape == (1, voxels_per_edge, voxels_per_edge, voxels_per_edge, size)
deepchem/utils/fragment_utils.py +10 −0 Original line number Diff line number Diff line Loading @@ -119,6 +119,16 @@ class MolecularFragment(object): """ return self.atoms def GetNumAtoms(self) -> int: """Returns the number of atoms Returns ------- int Number of atoms in this fragment. """ return len(self.atoms) def GetCoords(self) -> np.ndarray: """Returns 3D coordinates for this fragment as numpy array. Loading
