def _featurize(self, datapoint, **kwargs): # -> Optional[np.ndarray]:
"""
Compute featurization for a single mol/protein complex
Parameters
----------
datapoint: Tuple[str, str]
Filenames for molecule and protein.
"""
if 'complex' in kwargs:
datapoint = kwargs.get("complex")
raise DeprecationWarning(
'Complex is being phased out as a parameter, please pass "datapoint" instead.'
)
try:
fragments = rdkit_utils.load_complex(datapoint,
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)
if self.reduce_to_contacts:
fragments = reduce_molecular_complex_to_contacts(
fragments, self.cutoff)
for (frag1_ind,
frag2_ind) in itertools.combinations(range(len(fragments)), 2):
frag1, frag2 = fragments[frag1_ind], fragments[frag2_ind]
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]
# rdks = [frag1[1], frag2[1]]
pairwise_features.append(
np.concatenate([
sum([
voxelize(convert_atom_pair_to_voxel,
hash_function=None,
box_width=self.box_width,
voxel_width=self.voxel_width,
coordinates=xyz,
feature_list=hbond_list,
nb_channel=1) for xyz in xyzs
]) for hbond_list in compute_hydrogen_bonds(
frag1, frag2, distances, self.distance_bins,
self.angle_cutoffs)
],
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)
def _featurize(self, mol_pdb: str, protein_pdb: str) -> np.ndarray:
"""
Compute featurization for a single mol/protein complex
Parameters
----------
mol_pdb: str
Filename for ligand molecule
protein_pdb: str
Filename for protein molecule
"""
molecular_complex = (mol_pdb, protein_pdb)
try:
fragments = rdkit_utils.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)
if self.reduce_to_contacts:
fragments = reduce_molecular_complex_to_contacts(fragments, self.cutoff)
for (frag1_ind, frag2_ind) in itertools.combinations(
range(len(fragments)), 2):
frag1, frag2 = fragments[frag1_ind], fragments[frag2_ind]
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]
# rdks = [frag1[1], frag2[1]]
pairwise_features.append(
np.concatenate(
[
sum([
voxelize(
convert_atom_pair_to_voxel,
hash_function=None,
box_width=self.box_width,
voxel_width=self.voxel_width,
coordinates=xyz,
feature_list=hbond_list,
nb_channel=1) for xyz in xyzs
]) for hbond_list in compute_hydrogen_bonds(
frag1, frag2, distances, self.distance_bins,
self.angle_cutoffs)
],
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)
def _featurize(self, complex: Tuple[str, str]) -> Optional[np.ndarray]:
"""
Compute featurization for a single mol/protein complex
Parameters
----------
complex: Tuple[str, str]
Filenames for molecule and protein.
"""
try:
fragments = rdkit_utils.load_complex(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)
if self.reduce_to_contacts:
fragments = reduce_molecular_complex_to_contacts(
fragments, self.cutoff)
# We compute pairwise contact fingerprints
for (frag1_ind,
frag2_ind) in itertools.combinations(range(len(fragments)), 2):
frag1, frag2 = fragments[frag1_ind], fragments[frag2_ind]
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]
# rdks = [frag1[1], frag2[1]]
pairwise_features.append(
np.concatenate([
np.array([len(hbond_list)])
for hbond_list in compute_hydrogen_bonds(
frag1, frag2, distances, self.distance_bins,
self.angle_cutoffs)
],
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)
def _compute_feature(self, feature_name, prot_xyz, prot_rdk, lig_xyz,
lig_rdk, distances):
if feature_name == 'ecfp_ligand':
return [
compute_ecfp_features(lig_rdk, self.ecfp_degree,
self.ecfp_power)
]
if feature_name == 'ecfp_hashed':
return [
vectorize(hash_ecfp,
feature_dict=ecfp_dict,
size=2**self.ecfp_power)
for ecfp_dict in featurize_contacts_ecfp(
(prot_xyz, prot_rdk), (lig_xyz, lig_rdk),
distances,
cutoff=self.cutoffs['ecfp_cutoff'],
ecfp_degree=self.ecfp_degree)
]
if feature_name == 'splif_hashed':
return [
vectorize(hash_ecfp_pair,
feature_dict=splif_dict,
size=2**self.splif_power)
for splif_dict in featurize_splif((prot_xyz, prot_rdk), (
lig_xyz, lig_rdk), self.cutoffs['splif_contact_bins'],
distances, self.ecfp_degree)
]
if feature_name == 'hbond_count':
return [
vectorize(hash_ecfp_pair, feature_list=hbond_list, size=2**0)
for hbond_list in
compute_hydrogen_bonds((prot_xyz, prot_rdk), (
lig_xyz,
lig_rdk), distances, self.cutoffs['hbond_dist_bins'],
self.cutoffs['hbond_angle_cutoffs'])
]
if feature_name == 'ecfp':
return [
sum([
voxelize(
convert_atom_to_voxel,
xyz,
box_width=self.box_width,
voxel_width=self.voxel_width,
hash_function=hash_ecfp,
feature_dict=ecfp_dict,
nb_channel=2**self.ecfp_power,
) for xyz, ecfp_dict in zip(
(prot_xyz, lig_xyz),
featurize_contacts_ecfp(
(prot_xyz, prot_rdk), (lig_xyz, lig_rdk),
distances,
cutoff=self.cutoffs['ecfp_cutoff'],
ecfp_degree=self.ecfp_degree))
])
]
if feature_name == 'splif':
return [
voxelize(
convert_atom_pair_to_voxel,
(prot_xyz, lig_xyz),
box_width=self.box_width,
voxel_width=self.voxel_width,
hash_function=hash_ecfp_pair,
feature_dict=splif_dict,
nb_channel=2**self.splif_power,
)
for splif_dict in featurize_splif((prot_xyz, prot_rdk), (
lig_xyz, lig_rdk), self.cutoffs['splif_contact_bins'],
distances, self.ecfp_degree)
]
if feature_name == 'sybyl':
def hash_sybyl_func(x):
hash_sybyl(x, sybyl_types=self.sybyl_types)
return [
voxelize(
convert_atom_to_voxel,
xyz,
box_width=self.box_width,
voxel_width=self.voxel_width,
hash_function=hash_sybyl_func,
feature_dict=sybyl_dict,
nb_channel=len(self.sybyl_types),
) for xyz, sybyl_dict in zip(
(prot_xyz, lig_xyz),
featurize_binding_pocket_sybyl(
prot_xyz,
prot_rdk,
lig_xyz,
lig_rdk,
distances,
cutoff=self.cutoffs['sybyl_cutoff']))
]
if feature_name == 'salt_bridge':
return [
voxelize(
convert_atom_pair_to_voxel,
(prot_xyz, lig_xyz),
box_width=self.box_width,
voxel_width=self.voxel_width,
feature_list=compute_salt_bridges(
prot_rdk,
lig_rdk,
distances,
cutoff=self.cutoffs['salt_bridges_cutoff']),
nb_channel=1,
)
]
if feature_name == 'charge':
return [
sum([
voxelize(convert_atom_to_voxel,
xyz,
box_width=self.box_width,
voxel_width=self.voxel_width,
feature_dict=compute_charge_dictionary(mol),
nb_channel=1,
dtype="np.float16")
for xyz, mol in ((prot_xyz, prot_rdk), (lig_xyz, lig_rdk))
])
]
if feature_name == 'hbond':
return [
voxelize(
convert_atom_pair_to_voxel,
(prot_xyz, lig_xyz),
box_width=self.box_width,
voxel_width=self.voxel_width,
feature_list=hbond_list,
nb_channel=2**0,
) for hbond_list in
compute_hydrogen_bonds((prot_xyz, prot_rdk), (
lig_xyz,
lig_rdk), distances, self.cutoffs['hbond_dist_bins'],
self.cutoffs['hbond_angle_cutoffs'])
]
if feature_name == 'pi_stack':
return voxelize_pi_stack(prot_xyz, prot_rdk, lig_xyz, lig_rdk,
distances,
self.cutoffs['pi_stack_dist_cutoff'],
self.cutoffs['pi_stack_angle_cutoff'],
self.box_width, self.voxel_width)
if feature_name == 'cation_pi':
return [
sum([
voxelize(
convert_atom_to_voxel,
xyz,
box_width=self.box_width,
voxel_width=self.voxel_width,
feature_dict=cation_pi_dict,
nb_channel=1,
) for xyz, cation_pi_dict in zip(
(prot_xyz, lig_xyz),
compute_binding_pocket_cation_pi(
prot_rdk,
lig_rdk,
dist_cutoff=self.cutoffs['cation_pi_dist_cutoff'],
angle_cutoff=self.
cutoffs['cation_pi_angle_cutoff'],
))
])
]
raise ValueError('Unknown feature type "%s"' % feature_name)
