class GridPoseScorer(object):
def __init__(self, model, feat="grid"):
"""Initializes a pose-scorer."""
self.model = model
if feat == "grid":
self.featurizer = RdkitGridFeaturizer(
voxel_width=16.0,
# TODO: add pi_stack and cation_pi to feature_types (it's not trivial
# because they require sanitized molecules)
# feature_types=["ecfp", "splif", "hbond", "pi_stack", "cation_pi",
# "salt_bridge"],
feature_types=["ecfp", "splif", "hbond", "salt_bridge"],
ecfp_power=9,
splif_power=9,
flatten=True)
else:
raise ValueError("feat not defined.")
def score(self, protein_file, ligand_file):
"""Returns a score for a protein/ligand pair."""
features, _ = self.featurizer.featurize_complexes([ligand_file],
[protein_file])
dataset = NumpyDataset(X=features, y=None, w=None, ids=None)
score = self.model.predict(dataset)
return score
class GridPoseScorer(object):
def __init__(self, model, feat="grid"):
"""Initializes a pose-scorer."""
self.model = model
if feat == "grid":
self.featurizer = RdkitGridFeaturizer(
voxel_width=16.0,
# TODO: add pi_stack and cation_pi to feature_types (it's not trivial
# because they require sanitized molecules)
# feature_types=["ecfp", "splif", "hbond", "pi_stack", "cation_pi",
# "salt_bridge"],
feature_types=["ecfp", "splif", "hbond", "salt_bridge"],
ecfp_power=9,
splif_power=9,
flatten=True)
else:
raise ValueError("feat not defined.")
def score(self, protein_file, ligand_file):
"""Returns a score for a protein/ligand pair."""
features = self.featurizer.featurize_complexes([ligand_file],
[protein_file])
dataset = NumpyDataset(X=features, y=None, w=None, ids=None)
score = self.model.predict(dataset)
return score
class GridPoseScorer(object):
def __init__(self, model, feat="grid"):
"""Initializes a pose-scorer."""
self.model = model
if feat == "grid":
self.featurizer = RdkitGridFeaturizer(
voxel_width=16.0,
feature_types="voxel_combined",
# TODO(rbharath, enf): Figure out why pi_stack is slow and cation_pi
# causes segfaults.
#voxel_feature_types=["ecfp", "splif", "hbond", "pi_stack", "cation_pi",
#"salt_bridge"], ecfp_power=9, splif_power=9,
voxel_feature_types=["ecfp", "splif", "hbond", "salt_bridge"],
ecfp_power=9,
splif_power=9,
parallel=True,
flatten=True)
else:
raise ValueError("feat not defined.")
def score(self, protein_file, ligand_file):
"""Returns a score for a protein/ligand pair."""
features = self.featurizer.featurize_complexes([ligand_file],
[protein_file])
dataset = NumpyDataset(X=features, y=None, w=None, ids=None)
score = self.model.predict(dataset)
return score