def __init__(self, exhaustiveness=10, detect_pockets=False):
"""Builds model."""
self.base_dir = tempfile.mkdtemp()
print("About to download trained model.")
call((
"wget -nv -c http://deepchem.io.s3-website-us-west-1.amazonaws.com/trained_models/random_full_DNN.tar.gz"
).split())
call(("tar -zxvf random_full_DNN.tar.gz").split())
call(("mv random_full_DNN %s" % (self.base_dir)).split())
self.model_dir = os.path.join(self.base_dir, "random_full_DNN")
# Fit model on dataset
pdbbind_tasks = ["-logKd/Ki"]
n_features = 2052
model = TensorflowMultiTaskRegressor(len(pdbbind_tasks),
n_features,
logdir=self.model_dir,
dropouts=[.25],
learning_rate=0.0003,
weight_init_stddevs=[.1],
batch_size=64)
model.reload()
self.pose_scorer = GridPoseScorer(model, feat="grid")
self.pose_generator = VinaPoseGenerator(exhaustiveness=exhaustiveness,
detect_pockets=detect_pockets)
class VinaGridRFDocker(Docker):
"""Vina pose-generation, RF-models on grid-featurization of complexes."""
def __init__(self, exhaustiveness=10, detect_pockets=False):
"""Builds model."""
self.base_dir = tempfile.mkdtemp()
logger.info("About to download trained model.")
call((
"wget -nv -c http://deepchem.io.s3-website-us-west-1.amazonaws.com/trained_models/random_full_RF.tar.gz"
).split())
call(("tar -zxvf random_full_RF.tar.gz").split())
call(("mv random_full_RF %s" % (self.base_dir)).split())
self.model_dir = os.path.join(self.base_dir, "random_full_RF")
# Fit model on dataset
model = SklearnModel(model_dir=self.model_dir)
model.reload()
self.pose_scorer = GridPoseScorer(model, feat="grid")
self.pose_generator = VinaPoseGenerator(exhaustiveness=exhaustiveness,
detect_pockets=detect_pockets)
def dock(self,
protein_file,
ligand_file,
centroid=None,
box_dims=None,
dry_run=False):
"""Docks using Vina and RF."""
protein_docked, ligand_docked = self.pose_generator.generate_poses(
protein_file, ligand_file, centroid, box_dims, dry_run)
if not dry_run:
score = self.pose_scorer.score(protein_docked, ligand_docked)
else:
score = np.zeros((1, ))
return (score, (protein_docked, ligand_docked))
def __init__(self, exhaustiveness=10, detect_pockets=False):
"""Builds model."""
self.base_dir = tempfile.mkdtemp()
print("About to download trained model.")
call((
"wget -c http://deepchem.io.s3-website-us-west-1.amazonaws.com/trained_models/random_full_DNN.tar.gz"
).split())
call(("tar -zxvf random_full_DNN.tar.gz").split())
call(("mv random_full_DNN %s" % (self.base_dir)).split())
self.model_dir = os.path.join(self.base_dir, "random_full_DNN")
# Fit model on dataset
pdbbind_tasks = ["-logKd/Ki"]
n_features = 2052
model = TensorflowMultiTaskRegressor(
len(pdbbind_tasks),
n_features,
logdir=self.model_dir,
dropouts=[.25],
learning_rate=0.0003,
weight_init_stddevs=[.1],
batch_size=64)
model.reload()
self.pose_scorer = GridPoseScorer(model, feat="grid")
self.pose_generator = VinaPoseGenerator(
exhaustiveness=exhaustiveness, detect_pockets=detect_pockets)
class VinaGridDNNDocker(object):
"""Vina pose-generation, DNN-models on grid-featurization of complexes."""
def __init__(self, exhaustiveness=10, detect_pockets=False):
"""Builds model."""
self.base_dir = tempfile.mkdtemp()
print("About to download trained model.")
call(("wget -c http://deepchem.io.s3-website-us-west-1.amazonaws.com/trained_models/random_full_DNN.tar.gz").split())
call(("tar -zxvf random_full_DNN.tar.gz").split())
call(("mv random_full_DNN %s" % (self.base_dir)).split())
self.model_dir = os.path.join(self.base_dir, "random_full_DNN")
# Fit model on dataset
pdbbind_tasks = ["-logKd/Ki"]
n_features = 2052
model = TensorflowMultiTaskRegressor(
len(pdbbind_tasks), n_features, logdir=self.model_dir, dropouts=[.25],
learning_rate=0.0003, weight_init_stddevs=[.1], batch_size=64)
model.reload()
self.pose_scorer = GridPoseScorer(model, feat="grid")
self.pose_generator = VinaPoseGenerator(
exhaustiveness=exhaustiveness, detect_pockets=detect_pockets)
def dock(self, protein_file, ligand_file):
"""Docks using Vina and DNNs."""
protein_docked, ligand_docked = self.pose_generator.generate_poses(
protein_file, ligand_file)
score = self.pose_scorer.score(protein_docked, ligand_docked)
return (score, (protein_docked, ligand_docked))
class VinaGridRFDocker(Docker):
"""Vina pose-generation, RF-models on grid-featurization of complexes."""
def __init__(self, exhaustiveness=10, detect_pockets=False):
"""Builds model."""
self.base_dir = tempfile.mkdtemp()
print("About to download trained model.")
call(("wget -c http://deepchem.io.s3-website-us-west-1.amazonaws.com/trained_models/random_full_RF.tar.gz").split())
call(("tar -zxvf random_full_RF.tar.gz").split())
call(("mv random_full_RF %s" % (self.base_dir)).split())
self.model_dir = os.path.join(self.base_dir, "random_full_RF")
# Fit model on dataset
model = SklearnModel(model_dir=self.model_dir)
model.reload()
self.pose_scorer = GridPoseScorer(model, feat="grid")
self.pose_generator = VinaPoseGenerator(
exhaustiveness=exhaustiveness, detect_pockets=detect_pockets)
def dock(self, protein_file, ligand_file):
"""Docks using Vina and RF."""
protein_docked, ligand_docked = self.pose_generator.generate_poses(
protein_file, ligand_file)
score = self.pose_scorer.score(protein_docked, ligand_docked)
return (score, (protein_docked, ligand_docked))
def __init__(self, exhaustiveness=10, detect_pockets=False):
"""Builds model."""
self.base_dir = tempfile.mkdtemp()
logger.info("About to download trained model.")
call((
"wget -nv -c http://deepchem.io.s3-website-us-west-1.amazonaws.com/trained_models/random_full_RF.tar.gz"
).split())
call(("tar -zxvf random_full_RF.tar.gz").split())
call(("mv random_full_RF %s" % (self.base_dir)).split())
self.model_dir = os.path.join(self.base_dir, "random_full_RF")
# Fit model on dataset
model = SklearnModel(model_dir=self.model_dir)
model.reload()
self.pose_scorer = GridPoseScorer(model, feat="grid")
self.pose_generator = VinaPoseGenerator(exhaustiveness=exhaustiveness,
detect_pockets=detect_pockets)
def __init__(self, exhaustiveness=10, detect_pockets=False):
"""Builds model."""
self.base_dir = tempfile.mkdtemp()
print("About to download trained model.")
call(("wget -c http://deepchem.io.s3-website-us-west-1.amazonaws.com/trained_models/random_full_RF.tar.gz").split())
call(("tar -zxvf random_full_RF.tar.gz").split())
call(("mv random_full_RF %s" % (self.base_dir)).split())
self.model_dir = os.path.join(self.base_dir, "random_full_RF")
# Fit model on dataset
model = SklearnModel(model_dir=self.model_dir)
model.reload()
self.pose_scorer = GridPoseScorer(model, feat="grid")
self.pose_generator = VinaPoseGenerator(
exhaustiveness=exhaustiveness, detect_pockets=detect_pockets)
class VinaGridDNNDocker(object):
"""Vina pose-generation, DNN-models on grid-featurization of complexes."""
def __init__(self, exhaustiveness=10, detect_pockets=False):
"""Builds model."""
self.base_dir = tempfile.mkdtemp()
print("About to download trained model.")
call((
"wget -nv -c http://deepchem.io.s3-website-us-west-1.amazonaws.com/trained_models/random_full_DNN.tar.gz"
).split())
call(("tar -zxvf random_full_DNN.tar.gz").split())
call(("mv random_full_DNN %s" % (self.base_dir)).split())
self.model_dir = os.path.join(self.base_dir, "random_full_DNN")
# Fit model on dataset
pdbbind_tasks = ["-logKd/Ki"]
n_features = 2052
model = TensorflowMultiTaskRegressor(len(pdbbind_tasks),
n_features,
logdir=self.model_dir,
dropouts=[.25],
learning_rate=0.0003,
weight_init_stddevs=[.1],
batch_size=64)
model.reload()
self.pose_scorer = GridPoseScorer(model, feat="grid")
self.pose_generator = VinaPoseGenerator(exhaustiveness=exhaustiveness,
detect_pockets=detect_pockets)
def dock(self,
protein_file,
ligand_file,
centroid=None,
box_dims=None,
dry_run=False):
"""Docks using Vina and DNNs."""
protein_docked, ligand_docked = self.pose_generator.generate_poses(
protein_file, ligand_file, centroid, box_dims, dry_run)
if not dry_run:
score = self.pose_scorer.score(protein_docked, ligand_docked)
else:
score = np.zeros((1, ))
return (score, (protein_docked, ligand_docked))