model.fit(train_dataset)
#Append trains cores and results
train_scores = model.evaluate(
train_dataset,
[metric, dc.metrics.Metric(dc.metrics.mae_score)])
train_results = np.concatenate(
(train_results, list(train_scores.values())))
valid_scores = model.evaluate(
valid_dataset,
[metric, dc.metrics.Metric(dc.metrics.mae_score)])
test_results = np.concatenate((test_results, list(valid_scores.values())))
#Append trains cores and results
predict_train = pd.DataFrame(
model.predict(train_dataset),
columns=['prediction']).to_csv(modeldir + "predict_train_" +
str(estimator) + '.csv')
predict_valid = pd.DataFrame(
model.predict(valid_dataset),
columns=['prediction']).to_csv(modeldir + "predict_validation_" +
str(estimator) + '.csv')
#Append measures
estimators = np.concatenate((estimators, [estimator, estimator]))
metrics = np.concatenate((metrics, ["r2", "mse"]))
# Fit trained model
model.save()
print("Dataframe for n_estimator selection")
df = pd.DataFrame({
metric = dc.metrics.Metric(dc.metrics.pearson_r2_score, np.median)
# Do setup required for tf/keras models
sklmodel = KernelRidge(kernel="rbf", alpha=1e-5, gamma=0.05)
model = SklearnModel(sklmodel, modeldir)
# Fit trained model
print("Fitting model")
model.fit(train_dataset)
model.save()
print("Evaluating model")
train_scores = model.evaluate(train_dataset, [metric, dc.metrics.Metric(dc.metrics.mae_score)])
= model.evaluate(valid_dataset, [metric, dc.metrics.Metric(dc.metrics.mae_score)])
#Error kernel
predict_train = pd.DataFrame(model.predict(train_dataset), columns=['prediction']).to_csv(modeldir + "predict_train.csv")
predict_valid = pd.DataFrame(model.predict(valid_dataset), columns=['prediction']).to_csv(modeldir + "predict_validation.csv")
print("Train scores")
print(train_scores)
print("Validation scores")
print(valid_scores)
metrics = np.concatenate(([], ["r2","mse"]))
print("Dataframe for n_estimator selection")
df = pd.DataFrame({'metrics': metrics,
'train_scores': list(train_scores.values()),
'result_scores': list(valid_scores.values())})
df.to_csv(modeldir + "KRRAnalysis.csv", index= False)
class RFConvexHullPocketFinder(BindingPocketFinder):
"""Uses pre-trained RF model + ConvexHulPocketFinder to select pockets."""
def __init__(self, pad=5):
self.pad = pad
self.convex_finder = ConvexHullPocketFinder(pad)
# Load binding pocket model
self.base_dir = tempfile.mkdtemp()
print("About to download trained model.")
# TODO(rbharath): Shift refined to full once trained.
call(("wget -c http://deepchem.io.s3-website-us-west-1.amazonaws.com/trained_models/pocket_random_refined_RF.tar.gz").split())
call(("tar -zxvf pocket_random_refined_RF.tar.gz").split())
call(("mv pocket_random_refined_RF %s" % (self.base_dir)).split())
self.model_dir = os.path.join(self.base_dir, "pocket_random_refined_RF")
# Fit model on dataset
self.model = SklearnModel(model_dir=self.model_dir)
self.model.reload()
# Create featurizers
self.pocket_featurizer = BindingPocketFeaturizer()
self.ligand_featurizer = CircularFingerprint(size=1024)
def find_pockets(self, protein_file, ligand_file):
"""Compute features for a given complex
TODO(rbharath): This has a log of code overlap with
compute_binding_pocket_features in
examples/binding_pockets/binding_pocket_datasets.py. Find way to refactor
to avoid code duplication.
"""
if not ligand_file.endswith(".sdf"):
raise ValueError("Only .sdf ligand files can be featurized.")
ligand_basename = os.path.basename(ligand_file).split(".")[0]
ligand_mol2 = os.path.join(
self.base_dir, ligand_basename + ".mol2")
# Write mol2 file for ligand
obConversion = ob.OBConversion()
conv_out = obConversion.SetInAndOutFormats(str("sdf"), str("mol2"))
ob_mol = ob.OBMol()
obConversion.ReadFile(ob_mol, str(ligand_file))
obConversion.WriteFile(ob_mol, str(ligand_mol2))
# Featurize ligand
mol = Chem.MolFromMol2File(str(ligand_mol2), removeHs=False)
if mol is None:
return None, None
# Default for CircularFingerprint
n_ligand_features = 1024
ligand_features = self.ligand_featurizer.featurize([mol])
# Featurize pocket
pockets, pocket_atoms_map, pocket_coords = self.convex_finder.find_pockets(
protein_file, ligand_file)
n_pockets = len(pockets)
n_pocket_features = BindingPocketFeaturizer.n_features
features = np.zeros((n_pockets, n_pocket_features+n_ligand_features))
pocket_features = self.pocket_featurizer.featurize(
protein_file, pockets, pocket_atoms_map, pocket_coords)
# Note broadcast operation
features[:, :n_pocket_features] = pocket_features
features[:, n_pocket_features:] = ligand_features
dataset = NumpyDataset(X=features)
pocket_preds = self.model.predict(dataset)
pocket_pred_proba = np.squeeze(self.model.predict_proba(dataset))
# Find pockets which are active
active_pockets = []
active_pocket_atoms_map = {}
active_pocket_coords = []
for pocket_ind in range(len(pockets)):
#################################################### DEBUG
# TODO(rbharath): For now, using a weak cutoff. Fix later.
#if pocket_preds[pocket_ind] == 1:
if pocket_pred_proba[pocket_ind][1] > .15:
#################################################### DEBUG
pocket = pockets[pocket_ind]
active_pockets.append(pocket)
active_pocket_atoms_map[pocket] = pocket_atoms_map[pocket]
active_pocket_coords.append(pocket_coords[pocket_ind])
return active_pockets, active_pocket_atoms_map, active_pocket_coords
class RFConvexHullPocketFinder(BindingPocketFinder):
"""Uses pre-trained RF model + ConvexHulPocketFinder to select pockets."""
def __init__(self, pad=5):
self.pad = pad
self.convex_finder = ConvexHullPocketFinder(pad)
# Load binding pocket model
self.base_dir = tempfile.mkdtemp()
print("About to download trained model.")
# TODO(rbharath): Shift refined to full once trained.
call((
"wget -c http://deepchem.io.s3-website-us-west-1.amazonaws.com/trained_models/pocket_random_refined_RF.tar.gz"
).split())
call(("tar -zxvf pocket_random_refined_RF.tar.gz").split())
call(("mv pocket_random_refined_RF %s" % (self.base_dir)).split())
self.model_dir = os.path.join(self.base_dir,
"pocket_random_refined_RF")
# Fit model on dataset
self.model = SklearnModel(model_dir=self.model_dir)
self.model.reload()
# Create featurizers
self.pocket_featurizer = BindingPocketFeaturizer()
self.ligand_featurizer = CircularFingerprint(size=1024)
def find_pockets(self, protein_file, ligand_file):
"""Compute features for a given complex
TODO(rbharath): This has a log of code overlap with
compute_binding_pocket_features in
examples/binding_pockets/binding_pocket_datasets.py. Find way to refactor
to avoid code duplication.
"""
if not ligand_file.endswith(".sdf"):
raise ValueError("Only .sdf ligand files can be featurized.")
ligand_basename = os.path.basename(ligand_file).split(".")[0]
ligand_mol2 = os.path.join(self.base_dir, ligand_basename + ".mol2")
# Write mol2 file for ligand
obConversion = ob.OBConversion()
conv_out = obConversion.SetInAndOutFormats(str("sdf"), str("mol2"))
ob_mol = ob.OBMol()
obConversion.ReadFile(ob_mol, str(ligand_file))
obConversion.WriteFile(ob_mol, str(ligand_mol2))
# Featurize ligand
mol = Chem.MolFromMol2File(str(ligand_mol2), removeHs=False)
if mol is None:
return None, None
# Default for CircularFingerprint
n_ligand_features = 1024
ligand_features = self.ligand_featurizer.featurize([mol])
# Featurize pocket
pockets, pocket_atoms_map, pocket_coords = self.convex_finder.find_pockets(
protein_file, ligand_file)
n_pockets = len(pockets)
n_pocket_features = BindingPocketFeaturizer.n_features
features = np.zeros((n_pockets, n_pocket_features + n_ligand_features))
pocket_features = self.pocket_featurizer.featurize(
protein_file, pockets, pocket_atoms_map, pocket_coords)
# Note broadcast operation
features[:, :n_pocket_features] = pocket_features
features[:, n_pocket_features:] = ligand_features
dataset = NumpyDataset(X=features)
pocket_preds = self.model.predict(dataset)
pocket_pred_proba = np.squeeze(self.model.predict_proba(dataset))
# Find pockets which are active
active_pockets = []
active_pocket_atoms_map = {}
active_pocket_coords = []
for pocket_ind in range(len(pockets)):
#################################################### DEBUG
# TODO(rbharath): For now, using a weak cutoff. Fix later.
#if pocket_preds[pocket_ind] == 1:
if pocket_pred_proba[pocket_ind][1] > .15:
#################################################### DEBUG
pocket = pockets[pocket_ind]
active_pockets.append(pocket)
active_pocket_atoms_map[pocket] = pocket_atoms_map[pocket]
active_pocket_coords.append(pocket_coords[pocket_ind])
return active_pockets, active_pocket_atoms_map, active_pocket_coords