def test_default_featurizer(self):
smiles = ["C1=CC=CN=C1", "O=C(NCc1cc(OC)c(O)cc1)CCCC/C=C/C(C)C"]
featurizer = MolGraphConvFeaturizer()
graph_feat = featurizer.featurize(smiles)
assert len(graph_feat) == 2
# assert "C1=CC=CN=C1"
assert graph_feat[0].num_nodes == 6
assert graph_feat[0].num_node_features == 30
assert graph_feat[0].num_edges == 12
# assert "O=C(NCc1cc(OC)c(O)cc1)CCCC/C=C/C(C)C"
assert graph_feat[1].num_nodes == 22
assert graph_feat[1].num_node_features == 30
assert graph_feat[1].num_edges == 44
def test_featurizer_with_use_partial_charge(self):
smiles = ["C1=CC=CN=C1", "O=C(NCc1cc(OC)c(O)cc1)CCCC/C=C/C(C)C"]
featurizer = MolGraphConvFeaturizer(use_partial_charge=True)
graph_feat = featurizer.featurize(smiles)
assert len(graph_feat) == 2
# assert "C1=CC=CN=C1"
assert graph_feat[0].num_nodes == 6
assert graph_feat[0].num_node_features == 31
assert graph_feat[0].num_edges == 12
# assert "O=C(NCc1cc(OC)c(O)cc1)CCCC/C=C/C(C)C"
assert graph_feat[1].num_nodes == 22
assert graph_feat[1].num_node_features == 31
assert graph_feat[1].num_edges == 44
def test_mpnn_classification():
# load datasets
featurizer = MolGraphConvFeaturizer(use_edges=True)
tasks, dataset, transformers, metric = get_dataset('classification',
featurizer=featurizer)
# initialize models
n_tasks = len(tasks)
model = MPNNModel(mode='classification',
n_tasks=n_tasks,
learning_rate=0.0005)
# overfit test
model.fit(dataset, nb_epoch=200)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean-roc_auc_score'] >= 0.85
# test on a small MoleculeNet dataset
from deepchem.molnet import load_bace_classification
tasks, all_dataset, transformers = load_bace_classification(
featurizer=featurizer)
train_set, _, _ = all_dataset
model = MPNNModel(mode='classification',
n_tasks=len(tasks),
node_out_feats=2,
edge_hidden_feats=2,
num_step_message_passing=1,
num_step_set2set=1,
num_layer_set2set=1)
model.fit(train_set, nb_epoch=1)
def test_attentivefp_classification():
# load datasets
featurizer = MolGraphConvFeaturizer(use_edges=True)
tasks, dataset, transformers, metric = get_dataset(
'classification', featurizer=featurizer)
# initialize models
n_tasks = len(tasks)
model = AttentiveFPModel(
mode='classification',
n_tasks=n_tasks,
batch_size=10,
learning_rate=0.001)
# overfit test
model.fit(dataset, nb_epoch=100)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean-roc_auc_score'] >= 0.85
# test on a small MoleculeNet dataset
from deepchem.molnet import load_bace_classification
tasks, all_dataset, transformers = load_bace_classification(
featurizer=featurizer)
train_set, _, _ = all_dataset
model = AttentiveFPModel(
mode='classification',
n_tasks=len(tasks),
num_layers=1,
num_timesteps=1,
graph_feat_size=2)
model.fit(train_set, nb_epoch=1)
def test_mpnn_regression():
# load datasets
featurizer = MolGraphConvFeaturizer(use_edges=True)
tasks, dataset, transformers, metric = get_dataset('regression',
featurizer=featurizer)
# initialize models
n_tasks = len(tasks)
model = MPNNModel(mode='regression', n_tasks=n_tasks, batch_size=10)
# overfit test
model.fit(dataset, nb_epoch=400)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean_absolute_error'] < 0.5
# test on a small MoleculeNet dataset
from deepchem.molnet import load_delaney
tasks, all_dataset, transformers = load_delaney(featurizer=featurizer)
train_set, _, _ = all_dataset
model = MPNNModel(mode='regression',
n_tasks=len(tasks),
node_out_feats=2,
edge_hidden_feats=2,
num_step_message_passing=1,
num_step_set2set=1,
num_layer_set2set=1)
model.fit(train_set, nb_epoch=1)
def test_gcn_regression():
# load datasets
featurizer = MolGraphConvFeaturizer()
tasks, dataset, transformers, metric = get_dataset('regression',
featurizer=featurizer)
# initialize models
n_tasks = len(tasks)
model = GCNModel(mode='regression',
n_tasks=n_tasks,
number_atom_features=30,
batch_size=10,
learning_rate=0.003)
# overfit test
model.fit(dataset, nb_epoch=300)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean_absolute_error'] < 0.5
# test on a small MoleculeNet dataset
from deepchem.molnet import load_delaney
tasks, all_dataset, transformers = load_delaney(featurizer=featurizer)
train_set, _, _ = all_dataset
model = dc.models.GCNModel(n_tasks=len(tasks),
graph_conv_layers=[2],
residual=False,
predictor_hidden_feats=2)
model.fit(train_set, nb_epoch=1)
def test_gat_classification():
# load datasets
featurizer = MolGraphConvFeaturizer()
tasks, dataset, transformers, metric = get_dataset('classification',
featurizer=featurizer)
# initialize models
n_tasks = len(tasks)
model = GATModel(mode='classification',
n_tasks=n_tasks,
number_atom_features=30,
batch_size=10,
learning_rate=0.001)
# overfit test
model.fit(dataset, nb_epoch=100)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean-roc_auc_score'] >= 0.85
# test on a small MoleculeNet dataset
from deepchem.molnet import load_bace_classification
tasks, all_dataset, transformers = load_bace_classification(
featurizer=featurizer)
train_set, _, _ = all_dataset
model = dc.models.GATModel(mode='classification',
n_tasks=len(tasks),
graph_attention_layers=[2],
n_attention_heads=1,
residual=False,
predictor_hidden_feats=2)
model.fit(train_set, nb_epoch=1)
def test_gcn_reload():
# load datasets
featurizer = MolGraphConvFeaturizer()
tasks, dataset, transformers, metric = get_dataset('classification',
featurizer=featurizer)
# initialize models
n_tasks = len(tasks)
model_dir = tempfile.mkdtemp()
model = GCNModel(mode='classification',
n_tasks=n_tasks,
number_atom_features=30,
model_dir=model_dir,
batch_size=10,
learning_rate=0.0003)
model.fit(dataset, nb_epoch=70)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean-roc_auc_score'] >= 0.85
reloaded_model = GCNModel(mode='classification',
n_tasks=n_tasks,
number_atom_features=30,
model_dir=model_dir,
batch_size=10,
learning_rate=0.0003)
reloaded_model.restore()
pred_mols = ["CCCC", "CCCCCO", "CCCCC"]
X_pred = featurizer(pred_mols)
random_dataset = dc.data.NumpyDataset(X_pred)
original_pred = model.predict(random_dataset)
reload_pred = reloaded_model.predict(random_dataset)
assert np.all(original_pred == reload_pred)
def test_attentivefp_regression():
# load datasets
featurizer = MolGraphConvFeaturizer(use_edges=True)
tasks, dataset, transformers, metric = get_dataset(
'regression', featurizer=featurizer)
# initialize models
n_tasks = len(tasks)
model = AttentiveFPModel(mode='regression', n_tasks=n_tasks, batch_size=10)
# overfit test
model.fit(dataset, nb_epoch=100)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean_absolute_error'] < 0.5
# test on a small MoleculeNet dataset
from deepchem.molnet import load_delaney
tasks, all_dataset, transformers = load_delaney(featurizer=featurizer)
train_set, _, _ = all_dataset
model = AttentiveFPModel(
mode='regression',
n_tasks=len(tasks),
num_layers=1,
num_timesteps=1,
graph_feat_size=2)
model.fit(train_set, nb_epoch=1)
def test_featurizer_with_self_loop(self):
smiles = ["C1=CC=CN=C1", "O=C(NCc1cc(OC)c(O)cc1)CCCC/C=C/C(C)C"]
featurizer = MolGraphConvFeaturizer(add_self_edges=True)
graph_feat = featurizer.featurize(smiles)
assert len(graph_feat) == 2
# assert "C1=CC=CN=C1"
assert graph_feat[0].num_nodes == 6
assert graph_feat[0].num_node_features == 39
assert graph_feat[0].num_edges == 12 + 6
assert graph_feat[0].num_edge_features == 11
# assert "O=C(NCc1cc(OC)c(O)cc1)CCCC/C=C/C(C)C"
assert graph_feat[1].num_nodes == 22
assert graph_feat[1].num_node_features == 39
assert graph_feat[1].num_edges == 44 + 22
assert graph_feat[1].num_edge_features == 11
def test_attentivefp_regression():
# load datasets
featurizer = MolGraphConvFeaturizer(use_edges=True)
tasks, dataset, transformers, metric = get_dataset('regression',
featurizer=featurizer)
# initialize models
n_tasks = len(tasks)
model = AttentiveFPModel(mode='regression', n_tasks=n_tasks, batch_size=10)
# overfit test
model.fit(dataset, nb_epoch=100)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean_absolute_error'] < 0.5
def test_gat_regression():
# load datasets
featurizer = MolGraphConvFeaturizer()
tasks, dataset, transformers, metric = get_dataset('regression',
featurizer=featurizer)
# initialize models
n_tasks = len(tasks)
model = GATModel(mode='regression', n_tasks=n_tasks, batch_size=10)
# overfit test
# GAT's convergence is a little slow
model.fit(dataset, nb_epoch=300)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean_absolute_error'] < 0.5
def test_attentivefp_classification():
# load datasets
featurizer = MolGraphConvFeaturizer(use_edges=True)
tasks, dataset, transformers, metric = get_dataset('classification',
featurizer=featurizer)
# initialize models
n_tasks = len(tasks)
model = AttentiveFPModel(mode='classification',
n_tasks=n_tasks,
batch_size=10,
learning_rate=0.001)
# overfit test
model.fit(dataset, nb_epoch=100)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean-roc_auc_score'] >= 0.85
def test_gcn_regression():
# load datasets
featurizer = MolGraphConvFeaturizer()
tasks, dataset, transformers, metric = get_dataset('regression',
featurizer=featurizer)
# initialize models
n_tasks = len(tasks)
model = GCNModel(mode='regression',
n_tasks=n_tasks,
number_atom_features=30,
batch_size=10)
# overfit test
model.fit(dataset, nb_epoch=100)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean_absolute_error'] < 0.5
def load_dataset(args):
splitter = 'scaffold'
if args['featurizer'] == 'ECFP':
featurizer = 'ECFP'
elif args['featurizer'] == 'GC':
from deepchem.feat import MolGraphConvFeaturizer
featurizer = MolGraphConvFeaturizer()
if args['dataset'] == 'BACE_classification':
from deepchem.molnet import load_bace_classification
tasks, all_dataset, transformers = load_bace_classification(
featurizer=featurizer, splitter=splitter, reload=False)
elif args['dataset'] == 'BBBP':
from deepchem.molnet import load_bbbp
tasks, all_dataset, transformers = load_bbbp(
featurizer=featurizer, splitter=splitter, reload=False)
elif args['dataset'] == 'BACE_regression':
from deepchem.molnet import load_bace_regression
tasks, all_dataset, transformers = load_bace_regression(
featurizer=featurizer, splitter=splitter, reload=False)
elif args['dataset'] == 'ClinTox':
from deepchem.molnet import load_clintox
tasks, all_dataset, transformers = load_clintox(
featurizer=featurizer, splitter=splitter, reload=False)
elif args['dataset'] == 'Delaney':
from deepchem.molnet import load_delaney
tasks, all_dataset, transformers = load_delaney(
featurizer=featurizer, splitter=splitter, reload=False)
elif args['dataset'] == 'HOPV':
from deepchem.molnet import load_hopv
tasks, all_dataset, transformers = load_hopv(
featurizer=featurizer, splitter=splitter, reload=False)
elif args['dataset'] == 'SIDER':
from deepchem.molnet import load_sider
tasks, all_dataset, transformers = load_sider(
featurizer=featurizer, splitter=splitter, reload=False)
elif args['dataset'] == 'Lipo':
from deepchem.molnet import load_lipo
tasks, all_dataset, transformers = load_lipo(
featurizer=featurizer, splitter=splitter, reload=False)
else:
raise ValueError('Unexpected dataset: {}'.format(args['dataset']))
return args, tasks, all_dataset, transformers
def test_gcn_classification():
# load datasets
featurizer = MolGraphConvFeaturizer()
tasks, dataset, transformers, metric = get_dataset('classification',
featurizer=featurizer)
# initialize models
n_tasks = len(tasks)
model = GCNModel(mode='classification',
n_tasks=n_tasks,
number_atom_features=30,
batch_size=10,
learning_rate=0.0003)
# overfit test
model.fit(dataset, nb_epoch=70)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean-roc_auc_score'] >= 0.85
def test_gat_classification():
# load datasets
featurizer = MolGraphConvFeaturizer()
tasks, dataset, transformers, metric = get_dataset('classification',
featurizer=featurizer)
# initialize models
n_tasks = len(tasks)
model = GATModel(mode='classification',
n_tasks=n_tasks,
batch_size=10,
learning_rate=0.001)
# overfit test
# GAT's convergence is a little slow
model.fit(dataset, nb_epoch=150)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean-roc_auc_score'] >= 0.85
def test_gat_classification():
# load datasets
featurizer = MolGraphConvFeaturizer()
tasks, dataset, transformers, metric = get_dataset('regression',
featurizer=featurizer)
# initialize models
n_tasks = len(tasks)
model = GATModel(n_tasks=n_tasks,
loss=losses.L2Loss(),
batch_size=4,
learning_rate=0.001)
# overfit test
model.fit(dataset, nb_epoch=100)
scores = model.evaluate(dataset, [metric], transformers)
# TODO: check this asseration is correct or not
assert scores['mean_absolute_error'] < 1.0
def load_dataset(args):
splitter = 'scaffold'
if args['featurizer'] == 'ECFP':
featurizer = 'ECFP'
elif args['featurizer'] == 'GC':
from deepchem.feat import MolGraphConvFeaturizer
featurizer = MolGraphConvFeaturizer()
elif args['featurizer'] == 'AC':
from deepchem.feat import AtomicConvFeaturizer
featurizer = AtomicConvFeaturizer(frag1_num_atoms=100,
frag2_num_atoms=1000,
complex_num_atoms=1100,
max_num_neighbors=12,
neighbor_cutoff=4)
if args['dataset'] == 'BACE_classification':
from deepchem.molnet import load_bace_classification
tasks, all_dataset, transformers = load_bace_classification(
featurizer=featurizer, splitter=splitter, reload=False)
elif args['dataset'] == 'BBBP':
from deepchem.molnet import load_bbbp
tasks, all_dataset, transformers = load_bbbp(featurizer=featurizer,
splitter=splitter,
reload=False)
elif args['dataset'] == 'BACE_regression':
from deepchem.molnet import load_bace_regression
tasks, all_dataset, transformers = load_bace_regression(
featurizer=featurizer, splitter=splitter, reload=False)
elif args['dataset'] == 'ClinTox':
from deepchem.molnet import load_clintox
tasks, all_dataset, transformers = load_clintox(featurizer=featurizer,
splitter=splitter,
reload=False)
elif args['dataset'] == 'Delaney':
from deepchem.molnet import load_delaney
tasks, all_dataset, transformers = load_delaney(featurizer=featurizer,
splitter=splitter,
reload=False)
elif args['dataset'] == 'HOPV':
from deepchem.molnet import load_hopv
tasks, all_dataset, transformers = load_hopv(featurizer=featurizer,
splitter=splitter,
reload=False)
elif args['dataset'] == 'SIDER':
from deepchem.molnet import load_sider
tasks, all_dataset, transformers = load_sider(featurizer=featurizer,
splitter=splitter,
reload=False)
elif args['dataset'] == 'Lipo':
from deepchem.molnet import load_lipo
tasks, all_dataset, transformers = load_lipo(featurizer=featurizer,
splitter=splitter,
reload=False)
elif args['dataset'] == 'PDBbind':
from deepchem.molnet import load_pdbbind
tasks, all_dataset, transformers = load_pdbbind(
featurizer=featurizer,
save_dir='.',
data_dir='.',
splitter='random',
pocket=True,
set_name='core', # refined
reload=False)
else:
raise ValueError('Unexpected dataset: {}'.format(args['dataset']))
return args, tasks, all_dataset, transformers