def _setup_graphs_mols(self):
"""Setup graphs and smiles if needed."""
smiles_to_mol = functools.partial(featurization.smiles_to_mol,
infer_hydrogens=True)
tensorizer = featurization.MolTensorizer(preprocess_fn=smiles_to_mol)
smiles = ['CO', 'CCC', 'CN1C=NC2=C1C(=O)N(C(=O)N2C)C']
mols = [smiles_to_mol(smi) for smi in smiles]
graphs = graph_utils.smiles_to_graphs_tuple(smiles, tensorizer)
return graphs, mols
def _setup_graphs(self):
"""Setup graphs and smiles if needed."""
tensorizer = featurization.MolTensorizer()
smiles = [
'CO', 'Cc1occc1C(=O)Nc2ccccc2',
'CC(C)=CCCC(C)=CC(=O)', 'c1ccc2c(c1)ccc3c2ccc4c5ccccc5ccc43', 'c1ccsc1',
'c2ccc1scnc1c2', 'Clc1cc(Cl)c(c(Cl)c1)c2c(Cl)cccc2Cl',
]
graphs = graph_utils.smiles_to_graphs_tuple(smiles, tensorizer)
return graphs, smiles, tensorizer
def _setup_graphs_model(self):
"""Setup graphs and smiles if needed."""
tensorizer = featurization.MolTensorizer()
smiles = ['CO', 'CCC', 'CN1C=NC2=C1C(=O)N(C(=O)N2C)C']
graphs = graph_utils.smiles_to_graphs_tuple(smiles, tensorizer)
# Fix seed so that initialization is deterministic.
tf.random.set_seed(0)
model = experiments.GNN(5, 3, 10, 1, models.BlockType('gcn'), 'relu',
templates.TargetType.globals, 3)
model(graphs)
return graphs, model, tensorizer
def _setup_experiment(self):
"""Setup graphs and smiles if needed."""
smiles = ['CO', 'CCC', 'CN1C=NC2=C1C(=O)N(C(=O)N2C)C']
n = len(smiles)
smiles_to_mol = functools.partial(
featurization.smiles_to_mol, infer_hydrogens=True)
tensorizer = featurization.MolTensorizer(preprocess_fn=smiles_to_mol)
train_index, test_index = np.arange(n - 1), np.arange(n - 1, n)
mol_list = [smiles_to_mol(smi) for smi in smiles]
x = graph_utils.smiles_to_graphs_tuple(smiles, tensorizer)
task = tasks.get_task(tasks.Task.crippen)
y = task.get_true_predictions(mol_list)
atts = task.get_true_attributions(mol_list)
exp = experiments.ExperimentData.from_data_and_splits(
x, y, atts, train_index, test_index)
model = experiments.GNN(5, 3, 10, 1, models.BlockType.gcn, 'relu',
templates.TargetType.globals, 2)
model(x)
method = techniques.CAM()
return exp, model, task, method
def _setup_graphs(self): """Setup graphs and smiles if needed.""" tensorizer = featurization.MolTensorizer() smiles = ['CO', 'CCC', 'CN1C=NC2=C1C(=O)N(C(=O)N2C)C'] return graph_utils.smiles_to_graphs_tuple(smiles, tensorizer)