def test_predict_compound_names(self):
try:
self.args.test_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'delaney_toy_smiles_names.csv')
self.args.use_compound_names = True
modify_predict_args(self.args)
make_predictions(self.args)
except:
self.fail('predict_compound_names')
def predict_desc(args):
import chemprop
chemprop_root = os.path.dirname(os.path.dirname(chemprop.__file__))
#trick chemprop
args.test_path = 'foo'
args.preds_path = 'foo'
args.checkpoint_path = os.path.join(chemprop_root, 'trained_model',
'QM_137k.pt')
modify_predict_args(args)
def num_atoms_bonds(smiles):
m = Chem.MolFromSmiles(smiles)
m = Chem.AddHs(m)
return len(m.GetAtoms()), len(m.GetBonds())
# predict descriptors for reactants in the reactions
reactivity_data = pd.read_csv(args.data_path, index_col=0)
reactants = set()
for _, row in reactivity_data.iterrows():
rs, _, _ = row['rxn_smiles'].split('>')
rs = rs.split('.')
for r in rs:
reactants.add(r)
reactants = list(reactants)
print('Predicting descriptors for reactants...')
test_preds, test_smiles = make_predictions(args, smiles=reactants)
partial_charge = test_preds[0]
partial_neu = test_preds[1]
partial_elec = test_preds[2]
NMR = test_preds[3]
bond_order = test_preds[4]
bond_distance = test_preds[5]
n_atoms, n_bonds = zip(*[num_atoms_bonds(x) for x in reactants])
partial_charge = np.split(partial_charge.flatten(),
np.cumsum(np.array(n_atoms)))[:-1]
partial_neu = np.split(partial_neu.flatten(),
np.cumsum(np.array(n_atoms)))[:-1]
partial_elec = np.split(partial_elec.flatten(),
np.cumsum(np.array(n_atoms)))[:-1]
NMR = np.split(NMR.flatten(), np.cumsum(np.array(n_atoms)))[:-1]
bond_order = np.split(bond_order.flatten(),
np.cumsum(np.array(n_bonds)))[:-1]
bond_distance = np.split(bond_distance.flatten(),
np.cumsum(np.array(n_bonds)))[:-1]
df = pd.DataFrame({
'smiles': reactants,
'partial_charge': partial_charge,
'fukui_neu': partial_neu,
'fukui_elec': partial_elec,
'NMR': NMR,
'bond_order': bond_order,
'bond_length': bond_distance
})
invalid = check_chemprop_out(df)
# FIXME remove invalid molecules from reaction dataset
print(invalid)
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
df.to_pickle(os.path.join(args.output_dir, 'reactants_descriptors.pickle'))
save_dir = args.model_dir
if not args.predict:
df, scalers = min_max_normalize(df)
pickle.dump(scalers,
open(os.path.join(save_dir, 'scalers.pickle'), 'wb'))
else:
scalers = pickle.load(
open(os.path.join(save_dir, 'scalers.pickle'), 'rb'))
df, _ = min_max_normalize(df, scalers)
df.to_pickle(
os.path.join(args.output_dir, 'reactants_descriptors_norm.pickle'))
return df
def test_predict(self):
try:
modify_predict_args(self.args)
make_predictions(self.args)
except:
self.fail('predict')
def predict():
"""Renders the predict page and makes predictions if the method is POST."""
if request.method == 'GET':
return render_predict()
# Get arguments
ckpt_id = request.form['checkpointName']
if request.form['textSmiles'] != '':
smiles = request.form['textSmiles'].split()
elif request.form['drawSmiles'] != '':
smiles = [request.form['drawSmiles']]
else:
print(" GOT HERE")
# Upload data file with SMILES
data = request.files['data']
data_name = secure_filename(data.filename)
data_path = os.path.join(app.config['TEMP_FOLDER'], data_name)
data.save(data_path)
# Check if header is smiles
possible_smiles = get_header(data_path)[0]
smiles = [possible_smiles] if Chem.MolFromSmiles(possible_smiles) is not None else []
# Get remaining smiles
smiles.extend(get_smiles(data_path))
models = db.get_models(ckpt_id)
model_paths = [os.path.join(app.config['CHECKPOINT_FOLDER'], f'{model["id"]}.pt') for model in models]
task_names = load_task_names(model_paths[0])
num_tasks = len(task_names)
gpu = request.form.get('gpu')
# Create and modify args
args = load_args(model_paths[0])
if args.features_path != None:
args.features_generator = ["rdkit_2d_normalized"]
args.features_path = None
preds_path = os.path.join(app.config['TEMP_FOLDER'], app.config['PREDICTIONS_FILENAME'])
args.test_path = 'None' # TODO: Remove this hack to avoid assert crashing in modify_predict_args
args.preds_path = preds_path
args.checkpoint_paths = model_paths
if gpu is not None:
if gpu == 'None':
args.no_cuda = True
else:
args.gpu = int(gpu)
modify_predict_args(args)
# Run predictions
preds = make_predictions(args, smiles=smiles)
if all(p is None for p in preds):
return render_predict(errors=['All SMILES are invalid'])
# Replace invalid smiles with message
invalid_smiles_warning = "Invalid SMILES String"
preds = [pred if pred is not None else [invalid_smiles_warning] * num_tasks for pred in preds]
return render_predict(predicted=True,
smiles=smiles,
num_smiles=min(10, len(smiles)),
show_more=max(0, len(smiles)-10),
task_names=task_names,
num_tasks=len(task_names),
preds=preds,
warnings=["List contains invalid SMILES strings"] if None in preds else None,
errors=["No SMILES strings given"] if len(preds) == 0 else None)
def predict():
if request.method == 'GET':
return render_predict()
# Get arguments
checkpoint_name = request.form['checkpointName']
if 'data' in request.files:
# Upload data file with SMILES
data = request.files['data']
data_name = secure_filename(data.filename)
data_path = os.path.join(app.config['TEMP_FOLDER'], data_name)
data.save(data_path)
# Check if header is smiles
possible_smiles = get_header(data_path)[0]
smiles = [possible_smiles
] if Chem.MolFromSmiles(possible_smiles) is not None else []
# Get remaining smiles
smiles.extend(get_smiles(data_path))
elif request.form['textSmiles'] != '':
smiles = request.form['textSmiles'].split()
else:
smiles = [request.form['drawSmiles']]
checkpoint_path = os.path.join(app.config['CHECKPOINT_FOLDER'],
checkpoint_name)
task_names = load_task_names(checkpoint_path)
num_tasks = len(task_names)
gpu = request.form.get('gpu')
# Create and modify args
parser = ArgumentParser()
add_predict_args(parser)
args = parser.parse_args()
preds_path = os.path.join(app.config['TEMP_FOLDER'],
app.config['PREDICTIONS_FILENAME'])
args.test_path = 'None' # TODO: Remove this hack to avoid assert crashing in modify_predict_args
args.preds_path = preds_path
args.checkpoint_path = checkpoint_path
args.write_smiles = True
if gpu is not None:
if gpu == 'None':
args.no_cuda = True
else:
args.gpu = int(gpu)
modify_predict_args(args)
# Run predictions
preds = make_predictions(args, smiles=smiles)
if all(p is None for p in preds):
return render_predict(errors=['All SMILES are invalid'])
# Replace invalid smiles with message
invalid_smiles_warning = "Invalid SMILES String"
preds = [
pred if pred is not None else [invalid_smiles_warning] * num_tasks
for pred in preds
]
return render_predict(
predicted=True,
smiles=smiles,
num_smiles=min(10, len(smiles)),
show_more=max(0,
len(smiles) - 10),
task_names=task_names,
num_tasks=len(task_names),
preds=preds,
warnings=["List contains invalid SMILES strings"]
if None in preds else None,
errors=["No SMILES strings given"] if len(preds) == 0 else None)
