def test_nnscore_desc():
"""Test NNScore descriptors generators"""
mols = list(oddt.toolkit.readfile('sdf', actives_sdf))
list(map(lambda x: x.addh(only_polar=True), mols))
rec = next(oddt.toolkit.readfile('pdb', receptor_pdb))
rec.protein = True
rec.addh(only_polar=True)
# Delete molecule which has differences in Acceptor-Donor def in RDK and OB
del mols[65]
gen = nnscore(protein=rec).descriptor_generator
descs = gen.build(mols)
# save correct results (for future use)
# np.savetxt(os.path.join(results, 'nnscore_descs.csv'),
# descs,
# fmt='%.16g',
# delimiter=',')
if oddt.toolkit.backend == 'ob':
descs_correct = np.loadtxt(os.path.join(results, 'nnscore_descs_ob.csv'),
delimiter=',')
else:
descs_correct = np.loadtxt(os.path.join(results, 'nnscore_descs_rdk.csv'),
delimiter=',')
# help debug errors
for i in range(descs.shape[1]):
mask = np.abs(descs[:, i] - descs_correct[:, i]) > 1e-4
if mask.sum() > 1:
print(i, gen.titles[i], mask.sum())
print(np.vstack((descs[mask, i], descs_correct[mask, i])))
assert_array_almost_equal(descs, descs_correct, decimal=4)
def test_model_train():
mols = list(oddt.toolkit.readfile('sdf', actives_sdf))[:10]
list(map(lambda x: x.addh(), mols))
rec = next(oddt.toolkit.readfile('pdb', receptor_pdb))
rec.protein = True
rec.addh()
data_dir = os.path.join(test_data_dir, 'data')
home_dir = mkdtemp()
pdbbind_versions = (2007, 2013, 2016)
pdbbind_dir = os.path.join(data_dir, 'pdbbind')
for pdbbind_v in pdbbind_versions:
version_dir = os.path.join(data_dir, 'v%s' % pdbbind_v)
if not os.path.isdir(version_dir):
os.symlink(pdbbind_dir, version_dir)
for model in [nnscore(n_jobs=1)] + [rfscore(version=v, n_jobs=1)
for v in [1, 2, 3]]:
with NamedTemporaryFile(suffix='.pickle') as f:
model.gen_training_data(data_dir, pdbbind_versions=pdbbind_versions,
home_dir=home_dir)
model.train(home_dir=home_dir, sf_pickle=f.name)
model.set_protein(rec)
preds = model.predict(mols)
assert len(preds) == 10
assert preds.dtype == np.float
assert model.score(mols, preds) == 1.0
for pdbbind_v in pdbbind_versions:
version_dir = os.path.join(data_dir, 'v%s' % pdbbind_v)
if os.path.islink(version_dir):
os.unlink(version_dir)
def test_nnscore():
"""Test NNScore descriptors generators"""
mols = list(oddt.toolkit.readfile('sdf', os.path.join(test_data_dir, 'data/dude/xiap/actives_docked.sdf')))
list(map(lambda x: x.addh(only_polar=True), mols))
rec = next(oddt.toolkit.readfile('pdb', os.path.join(test_data_dir, 'data/dude/xiap/receptor_rdkit.pdb')))
rec.protein = True
rec.addh(only_polar=True)
# Delete molecule which has differences in Acceptor-Donor def in RDK and OB
del mols[65]
# print((rec.atom_dict['atomicnum'] == 1).sum(),
# rec.atom_dict['isdonor'].sum(),
# rec.atom_dict['isdonorh'].sum())
# for mol in mols:
# print(mol.num_rotors)
# print(sum(atom.Atom.GetAtomicNum() == 1 for atom in mol.atoms))
# print((mol.atom_dict['atomicnum'] == 1).sum(),
# mol.atom_dict['isdonor'].sum(),
# mol.atom_dict['isdonorh'].sum())
gen = nnscore(protein=rec).descriptor_generator
descs = gen.build(mols)
# save correct results (for future use)
# np.savetxt(os.path.join(test_data_dir,
# 'data/results/xiap/nnscore_descs.csv'),
# descs,
# fmt='%.16g',
# delimiter=',')
descs_correct = np.loadtxt(os.path.join(test_data_dir, 'data/results/xiap/nnscore_descs.csv'), delimiter=',')
# help debug errors
for i in range(descs.shape[1]):
mask = np.abs(descs[:, i] - descs_correct[:, i]) > 1e-4
if mask.sum() > 1:
print(i, gen.titles[i], mask.sum())
print(np.vstack((descs[mask, i], descs_correct[mask, i])))
assert_array_almost_equal(descs, descs_correct, decimal=4)
def test_vs_scoring():
protein = next(oddt.toolkit.readfile('pdb', xiap_protein))
protein.protein = True
data_dir = os.path.join(test_data_dir, 'data')
home_dir = mkdtemp()
pdbbind_versions = (2007, 2013, 2016)
pdbbind_dir = os.path.join(data_dir, 'pdbbind')
for pdbbind_v in pdbbind_versions:
version_dir = os.path.join(data_dir, 'v%s' % pdbbind_v)
if not os.path.isdir(version_dir):
os.symlink(pdbbind_dir, version_dir)
filenames = []
# train mocked SFs
for model in [nnscore(n_jobs=1)
] + [rfscore(version=v, n_jobs=1) for v in [1, 2, 3]]:
model.gen_training_data(data_dir,
pdbbind_versions=pdbbind_versions,
home_dir=home_dir)
filenames.append(model.train(home_dir=home_dir))
vs = virtualscreening(n_cpu=-1, chunksize=10)
vs.load_ligands('sdf', xiap_actives_docked)
# error if no protein is fed
with pytest.raises(ValueError):
vs.score('nnscore')
# bad sf name
with pytest.raises(ValueError):
vs.score('bad_sf', protein=protein)
vs.score('nnscore', protein=xiap_protein)
vs.score('nnscore_pdbbind2016', protein=protein)
vs.score('rfscore_v1', protein=protein)
vs.score('rfscore_v1_pdbbind2016', protein=protein)
vs.score('rfscore_v2', protein=protein)
vs.score('rfscore_v3', protein=protein)
# use pickle directly
vs.score(filenames[0], protein=protein)
# pass SF object directly
vs.score(scorer.load(filenames[0]), protein=protein)
# pass wrong object (sum is not an instance of scorer)
with pytest.raises(ValueError):
vs.score(sum, protein=protein)
mols = list(vs.fetch())
assert len(mols) == 100
mol_data = mols[0].data
assert 'nnscore' in mol_data
assert 'rfscore_v1' in mol_data
assert 'rfscore_v2' in mol_data
assert 'rfscore_v3' in mol_data
vs = virtualscreening(n_cpu=-1, chunksize=10)
vs.load_ligands('sdf', xiap_actives_docked)
vs.score('nnscore', protein=protein)
vs.score('rfscore_v1', protein=protein)
vs.score('rfscore_v2', protein=protein)
vs.score('rfscore_v3', protein=protein)
with NamedTemporaryFile('w', suffix='.sdf') as molfile:
with NamedTemporaryFile('w', suffix='.csv') as csvfile:
vs.write('sdf', molfile.name, csv_filename=csvfile.name)
data = pd.read_csv(csvfile.name)
assert 'nnscore' in data.columns
assert 'rfscore_v1' in data.columns
assert 'rfscore_v2' in data.columns
assert 'rfscore_v3' in data.columns
mols = list(oddt.toolkit.readfile('sdf', molfile.name))
assert len(mols) == 100
vs.write_csv(
csvfile.name,
fields=['nnscore', 'rfscore_v1', 'rfscore_v2', 'rfscore_v3'])
data = pd.read_csv(csvfile.name)
assert len(data.columns) == 4
assert 'nnscore' in data.columns
assert 'rfscore_v1' in data.columns
assert 'rfscore_v2' in data.columns
assert 'rfscore_v3' in data.columns
# remove files
for f in filenames:
os.unlink(f)
# remove symlinks
for pdbbind_v in pdbbind_versions:
version_dir = os.path.join(data_dir, 'v%s' % pdbbind_v)
if os.path.islink(version_dir):
os.unlink(version_dir)
'nnscore_descs_rdk.csv'),
delimiter=',')
# help debug errors
for i in range(descs.shape[1]):
mask = np.abs(descs[:, i] - descs_correct[:, i]) > 1e-4
if mask.sum() > 1:
print(i, gen.titles[i], mask.sum())
print(np.vstack((descs[mask, i], descs_correct[mask, i])))
assert_array_almost_equal(descs, descs_correct, decimal=4)
models = ([
PLECscore(n_jobs=1, version=v, size=2048) for v in ['linear', 'nn', 'rf']
] + [nnscore(n_jobs=1)] + [rfscore(version=v, n_jobs=1) for v in [1, 2, 3]])
@pytest.mark.parametrize('model', models)
def test_model_train(model):
mols = list(oddt.toolkit.readfile('sdf', actives_sdf))[:10]
list(map(lambda x: x.addh(), mols))
rec = next(oddt.toolkit.readfile('pdb', receptor_pdb))
rec.protein = True
rec.addh()
data_dir = os.path.join(test_data_dir, 'data')
home_dir = mkdtemp()
pdbbind_versions = (2007, 2013, 2016)
def init_nnscore(files):
global NNSCORES
for file in files:
NNSCORES.append(functions.nnscore().load(file))
def test_vs_scoring():
protein = next(oddt.toolkit.readfile('pdb', xiap_protein))
protein.protein = True
data_dir = os.path.join(test_data_dir, 'data')
home_dir = mkdtemp()
pdbbind_versions = (2007, 2013, 2016)
pdbbind_dir = os.path.join(data_dir, 'pdbbind')
for pdbbind_v in pdbbind_versions:
version_dir = os.path.join(data_dir, 'v%s' % pdbbind_v)
if not os.path.isdir(version_dir):
os.symlink(pdbbind_dir, version_dir)
filenames = []
# train mocked SFs
for model in [nnscore(n_jobs=1)] + [rfscore(version=v, n_jobs=1)
for v in [1, 2, 3]]:
model.gen_training_data(data_dir, pdbbind_versions=pdbbind_versions,
home_dir=home_dir)
filenames.append(model.train(home_dir=home_dir))
vs = virtualscreening(n_cpu=-1, chunksize=10)
vs.load_ligands('sdf', xiap_actives_docked)
# error if no protein is fed
with pytest.raises(ValueError):
vs.score('nnscore')
# bad sf name
with pytest.raises(ValueError):
vs.score('bad_sf', protein=protein)
vs.score('nnscore', protein=xiap_protein)
vs.score('nnscore_pdbbind2016', protein=protein)
vs.score('rfscore_v1', protein=protein)
vs.score('rfscore_v1_pdbbind2016', protein=protein)
vs.score('rfscore_v2', protein=protein)
vs.score('rfscore_v3', protein=protein)
vs.score('pleclinear', protein=protein)
vs.score('pleclinear_p5_l1_s65536_pdbbind2016', protein=protein)
# use pickle directly
vs.score(filenames[0], protein=protein)
# pass SF object directly
vs.score(scorer.load(filenames[0]), protein=protein)
# pass wrong object (sum is not an instance of scorer)
with pytest.raises(ValueError):
vs.score(sum, protein=protein)
mols = list(vs.fetch())
assert len(mols) == 100
mol_data = mols[0].data
assert 'nnscore' in mol_data
assert 'rfscore_v1' in mol_data
assert 'rfscore_v2' in mol_data
assert 'rfscore_v3' in mol_data
assert 'PLEClinear_p5_l1_s65536' in mol_data
vs = virtualscreening(n_cpu=-1, chunksize=10)
vs.load_ligands('sdf', xiap_actives_docked)
vs.score('nnscore', protein=protein)
vs.score('rfscore_v1', protein=protein)
vs.score('rfscore_v2', protein=protein)
vs.score('rfscore_v3', protein=protein)
with NamedTemporaryFile('w', suffix='.sdf') as molfile:
with NamedTemporaryFile('w', suffix='.csv') as csvfile:
vs.write('sdf', molfile.name, csv_filename=csvfile.name)
data = pd.read_csv(csvfile.name)
assert 'nnscore' in data.columns
assert 'rfscore_v1' in data.columns
assert 'rfscore_v2' in data.columns
assert 'rfscore_v3' in data.columns
mols = list(oddt.toolkit.readfile('sdf', molfile.name))
assert len(mols) == 100
vs.write_csv(csvfile.name, fields=['nnscore', 'rfscore_v1',
'rfscore_v2', 'rfscore_v3'])
data = pd.read_csv(csvfile.name)
assert len(data.columns) == 4
assert 'nnscore' in data.columns
assert 'rfscore_v1' in data.columns
assert 'rfscore_v2' in data.columns
assert 'rfscore_v3' in data.columns
# remove files
for f in filenames:
os.unlink(f)
# remove symlinks
for pdbbind_v in pdbbind_versions:
version_dir = os.path.join(data_dir, 'v%s' % pdbbind_v)
if os.path.islink(version_dir):
os.unlink(version_dir)