def remove_invalid(gen, canonize=True, n_jobs=1):
"""
Removes invalid molecules from the dataset
"""
if not canonize:
mols = mapper(n_jobs)(get_mol, gen)
return [gen_ for gen_, mol in zip(gen, mols) if mol is not None]
return [x for x in mapper(n_jobs)(canonic_smiles, gen) if
x is not None]
def compute_intermediate_statistics(smiles, n_jobs=1, device='cpu',
batch_size=512, pool=None):
"""
The function precomputes statistics such as mean and variance for FCD, etc.
It is useful to compute the statistics for test and scaffold test sets to
speedup metrics calculation.
"""
close_pool = False
if pool is None:
if n_jobs != 1:
pool = Pool(n_jobs)
close_pool = True
else:
pool = 1
statistics = {}
mols = mapper(pool)(get_mol, smiles)
kwargs = {'n_jobs': pool, 'device': device, 'batch_size': batch_size}
kwargs_fcd = {'n_jobs': n_jobs, 'device': device, 'batch_size': batch_size}
# statistics['FCD'] = FCDMetric(**kwargs_fcd).precalc(smiles)
statistics['SNN'] = SNNMetric(**kwargs).precalc(mols)
statistics['Frag'] = FragMetric(**kwargs).precalc(mols)
statistics['Scaf'] = ScafMetric(**kwargs).precalc(mols)
for name, func in [('logP', logP), ('SA', SA),
('QED', QED),
('weight', weight)]:
statistics[name] = WassersteinMetric(func, **kwargs).precalc(mols)
if close_pool:
pool.terminate()
return statistics
def novelty(gen, train, n_jobs=1):
"""
tbd
"""
gen_smiles = mapper(n_jobs)(canonic_smiles, gen)
gen_smiles_set = set(gen_smiles) - {None}
train_set = set(train)
return len(gen_smiles_set - train_set) / len(gen_smiles_set)
def fraction_valid(gen, n_jobs=1):
"""
Computes a number of valid molecules
Parameters:
gen: list of SMILES
n_jobs: number of threads for calculation
"""
gen = mapper(n_jobs)(get_mol, gen)
return 1 - gen.count(None) / len(gen)
def fraction_passes_filters(gen, n_jobs=1):
"""
Computes the fraction of molecules that pass filters:
* MCF
* PAINS
* Only allowed atoms ('C','N','S','O','F','Cl','Br','H')
* No charges
"""
passes = mapper(n_jobs)(mol_passes_filters, gen)
return np.mean(passes)
def fraction_unique(gen, k=None, n_jobs=1, check_validity=True):
"""
Computes a number of unique molecules
Parameters:
gen: list of SMILES
k: compute unique@k
n_jobs: number of threads for calculation
check_validity: raises ValueError if invalid molecules are present
"""
if k is not None:
if len(gen) < k:
warnings.warn(
"Can't compute unique@{}.".format(k) +
"gen contains only {} molecules".format(len(gen))
)
gen = gen[:k]
canonic = set(mapper(n_jobs)(canonic_smiles, gen))
if None in canonic and check_validity:
raise ValueError("Invalid molecule passed to unique@k")
return len(canonic) / len(gen)
def get_all_metrics(gen, k=None, n_jobs=1,
device='cpu', batch_size=512, pool=None,
test=None, test_scaffolds=None,
ptest=None, ptest_scaffolds=None,
train=None):
"""
Computes all available metrics between test (scaffold test)
and generated sets of SMILES.
Parameters:
gen: list of generated SMILES
k: int or list with values for unique@k. Will calculate number of
unique molecules in the first k molecules. Default [1000, 10000]
n_jobs: number of workers for parallel processing
device: 'cpu' or 'cuda:n', where n is GPU device number
batch_size: batch size for FCD metric
pool: optional multiprocessing pool to use for parallelization
test (None or list): test SMILES. If None, will load
a default test set
test_scaffolds (None or list): scaffold test SMILES. If None, will
load a default scaffold test set
ptest (None or dict): precalculated statistics of the test set. If
None, will load default test statistics. If you specified a custom
test set, default test statistics will be ignored
ptest_scaffolds (None or dict): precalculated statistics of the
scaffold test set If None, will load default scaffold test
statistics. If you specified a custom test set, default test
statistics will be ignored
train (None or list): train SMILES. If None, will load a default
train set
Available metrics:
* %valid
* %unique@k
* Frechet ChemNet Distance (FCD)
* Fragment similarity (Frag)
* Scaffold similarity (Scaf)
* Similarity to nearest neighbour (SNN)
* Internal diversity (IntDiv)
* Internal diversity 2: using square root of mean squared
Tanimoto similarity (IntDiv2)
* %passes filters (Filters)
* Distribution difference for logP, SA, QED, weight
* Novelty (molecules not present in train)
"""
# if test is None:
# if ptest is not None:
# raise ValueError(
# "You cannot specify custom test "
# "statistics for default test set")
# test = get_dataset('test')
# ptest = get_statistics('test')
#
# if test_scaffolds is None:
# if ptest_scaffolds is not None:
# raise ValueError(
# "You cannot specify custom scaffold test "
# "statistics for default scaffold test set")
# test_scaffolds = get_dataset('test_scaffolds')
# ptest_scaffolds = get_statistics('test_scaffolds')
#
# train = train or get_dataset('train')
if k is None:
k = [1000, 10000]
disable_rdkit_log()
metrics = {}
close_pool = False
if pool is None:
if n_jobs != 1:
pool = Pool(n_jobs)
close_pool = True
else:
pool = 1
metrics['valid'] = fraction_valid(gen, n_jobs=pool)
gen = remove_invalid(gen, canonize=True)
if not isinstance(k, (list, tuple)):
k = [k]
for _k in k:
metrics['unique@{}'.format(_k)] = fraction_unique(gen, _k, pool)
mols = mapper(pool)(get_mol, gen)
kwargs = {'n_jobs': pool, 'device': device, 'batch_size': batch_size}
# kwargs_fcd = {'n_jobs': n_jobs, 'device': device, 'batch_size': batch_size}
if test or ptest:
if ptest is None:
ptest = compute_intermediate_statistics(test, n_jobs=n_jobs,
device=device,
batch_size=batch_size,
pool=pool)
if test_scaffolds is not None and ptest_scaffolds is None:
ptest_scaffolds = compute_intermediate_statistics(
test_scaffolds, n_jobs=n_jobs,
device=device, batch_size=batch_size,
pool=pool
)
# metrics['FCD/Test'] = FCDMetric(**kwargs_fcd)(gen=gen, pref=ptest['FCD'])
metrics['SNN/Test'] = SNNMetric(**kwargs)(gen=mols, pref=ptest['SNN'])
metrics['Frag/Test'] = FragMetric(**kwargs)(gen=mols, pref=ptest['Frag'])
metrics['Scaf/Test'] = ScafMetric(**kwargs)(gen=mols, pref=ptest['Scaf'])
# Properties
for name, func in [('logP', logP), ('SA', SA),
('QED', QED),
('weight', weight)]:
metrics[name] = WassersteinMetric(func, **kwargs)(
gen=mols, pref=ptest[name])
if test_scaffolds or ptest_scaffolds:
if ptest_scaffolds is not None:
# metrics['FCD/TestSF'] = FCDMetric(**kwargs_fcd)(
# gen=gen, pref=ptest_scaffolds['FCD']
# )
metrics['SNN/TestSF'] = SNNMetric(**kwargs)(
gen=mols, pref=ptest_scaffolds['SNN']
)
metrics['Frag/TestSF'] = FragMetric(**kwargs)(
gen=mols, pref=ptest_scaffolds['Frag']
)
metrics['Scaf/TestSF'] = ScafMetric(**kwargs)(
gen=mols, pref=ptest_scaffolds['Scaf']
)
metrics['IntDiv'] = internal_diversity(mols, pool, device=device)
metrics['IntDiv2'] = internal_diversity(mols, pool, device=device, p=2)
metrics['Filters'] = fraction_passes_filters(mols, pool)
if train is not None:
metrics['Novelty'] = novelty(mols, train, pool)
enable_rdkit_log()
if close_pool:
pool.close()
pool.join()
return metrics
def precalc(self, mols):
if self.func is not None:
values = mapper(self.n_jobs)(self.func, mols)
else:
values = mols
return {'values': values}
