def reward(mols):
rr = 1.
for m in ('logp,sas,qed,unique' if metric == 'all' else metric).split(','):
if m == 'np':
rr *= MolecularMetrics.natural_product_scores(mols, norm=True)
elif m == 'logp':
rr *= MolecularMetrics.water_octanol_partition_coefficient_scores(mols, norm=True)
elif m == 'sas':
rr *= MolecularMetrics.synthetic_accessibility_score_scores(mols, norm=True)
elif m == 'qed':
rr *= MolecularMetrics.quantitative_estimation_druglikeness_scores(mols, norm=True)
elif m == 'novelty':
rr *= MolecularMetrics.novel_scores(mols, data)
elif m == 'dc':
rr *= MolecularMetrics.drugcandidate_scores(mols, data)
elif m == 'unique':
rr *= MolecularMetrics.unique_scores(mols)
elif m == 'diversity':
rr *= MolecularMetrics.diversity_scores(mols, data)
elif m == 'validity':
rr *= MolecularMetrics.valid_scores(mols)
else:
raise RuntimeError('{} is not defined as a metric'.format(m))
return rr.reshape(-1, 1)
def all_scores(mols, data, norm=False, reconstruction=False):
m0 = {
k: list(filter(lambda e: e is not None, v))
for k, v in {
'NP score':
MolecularMetrics.natural_product_scores(mols, norm=norm),
'QED score':
MolecularMetrics.quantitative_estimation_druglikeness_scores(mols),
'logP score':
MolecularMetrics.water_octanol_partition_coefficient_scores(
mols, norm=norm),
'SA score':
MolecularMetrics.synthetic_accessibility_score_scores(mols,
norm=norm),
'diversity score':
MolecularMetrics.diversity_scores(mols, data),
'drugcandidate score':
MolecularMetrics.drugcandidate_scores(mols, data)
}.items()
}
m1 = {
'valid score': MolecularMetrics.valid_total_score(mols) * 100,
'unique score': MolecularMetrics.unique_total_score(mols) * 100,
'novel score': MolecularMetrics.novel_total_score(mols, data) * 100
}
return m0, m1