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import evaluate
import datasets
import moses
from moses import metrics
import pandas as pd
from tdc import Evaluator
from tdc import Oracle
_DESCRIPTION = """
Comprehensive suite of metrics designed to assess the performance of molecular generation models, for understanding how well a model can produce novel, chemically valid molecules that are relevant to specific research objectives.
"""
_KWARGS_DESCRIPTION = """
Args:
generated_smiles (`list` of `string`): A collection of SMILES (Simplified Molecular Input Line Entry System) strings generated by the model, ideally encompassing more than 30,000 samples.
train_smiles (`list` of `string`): The dataset of SMILES strings used to train the model, serving as a reference to evaluate the novelty and diversity of the generated molecules.
Returns:
Dectionary item containing various metrics to evaluate model performance
"""
_CITATION = """
@article{DBLP:journals/corr/abs-1811-12823,
author = {Daniil Polykovskiy and
Alexander Zhebrak and
Benjam{\'{\i}}n S{\'{a}}nchez{-}Lengeling and
Sergey Golovanov and
Oktai Tatanov and
Stanislav Belyaev and
Rauf Kurbanov and
Aleksey Artamonov and
Vladimir Aladinskiy and
Mark Veselov and
Artur Kadurin and
Sergey I. Nikolenko and
Al{\'{a}}n Aspuru{-}Guzik and
Alex Zhavoronkov},
title = {Molecular Sets {(MOSES):} {A} Benchmarking Platform for Molecular
Generation Models},
journal = {CoRR},
volume = {abs/1811.12823},
year = {2018},
url = {http://arxiv.org/abs/1811.12823},
eprinttype = {arXiv},
eprint = {1811.12823},
timestamp = {Fri, 26 Nov 2021 15:34:30 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1811-12823.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class my_metric(evaluate.Metric):
def _info(self):
return evaluate.MetricInfo(
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
features=datasets.Features(
{
"generated_smiles": datasets.Sequence(datasets.Value("string")),
"train_smiles": datasets.Sequence(datasets.Value("string")),
}
if self.config_name == "multilabel"
else {
"generated_smiles": datasets.Value("string"),
"train_smiles": datasets.Value("string"),
}
),
reference_urls=["https://github.com/molecularsets/moses", "https://tdcommons.ai/functions/oracles/"],
)
def _compute(self, generated_smiles, train_smiles = None):
Results = metrics.get_all_metrics(gen = generated_smiles, train= train_smiles)
# evaluator = Evaluator(name = 'Diversity')
# Diversity = evaluator(generated_smiles)
evaluator = Evaluator(name = 'KL_Divergence')
KL_Divergence = evaluator(generated_smiles, train_smiles)
# evaluator = Evaluator(name = 'FCD_Distance')
# FCD_Distance = evaluator(generated_smiles, train_smiles)
# evaluator = Evaluator(name = 'Novelty')
# Novelty = evaluator(generated_smiles, train_smiles)
# evaluator = Evaluator(name = 'Validity')
# Validity = evaluator(generated_smiles)
Results.update({
# "PyTDC_Diversity": Diversity,
"KL_Divergence": KL_Divergence,
# "PyTDC_Validity": Validity,FCD_Distance": FCD_Distance,
# "PyTDC_Novelty": Novelty,
# "PyTDC_
})
oracle_list = [
'QED', 'SA', 'MPO', 'GSK3B', 'JNK3',
'DRD2', 'LogP', 'Rediscovery', 'Similarity',
'Median', 'Isomers', 'Valsartan_SMARTS', 'Hop'
]
# Iterate through each oracle and compute its score
for oracle_name in oracle_list:
oracle = Oracle(name=oracle_name)
if oracle_name in ['Rediscovery', 'MPO', 'Similarity', 'Median', 'Isomers', 'Hop']:
# Assuming these oracles return a dictionary where values are lists of scores
score = oracle(generated_smiles)
if isinstance(score, dict):
# Convert lists of scores to average score for these specific metrics
score = {key: sum(values)/len(values) for key, values in score.items()}
else:
# Assuming other oracles return a list of scores
score = oracle(generated_smiles)
if isinstance(score, list):
# Convert list of scores to average score
score = sum(score) / len(score)
Results.update({f"PyTDC_{oracle_name}": score})
return {"results": Results}
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