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import os
import sys
import re
from pathlib import Path
from typing import Collection, List, Dict, Type
import numpy as np
import pandas as pd
from tqdm import tqdm
from .metrics import FullEvaluator, FullCollectionEvaluator
AUXILIARY_COLUMNS = ['sample', 'sdf_file', 'pdb_file', 'subdir']
VALIDITY_METRIC_NAME = 'medchem.valid'
def get_data_type(key: str, data_types: Dict[str, Type], default=float) -> Type:
found_data_type_key = None
found_data_type_value = None
for data_type_key, data_type_value in data_types.items():
if re.match(data_type_key, key) is not None:
if found_data_type_key is not None:
raise ValueError(f'Multiple data type keys match [{key}]: {found_data_type_key}, {data_type_key}')
found_data_type_value = data_type_value
found_data_type_key = data_type_key
if found_data_type_key is None:
if default is None:
raise KeyError(key)
else:
found_data_type_value = default
return found_data_type_value
def convert_data_to_table(data: List[Dict], data_types: Dict[str, Type]) -> pd.DataFrame:
"""
Converts data from `evaluate_drugflow` to a detailed table
"""
table = []
for entry in data:
table_entry = {}
for key, value in entry.items():
if key in AUXILIARY_COLUMNS:
table_entry[key] = value
continue
if get_data_type(key, data_types) != list:
table_entry[key] = value
table.append(table_entry)
return pd.DataFrame(table)
def aggregated_metrics(table: pd.DataFrame, data_types: Dict[str, Type], validity_metric_name: str = None):
"""
Args:
table (pd.DataFrame): table with metrics computed for each sample
data_types (Dict[str, Type]): dictionary with data types for each column
validity_metric_name (str): name of the column that has validity metric
Returns:
agg_table (pd.DataFrame): table with columns ['metric', 'value', 'std']
"""
aggregated_results = []
# If validity column name is provided:
# 1. compute validity on the entire data
# 2. drop all invalid molecules to compute the rest
if validity_metric_name is not None:
aggregated_results.append({
'metric': validity_metric_name,
'value': table[validity_metric_name].fillna(False).astype(float).mean(),
'std': None,
})
table = table[table[validity_metric_name]]
# Compute aggregated metrics + standard deviations where applicable
for column in table.columns:
if column in AUXILIARY_COLUMNS + [validity_metric_name] or get_data_type(column, data_types) == str:
continue
with pd.option_context("future.no_silent_downcasting", True):
if get_data_type(column, data_types) == bool:
values = table[column].fillna(0).values.astype(float).mean()
std = None
else:
values = table[column].dropna().values.astype(float).mean()
std = table[column].dropna().values.astype(float).std()
aggregated_results.append({
'metric': column,
'value': values,
'std': std,
})
agg_table = pd.DataFrame(aggregated_results)
return agg_table
def collection_metrics(
table: pd.DataFrame,
reference_smiles: Collection[str],
validity_metric_name: str = None,
exclude_evaluators: Collection[str] = [],
):
"""
Args:
table (pd.DataFrame): table with metrics computed for each sample
reference_smiles (Collection[str]): list of reference SMILES (e.g. training set)
validity_metric_name (str): name of the column that has validity metric
exclude_evaluators (Collection[str]): Evaluator IDs to exclude
Returns:
col_table (pd.DataFrame): table with columns ['metric', 'value']
"""
# If validity column name is provided drop all invalid molecules
if validity_metric_name is not None:
table = table[table[validity_metric_name]]
evaluator = FullCollectionEvaluator(reference_smiles, exclude_evaluators=exclude_evaluators)
smiles = table['representation.smiles'].values
if len(smiles) == 0:
print('No valid input molecules')
return pd.DataFrame(columns=['metric', 'value'])
collection_metrics = evaluator(smiles)
results = [
{'metric': key, 'value': value}
for key, value in collection_metrics.items()
]
col_table = pd.DataFrame(results)
return col_table
def evaluate_drugflow_subdir(
in_dir: Path,
evaluator: FullEvaluator,
desc: str = None,
n_samples: int = None,
) -> List[Dict]:
"""
Computes per-molecule metrics for a single directory of samples for one target
"""
results = []
valid_files = [
int(fname.split('_')[0])
for fname in os.listdir(in_dir)
if fname.endswith('_ligand.sdf') and not fname.startswith('.')
]
if len(valid_files) == 0:
return pd.DataFrame()
upper_bound = max(valid_files) + 1
if n_samples is not None:
upper_bound = min(upper_bound, n_samples)
for i in tqdm(range(upper_bound), desc=desc, file=sys.stdout):
in_mol = Path(in_dir, f'{i}_ligand.sdf')
in_prot = Path(in_dir, f'{i}_pocket.pdb')
res = evaluator(in_mol, in_prot)
res['sample'] = i
res['sdf_file'] = str(in_mol)
res['pdb_file'] = str(in_prot)
results.append(res)
return results
def evaluate_drugflow(
in_dir: Path,
evaluator: FullEvaluator,
n_samples: int = None,
job_id: int = 0,
n_jobs: int = 1,
) -> List[Dict]:
"""
1. Computes per-molecule metrics for all single directories of samples
2. Aggregates these metrics
3. Computes additional collection metrics (if `reference_smiles_path` is provided)
"""
data = []
total_number_of_subdirs = len([path for path in in_dir.glob("[!.]*") if os.path.isdir(path)])
i = 0
for subdir in in_dir.glob("[!.]*"):
if not os.path.isdir(subdir):
continue
i += 1
if (i - 1) % n_jobs != job_id:
continue
curr_data = evaluate_drugflow_subdir(
in_dir=subdir,
evaluator=evaluator,
desc=f'[{i}/{total_number_of_subdirs}] {str(subdir.name)}',
n_samples=n_samples,
)
for entry in curr_data:
entry['subdir'] = str(subdir)
data.append(entry)
return data
def compute_all_metrics_drugflow(
in_dir: Path,
gnina_path: Path,
reduce_path: Path = None,
reference_smiles_path: Path = None,
n_samples: int = None,
validity_metric_name: str = VALIDITY_METRIC_NAME,
exclude_evaluators: Collection[str] = [],
job_id: int = 0,
n_jobs: int = 1,
):
evaluator = FullEvaluator(gnina=gnina_path, reduce=reduce_path, exclude_evaluators=exclude_evaluators)
data = evaluate_drugflow(in_dir=in_dir, evaluator=evaluator, n_samples=n_samples, job_id=job_id, n_jobs=n_jobs)
table_detailed = convert_data_to_table(data, evaluator.dtypes)
table_aggregated = aggregated_metrics(
table_detailed,
data_types=evaluator.dtypes,
validity_metric_name=validity_metric_name
)
# Add collection metrics (uniqueness, novelty, FCD, etc.) if reference smiles are provided
if reference_smiles_path is not None:
reference_smiles = np.load(reference_smiles_path)
col_metrics = collection_metrics(
table=table_detailed,
reference_smiles=reference_smiles,
validity_metric_name=validity_metric_name,
exclude_evaluators=exclude_evaluators
)
table_aggregated = pd.concat([table_aggregated, col_metrics])
return data, table_detailed, table_aggregated
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