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Merge pull request #1 from chembl/refactoring
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Refactoring
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@LinaHeinzke
LinaHeinzke authored Feb 21, 2024
2 parents 6982ee6 + 3fd8e87 commit d757444
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Showing 16 changed files with 1,380 additions and 1,460 deletions.
142 changes: 72 additions & 70 deletions src/add_chembl_compound_properties.py
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Original file line number Diff line number Diff line change
@@ -1,11 +1,18 @@
"""
Add ChEMBL compound properties to the dataset.
"""

import sqlite3

import pandas as pd

from dataset import Dataset
import sanity_checks


########### Add Compound Properties Based on ChEMBL Data ###########
def add_first_publication_date(
df_combined: pd.DataFrame, chembl_con: sqlite3.Connection, limit_to_literature: bool
def get_first_publication_cpd_date(
chembl_con: sqlite3.Connection, limit_to_literature: bool
) -> pd.DataFrame:
"""
Query and calculate the first publication of a compound
Expand All @@ -14,13 +21,11 @@ def add_first_publication_date(
of the compound in the literature according to ChEMBL.
Otherwise this is the first appearance in any source in ChEMBL.
:param df_combined: Pandas DataFrame with compound-target pairs
:type df_combined: pd.DataFrame
:param chembl_con: Sqlite3 connection to ChEMBL database.
:type chembl_con: sqlite3.Connection
:param limit_to_literature: Base first_publication_cpd on literature sources only if True.
:type limit_to_literature: bool
:return: Pandas DataFrame with added first_publication_cpd.
:return: Pandas DataFrame with parent_molregno and first_publication_cpd from ChEMBL.
:rtype: pd.DataFrame
"""
# information about salts is aggregated in the parent
Expand All @@ -42,26 +47,21 @@ def add_first_publication_date(
].transform("min")
df_docs = df_docs[["parent_molregno", "first_publication_cpd"]].drop_duplicates()

df_combined = df_combined.merge(df_docs, on="parent_molregno", how="left")
return df_docs

return df_combined


def add_chembl_properties_and_structures(
df_combined: pd.DataFrame, chembl_con: sqlite3.Connection
) -> tuple[pd.DataFrame, pd.DataFrame]:
def get_chembl_properties_and_structures(
chembl_con: sqlite3.Connection,
) -> pd.DataFrame:
"""
Add compound properties from the compound_properties table
Get compound properties from the compound_properties table
(e.g., alogp, #hydrogen bond acceptors / donors, etc.).
Add InChI, InChI key and canonical smiles.
Get InChI, InChI key and canonical smiles.
:param df_combined: Pandas DataFrame with compound-target pairs
:type df_combined: pd.DataFrame
:param chembl_con: Sqlite3 connection to ChEMBL database.
:type chembl_con: sqlite3.Connection
:return: - Pandas DataFrame with added compound properties and structures. \\
- Pandas DataFrame with compound properties and structures for all compound ids in ChEMBL.
:rtype: (pd.DataFrame, pd.DataFrame)
:return: Pandas DataFrame with compound properties and structures for all compound ids in ChEMBL
:rtype: pd.DataFrame
"""
sql = """
SELECT DISTINCT mh.parent_molregno,
Expand All @@ -79,14 +79,12 @@ def add_chembl_properties_and_structures(

df_cpd_props = pd.read_sql_query(sql, con=chembl_con)

df_combined = df_combined.merge(df_cpd_props, on="parent_molregno", how="left")

return df_combined, df_cpd_props
return df_cpd_props


def add_ligand_efficiency_metrics(df_combined: pd.DataFrame) -> pd.DataFrame:
def calculate_ligand_efficiency_metrics(dataset: Dataset):
"""
Calculate the ligand efficiency metrics for the compounds
Calculate and add the ligand efficiency metrics for the compounds
based on the mean pchembl values for a compound-target pair and
the following ligand efficiency (LE) formulas:
Expand All @@ -108,33 +106,37 @@ def add_ligand_efficiency_metrics(df_combined: pd.DataFrame) -> pd.DataFrame:
Once for the pchembl values based on binding + functional assays (BF)
and once for the pchembl values based on binding assays only (B).
:param df_combined: Pandas DataFrame with compound-target pairs
:type df_combined: pd.DataFrame
:return: Pandas DataFrame with added ligand efficiency metrics
:rtype: pd.DataFrame
:param dataset: Dataset with compound-target pairs.
Will be updated to include ligand efficiency metrics.
:type dataset: Dataset
"""
for suffix in ["BF", "B"]:
df_combined.loc[df_combined["heavy_atoms"] != 0, f"LE_{suffix}"] = (
df_combined[f"pchembl_value_mean_{suffix}"]
/ df_combined["heavy_atoms"]
dataset.df_result.loc[dataset.df_result["heavy_atoms"] != 0, f"LE_{suffix}"] = (
dataset.df_result[f"pchembl_value_mean_{suffix}"]
/ dataset.df_result["heavy_atoms"]
* (2.303 * 298 * 0.00199)
)

df_combined.loc[df_combined["mw_freebase"] != 0, f"BEI_{suffix}"] = (
df_combined[f"pchembl_value_mean_{suffix}"]
dataset.df_result.loc[
dataset.df_result["mw_freebase"] != 0, f"BEI_{suffix}"
] = (
dataset.df_result[f"pchembl_value_mean_{suffix}"]
* 1000
/ df_combined["mw_freebase"]
/ dataset.df_result["mw_freebase"]
)

df_combined.loc[df_combined["psa"] != 0, f"SEI_{suffix}"] = (
df_combined[f"pchembl_value_mean_{suffix}"] * 100 / df_combined["psa"]
dataset.df_result.loc[dataset.df_result["psa"] != 0, f"SEI_{suffix}"] = (
dataset.df_result[f"pchembl_value_mean_{suffix}"]
* 100
/ dataset.df_result["psa"]
)

df_combined[f"LLE_{suffix}"] = (
df_combined[f"pchembl_value_mean_{suffix}"] - df_combined["alogp"]
dataset.df_result[f"LLE_{suffix}"] = (
dataset.df_result[f"pchembl_value_mean_{suffix}"]
- dataset.df_result["alogp"]
)

df_combined = df_combined.astype(
dataset.df_result = dataset.df_result.astype(
{
f"LE_{suffix}": "float64",
f"BEI_{suffix}": "float64",
Expand All @@ -143,26 +145,19 @@ def add_ligand_efficiency_metrics(df_combined: pd.DataFrame) -> pd.DataFrame:
}
)

return df_combined


def add_atc_classification(
df_combined: pd.DataFrame, chembl_con: sqlite3.Connection
) -> tuple[pd.DataFrame, pd.DataFrame]:
def get_atc_classification(chembl_con: sqlite3.Connection) -> pd.DataFrame:
"""
Query and add ATC classifications (level 1) from the atc_classification and
Query ATC classifications (level 1) from the atc_classification and
molecule_atc_classification tables.
ATC level annotations for the same parent_molregno are combined into one description
that concatenates all descriptions sorted alphabetically
ATC level annotations for the same parent_molregno are combined into one description
that concatenates all descriptions sorted alphabetically
into one string with ' | ' as a separator.
:param df_combined: Pandas DataFrame with compound-target pairs
:type df_combined: pd.DataFrame
:param chembl_con: Sqlite3 connection to ChEMBL database.
:type chembl_con: sqlite3.Connection
:return: - Pandas DataFrame with added ATC classifications \\
- Pandas DataFrame with ATC annotations in ChEMBL
:rtype: (pd.DataFrame, pd.DataFrame)
:return: Pandas DataFrame with ATC annotations in ChEMBL.
:rtype: pd.DataFrame
"""
sql = """
SELECT DISTINCT mh.parent_molregno, atc.level1, atc.level1_description
Expand All @@ -185,14 +180,12 @@ def add_atc_classification(
].transform(lambda x: between_str_join.join(sorted(x)))
atc_levels = atc_levels[["parent_molregno", "atc_level1"]].drop_duplicates()

df_combined = df_combined.merge(atc_levels, on="parent_molregno", how="left")

return df_combined, atc_levels
return atc_levels


def add_all_chembl_compound_properties(
df_combined: pd.DataFrame, chembl_con: sqlite3.Connection, limit_to_literature: bool
) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
dataset: Dataset, chembl_con: sqlite3.Connection, limit_to_literature: bool
):
"""
Add ChEMBL-based compound properties to the given compound-target pairs, specifically:
Expand All @@ -202,24 +195,33 @@ def add_all_chembl_compound_properties(
- ligand efficiency metrics
- ATC classifications
:param df_combined: Pandas DataFrame with compound-target pairs
:type df_combined: pd.DataFrame
:param dataset: Dataset with compound-target pairs.
Will be updated to include compound properties.
:type dataset: Dataset
:param chembl_con: Sqlite3 connection to ChEMBL database.
:type chembl_con: sqlite3.Connection
:param limit_to_literature: Base first_publication_cpd on literature sources only if True.
:param limit_to_literature: Base first_publication_cpd on literature sources only if True.
Base it on all available sources otherwise.
:type limit_to_literature: bool
:return: - Pandas DataFrame with added compound properties \\
- Pandas DataFrame with compound properties and structures for all compound ids in ChEMBL \\
- Pandas DataFrame with ATC annotations in ChEMBL
:rtype: (pd.DataFrame, pd.DataFrame, pd.DataFrame)
"""
df_combined = add_first_publication_date(
df_combined, chembl_con, limit_to_literature
df_docs = get_first_publication_cpd_date(chembl_con, limit_to_literature)
dataset.df_result = dataset.df_result.merge(
df_docs, on="parent_molregno", how="left"
)
df_combined, df_cpd_props = add_chembl_properties_and_structures(
df_combined, chembl_con

df_cpd_props = get_chembl_properties_and_structures(chembl_con)
dataset.df_cpd_props = df_cpd_props
dataset.df_result = dataset.df_result.merge(
df_cpd_props, on="parent_molregno", how="left"
)
sanity_checks.check_compound_props(dataset.df_result, df_cpd_props)

calculate_ligand_efficiency_metrics(dataset)
sanity_checks.check_ligand_efficiency_metrics(dataset.df_result)

atc_levels = get_atc_classification(chembl_con)
dataset.atc_levels = atc_levels
dataset.df_result = dataset.df_result.merge(
atc_levels, on="parent_molregno", how="left"
)
df_combined = add_ligand_efficiency_metrics(df_combined)
df_combined, atc_levels = add_atc_classification(df_combined, chembl_con)
return df_combined, df_cpd_props, atc_levels
sanity_checks.check_atc(dataset.df_result, atc_levels)
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