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6d1bbc7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | """Tests for BindingDB ETL pipeline."""
from pathlib import Path
import pandas as pd
import pytest
from negbiodb.db import connect, create_database
from negbiodb.etl_bindingdb import (
_extract_inactive_rows_from_chunk,
_parse_relation_value,
run_bindingdb_etl,
)
MIGRATIONS_DIR = Path(__file__).resolve().parent.parent / "migrations"
@pytest.fixture
def migrated_db(tmp_path):
db_path = tmp_path / "test.db"
create_database(db_path, MIGRATIONS_DIR)
return db_path
class TestBindingDBHelpers:
def test_parse_relation_value(self):
assert _parse_relation_value(">10000") == (">", 10000.0)
assert _parse_relation_value("<=500") == ("<=", 500.0)
assert _parse_relation_value("12345") == ("=", 12345.0)
assert _parse_relation_value(None) == ("=", None)
def test_extract_inactive_rows_human_only(self):
chunk = pd.DataFrame(
{
"Ligand SMILES": ["c1ccccc1", "c1ccccc1", "CCO", "CCN"],
"UniProt (SwissProt) Primary ID of Target Chain": [
"P00533",
"P00533",
"P12345",
"P12345",
],
"Target Source Organism According to Curator or DataSource": [
"Homo sapiens",
"Homo sapiens",
"Mus musculus",
"Homo sapiens",
],
"Ki (nM)": [">10000", "500", ">15000", None],
"Kd (nM)": [None, None, None, ">12000"],
"BindingDB Reactant_set_id": [1, 2, 3, 4],
"Publication Year": [2010, 2011, 2012, 2013],
}
)
rows = _extract_inactive_rows_from_chunk(
chunk, inactivity_threshold_nm=10000, human_only=True
)
assert len(rows) == 2
assert {r["activity_type"] for r in rows} == {"Ki", "Kd"}
assert all(r["species_tested"] == "Homo sapiens" for r in rows)
def test_extract_inactive_rows_with_non_human_enabled(self):
chunk = pd.DataFrame(
{
"Ligand SMILES": ["CCO"],
"UniProt (SwissProt) Primary ID of Target Chain": ["P12345"],
"Target Source Organism According to Curator or DataSource": [
"Mus musculus"
],
"Ki (nM)": [">15000"],
"BindingDB Reactant_set_id": [3],
"Publication Year": [2012],
}
)
rows = _extract_inactive_rows_from_chunk(
chunk, inactivity_threshold_nm=10000, human_only=False
)
assert len(rows) == 1
assert rows[0]["species_tested"] == "Mus musculus"
def test_extract_inactive_rows_requires_organism_when_human_only(self):
chunk = pd.DataFrame(
{
"Ligand SMILES": ["c1ccccc1"],
"UniProt (SwissProt) Primary ID of Target Chain": ["P00533"],
"Ki (nM)": [">10000"],
}
)
rows = _extract_inactive_rows_from_chunk(
chunk, inactivity_threshold_nm=10000, human_only=True
)
assert rows == []
class TestRunBindingDBETL:
def test_run_bindingdb_etl_small_dataset(self, migrated_db, tmp_path):
tsv_path = tmp_path / "BindingDB_All.tsv"
pd.DataFrame(
{
"Ligand SMILES": ["c1ccccc1", "c1ccccc1", "CCO", "CCN"],
"UniProt (SwissProt) Primary ID of Target Chain": [
"P00533",
"P00533",
"P12345",
"P12345",
],
"Target Source Organism According to Curator or DataSource": [
"Homo sapiens",
"Homo sapiens",
"Homo sapiens",
"Homo sapiens",
],
"Ki (nM)": [">10000", ">20000", None, "500"],
"IC50 (nM)": [None, None, "15000", "500"],
"BindingDB Reactant_set_id": [1, 2, 3, 4],
"Publication Year": [2010, 2011, 2020, 2021],
}
).to_csv(tsv_path, sep="\t", index=False)
stats = run_bindingdb_etl(
db_path=migrated_db,
bindingdb_tsv_path=tsv_path,
chunksize=2,
)
assert stats["rows_read"] == 4
assert stats["rows_filtered_inactive"] == 3
assert stats["results_inserted"] == 3
with connect(migrated_db) as conn:
n_results = conn.execute(
"SELECT COUNT(*) FROM negative_results WHERE source_db='bindingdb'"
).fetchone()[0]
assert n_results == 3
n_pairs = conn.execute(
"SELECT COUNT(*) FROM compound_target_pairs"
).fetchone()[0]
assert n_pairs == 2
species = {
row[0]
for row in conn.execute(
"SELECT DISTINCT species_tested FROM negative_results WHERE source_db='bindingdb'"
).fetchall()
}
assert species == {"Homo sapiens"}
def test_run_bindingdb_etl_respects_threshold_and_human_toggle(
self, migrated_db, tmp_path, monkeypatch
):
import negbiodb.etl_bindingdb as mod
tsv_path = tmp_path / "BindingDB_All.tsv"
pd.DataFrame(
{
"Ligand SMILES": ["c1ccccc1", "CCO"],
"UniProt (SwissProt) Primary ID of Target Chain": ["P00533", "P12345"],
"Target Source Organism According to Curator or DataSource": [
"Homo sapiens",
"Mus musculus",
],
"Ki (nM)": ["15000", "25000"],
"BindingDB Reactant_set_id": [1, 2],
"Publication Year": [2010, 2011],
}
).to_csv(tsv_path, sep="\t", index=False)
monkeypatch.setattr(
mod,
"load_config",
lambda: {
"inactivity_threshold_nm": 10000,
"downloads": {"bindingdb": {"dest_dir": "unused"}},
"bindingdb_etl": {
"chunksize": 100000,
"inactive_threshold_nm": 20000,
"human_only": False,
},
},
)
stats = run_bindingdb_etl(
db_path=migrated_db,
bindingdb_tsv_path=tsv_path,
chunksize=10,
)
assert stats["results_inserted"] == 1
with connect(migrated_db) as conn:
row = conn.execute(
"SELECT inactivity_threshold, species_tested FROM negative_results "
"WHERE source_db='bindingdb'"
).fetchone()
assert row == (20000.0, "Mus musculus")
|