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fix find_nodes() and formatting
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Tom authored and Tom committed Aug 9, 2024
1 parent 18c5620 commit 83ab717
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56 changes: 44 additions & 12 deletions comptox_ai/chemical_featurizer/generate_vectors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -163,6 +163,7 @@ def retrieve_smiles(

return smiles_list, found_chemical_id_list


def generate_3d_conformers(smiles_list):
"""
Generates a list of mol objects with 3D conformer data from input list of SMILES strings.
Expand All @@ -171,7 +172,7 @@ def generate_3d_conformers(smiles_list):
----------
smiles_list : List[str]
List of SMILES strings.
Returns
-------
List[rdkit.Chem.rdchem.Mol]
Expand All @@ -189,7 +190,7 @@ def generate_3d_conformers(smiles_list):
for smiles in smiles_list:
mol = Chem.MolFromSmiles(smiles)
if mol is not None:
mol = Chem.AddHs(mol)
mol = Chem.AddHs(mol)
AllChem.EmbedMolecule(mol, randomSeed=0)
AllChem.UFFOptimizeMolecule(mol)
mols_with_3d.append(mol)
Expand Down Expand Up @@ -220,7 +221,7 @@ def create_vector_table(
rdkit_descriptors : bool
Whether full set of rdkit_descriptors should be calculated and incorporated as vectors.
dtype : type
Data type of output df (e.g. float, np.float32, etc.).
Data type of output df (e.g. float, np.float32, etc.).
molfeat_descriptors : List[str]
List of features to generate. For possible features, see https://molfeat.datamol.io/featurizers.
use_original_chemical_ids_for_df_index : bool
Expand Down Expand Up @@ -265,29 +266,60 @@ def create_vector_table(
for feature in molfeat_descriptors:
print(f"Calculating {feature} descriptors")

if feature in {"Roberta-Zinc480M-102M", "GPT2-Zinc480M-87M", "ChemGPT-1.2B", "ChemGPT-19M", "ChemGPT-4.7M", "MolT5", "ChemBERTa-77M-MTR", "ChemBERTa-77M-MLM"}:
featurizer = PretrainedHFTransformer(kind=feature, notation='smiles', dtype=dtype)

elif feature in {"gin_supervised_masking", "gin_supervised_infomax", "gin_supervised_edgepred", "jtvae_zinc_no_kl", "gin_supervised_contextpred"}:
if feature in {
"Roberta-Zinc480M-102M",
"GPT2-Zinc480M-87M",
"ChemGPT-1.2B",
"ChemGPT-19M",
"ChemGPT-4.7M",
"MolT5",
"ChemBERTa-77M-MTR",
"ChemBERTa-77M-MLM",
}:
featurizer = PretrainedHFTransformer(
kind=feature, notation="smiles", dtype=dtype
)

elif feature in {
"gin_supervised_masking",
"gin_supervised_infomax",
"gin_supervised_edgepred",
"jtvae_zinc_no_kl",
"gin_supervised_contextpred",
}:
featurizer = PretrainedDGLTransformer(kind=feature, dtype=dtype)

else:
featurizer = MoleculeTransformer(featurizer=feature, dtype=dtype, verbose=True)
if feature in {"desc3D", "desc2D", "electroshape", "usrcat", "usr", "cats3d", "pharm3D-cats", "pharm3D-gobbi", "pharm3D-pmapper"}:
featurizer = MoleculeTransformer(
featurizer=feature, dtype=dtype, verbose=True
)
if feature in {
"desc3D",
"desc2D",
"electroshape",
"usrcat",
"usr",
"cats3d",
"pharm3D-cats",
"pharm3D-gobbi",
"pharm3D-pmapper",
}:
mol_list = generate_3d_conformers(smiles_list)
conformer_3D_flag = True

chemical_list = mol_list if conformer_3D_flag else smiles_list
vectors.append(featurizer(chemical_list).tolist())

df_column_names.append(feature)

if rdkit_descriptors:
print(f"Calculating rdkit descriptors")
mols = [Chem.MolFromSmiles(smiles) for smiles in smiles_list]
rdkit_features = np.array(
[
np.array(list(Descriptors.CalcMolDescriptors(mol).values()), dtype=dtype)
np.array(
list(Descriptors.CalcMolDescriptors(mol).values()), dtype=dtype
)
for mol in mols
]
)
Expand Down
144 changes: 94 additions & 50 deletions comptox_ai/db/graph_db.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -428,72 +428,116 @@ def find_node(self, name=None, properties=None):

return node_response[0]["n"]

def find_nodes(self, properties={}, node_types=[]):
def find_nodes(self, search_dict):
"""
Find multiple nodes by node properties and/or labels.
Parameters
----------
properties : dict
Dict of property values to match in the database query. Each key of
`properties` should be a (case-sensitive) node property, and each value
should be the value of that property (case- and type-sensitive).
node_types : list of str
Case sensitive list of strings representing node labels (node types) to
include in the results. Two or more node types in a single query may
significantly increase runtime. When multiple node labels are given, the
results will be the union of all property queries when applied
search_dict : Dict[str, Dict[str, Union[str, int, list]]]
Case sensitive dictionary of nodes to search for in the format of {node_type : {property : property_value(s)}}
Returns
-------
generator of dict
A generator containing dict representations of nodes matching the given
query.
Dict[str : List[Dict[str, str]]]
Nodes found from the query in the format {node_type : [{node_property : property_value}]}
Notes
-----
The value returned in the event of a successful query can be extremely
large. To improve performance, the results are returned as a generator
rather than a list.
"""
if (not properties) and (len(node_types) == 0):
raise ValueError(
"Error: Query must contain at least one node property or node type."
)
Raises
-------
TypeError
If type of search_dict is not Dict or if property is not str or if property values are not str, int, or List[Union[str, int]]
if not properties:
warnings.warn(
"Warning: No property filters given - the query result may be very large!"
)
Examples
--------
>>> db.find_nodes({"Chemical" : {'commonName': ["Hydroxychloroquine", "Warfarin"]}, "Gene" : {"xrefNcbiGene" : 1031, "geneSymbol" : "CDKN1A"}})
{'Chemical': [{'commonName': 'Hydroxychloroquine',
'synonyms': "+-|Ethanol, 2-[[4-[(7-chloro-4-quinolinyl)amino]pentyl]ethylamino]-|(.+-.)-Hydroxychloroquine|7-Chloro-4-[4-(N-ethyl-N-β-hydroxyethylamino)-1-methylbutylamino]quinoline|7-Chloro-4-[4'-[ethyl (2''-hydroxyethyl) amino]-1'-methylbutylamino]quinoline|7-Chloro-4-[4-[ethyl(2-hydroxyethyl)amino]-1-methylbutylamino]quinoline|7-Chloro-4-[5-(N-ethyl-N-2-hydroxyethylamino)-2-pentyl]aminoquinoline|Ethanol, 2-[[4-[(7-chloro-4-quinolyl)amino]pentyl]ethylamino]-|hidroxicloroquina|Hydroxychlorochin|Oxichloroquine|Oxychlorochin|Oxychloroquine|Racemic Hydroxychloroquine|5-22-10-00280|BRN 0253894|7-Chloro-4-(4-(N-ethyl-N-beta-hydroxyethylamino)-1-methylbutylamino)quinoline|7-Chloro-4-(5-(N-ethyl-N-2-hydroxyethylamino)-2-pentyl)aminoquinoline|EINECS 204-249-8|Oxichlorochinum|Hydroxychloroquinum|Idrossiclorochina|UNII-4QWG6N8QKH|(+-)-hydroxychloroquine|2-((4-((7-chloro-4-quinolyl)amino)pentyl)ethylamino)ethanol|2-(N-(4-(7-chlor-4-chinolylamino)-4-methylbutyl)ethylamino)ethanol|7-chloro-4-(4-(N-ethyl-N-beta-hydroxyethylamino)-1-methylbutylamino)quinoline|7-chloro-4-(4-(ethyl(2-hydroxyethyl)amino)-1-methylbutylamino)quinoline|7-chloro-4-[4-(N-ethyl-N-beta-hydroxyethylamino)-1-methylbutylamino]quinoline|7-chloro-4-[5-(N-ethyl-N-2-hydroxyethylamino)-2-pentyl]aminoquinoline|NSC4375|oxichlorochine",
'xrefDrugbank': 'DB01611',
'xrefMeSH': 'MESH:D006886',
'xrefDTXSID': 'DTXSID8023135',
'xrefPubchemSID': '315673741.0',
'xrefCasRN': '118-42-3',
'uri': 'http://jdr.bio/ontologies/comptox.owl#chemical_dtxsid8023135',
'monoisotopicMass': '335.1764402000',
'maccs': '00000000000000000000000000000000000000000000000000000000000000000100000000000000101001110011000100101101110111010011101001100100110111110111001001011101010111101111110',
'molFormula': 'C18H26ClN3O',
'sMILES': 'CCN(CCO)CCCC(C)NC1=CC=NC2=CC(Cl)=CC=C12',
'xrefPubchemCID': '3652',
'molWeight': '335.8800000000'},
{'commonName': 'Warfarin',
'synonyms': "Coumadin|2H-1-Benzopyran-2-one, 4-hydroxy-3-(3-oxo-1-phenylbutyl)-|(.+-.)-Warfarin|(.+-.)-Warfarin-alcohol|(RS)-Warfarin|1-(4'-Hydroxy-3'-coumarinyl)-1-phenyl-3-butanone|3-(1'-Phenyl-2'-acetylethyl)-4-hydroxycoumarin|3-(α-Acetonylbenzyl)-4-hydroxycoumarin|3-(α-Phenyl-β-acetylethyl)-4-hydroxycoumarin|3-(α-Phenyl-β-acetylethyl)-4-hydroxy-coumarin|4-Hydroxy-3-(3-oxo-1-phenylbutyl)-2H-1-benzopyran-2-one|4-Hydroxy-3-(3-oxo-1-phenylbutyl)-2H-chromen-2-one|Athrombine-K|BENZOPYRAN(2H-1)-2-ONE, 4-HYDROXY-3-(3-OXO-1- PHENYLBUTYL)-|Brumolin|Coumafen|Coumafene|Coumaphen|Coumarin, 3-(α-acetonylbenzyl)-4-hydroxy-|Coumefene|Dethmor|DL-3-(α-Acetonylbenzyl)-4-hydroxycoumarin|Kumader|Kumatox|NSC 59813|rac-Warfarin|Ratron G|Rodafarin|Rodafarin C|Temus W|Vampirinip II|Vampirinip III|W.A.R.F. 42|WARF compound 42|warfarina|Warfarine|Zoocoumarin|5-18-04-00162|200 coumarin|Arab Rat Death|BRN 1293536|Caswell No. 903|Compound 42|Coumaphene|Coumarins|Cov-R-Tox|Dethnel|Eastern states duocide|EINECS 201-377-6|EPA Pesticide Chemical Code 086002|Frass-ratron|4-Hydroxy-3-(3-oxo-1-phenylbutyl)coumarin|Kypfarin|Liqua-tox|Maag rattentod cum|Mar-frin|Martin's mar-frin|Maveran|Mouse pak|3-(alpha-Phenyl-beta-acetylethyl)-4-hydroxycoumarin|Rat & mice bait|Rat-o-cide #2|Rat-Gard|Rat-B-gon|Rat-Kill|Rat-Mix|Rat-ola|Ratorex|Ratoxin|Rats-No-More|Ratten-koederrohr|Rattenstreupulver Neu Schacht|Rattenstreupulver new schacht|Rattentraenke|Rat-Trol|Rattunal|Rat-A-way|RCRA waste number P001|Ro-Deth|Rodex blox|Rough & ready mouse mix|Solfarin|Spray-trol brand roden-trol|Tox-Hid|Twin light rat away|Warfarat|Warfarin Q|Warfarin plus|DL-3-(alpha-Acetonylbenzyl)-4-hydroxycoumarin|Dicusat E|(Phenyl-1 acetyl-2 ethyl) 3-hydroxy-4 coumarine|3-(alpha-Phenyl-beta-acetylaethyl)-4-hydroxycumarin|4-Hydroxy-3-(3-oxo-1-fenyl-butyl) cumarine|4-Hydroxy-3-(3-oxo-1-phenyl-butyl)-cumarin|4-Idrossi-3-(3-oxo-1-fenil-butil)-cumarine|Warfarinum|UNII-5Q7ZVV76EI",
'xrefDrugbank': 'DB00682',
'xrefMeSH': 'MESH:D014859',
'xrefDTXSID': 'DTXSID5023742',
'xrefPubchemSID': '315674265.0',
'uri': 'http://jdr.bio/ontologies/comptox.owl#chemical_dtxsid5023742',
'xrefCasRN': '81-81-2',
'monoisotopicMass': '308.1048589950',
'maccs': '00000000000000000000000000000000000000000000000000000000010000000000000000000000000000000101000000100100010000000101000000010101000010001101100111100010101101011011110',
'molFormula': 'C19H16O4',
'sMILES': 'CC(=O)CC(C1=CC=CC=C1)C1=C(O)C2=CC=CC=C2OC1=O',
'xrefAOPWikiStressorID': 195,
'xrefPubchemCID': '54678486',
'molWeight': '308.3330000000'}],
'Gene': [{'typeOfGene': 'protein-coding',
'commonName': 'cyclin dependent kinase inhibitor 2C',
'xrefOMIM': '603369',
'xrefHGNC': '1789',
'xrefEnsembl': 'ENSG00000123080',
'geneSymbol': 'CDKN2C',
'uri': 'http://jdr.bio/ontologies/comptox.owl#gene_cdkn2c',
'xrefNcbiGene': 1031},
{'typeOfGene': 'protein-coding',
'commonName': 'cyclin dependent kinase inhibitor 1A',
'xrefOMIM': '116899',
'xrefHGNC': '1784',
'xrefEnsembl': 'ENSG00000124762',
'geneSymbol': 'CDKN1A',
'uri': 'http://jdr.bio/ontologies/comptox.owl#gene_cdkn1a',
'xrefNcbiGene': 1026}]}
"""

result = dict()

if not isinstance(search_dict, Dict):
raise TypeError("search_dict should be a dictionary.")

for node_type, property_dict in search_dict.items():

if not isinstance(node_type, str):
raise TypeError("node_type should be str.")
property_match_substrings = []

for property, property_value in property_dict.items():
if isinstance(property_value, int):
property_match_substrings.append(
f"((n.{property}) = {property_value})"
)
elif isinstance(property_value, str):
property_match_substrings.append(
f"((n.{property}) = '{property_value}')"
)
elif isinstance(property_value, list):
property_match_substrings.append(
f"((n.{property}) IN {property_value})"
)
else:
raise TypeError(
"Property values should be str, int, or list of str or int."
)

prop_string = ", ".join(
[
f"{k}: '{v}'" if type(v) == str else f"{k}: {v}"
for k, v in properties.items()
]
)
match_substring = " OR ".join(property_match_substrings)

# Use a WHERE clause when multiple node types are given
if len(node_types) == 1:
# Only 1 node label - include it in the MATCH clause
match_clause = f"MATCH (n:{node_types[0]} {{ {prop_string} }})"
where_clause = ""
elif len(node_types) > 1:
# Multiple node labels - include them in the WHERE clause
match_clause = f"MATCH (n {{ {prop_string} }})"
where_clause = " WHERE n:" + " OR n:".join(node_types)
else:
# No node labels - just use bare MATCH clause and no WHERE clause
match_clause = f"MATCH (n {{ {prop_string} }})"
where_clause = ""
query = f"MATCH (n:{node_type}) WHERE {match_substring} RETURN n"

query = match_clause + where_clause + " RETURN n;"
print(query)
response = self.run_cypher(query)

print(query)
response = [node["n"] for node in response]

nodes_response = self.run_cypher(query)
result[node_type] = response

return (n["n"] for n in nodes_response)
return result

def find_relationships(self):
"""
Expand Down
13 changes: 10 additions & 3 deletions tests/test_chemical_featurizer.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -128,13 +128,20 @@ def test_retrieve_dtxsid(self):
["DTXSID8023135", "DTXSID5023742"],
)


class TestGenerate3DConformers:
def test_generate_3d_conformers(self):
mol_list = generate_3d_conformers(["CCN(CCO)CCCC(C)Nc1ccnc2cc(Cl)ccc12", "CC(=O)CC(c1ccccc1)c1c(O)c2ccccc2oc1=O"])
mol_list = generate_3d_conformers(
[
"CCN(CCO)CCCC(C)Nc1ccnc2cc(Cl)ccc12",
"CC(=O)CC(c1ccccc1)c1c(O)c2ccccc2oc1=O",
]
)

assert Descriptors.MolWt(mol_list[0]) == pytest.approx(335.8789999999995, 0.001)
assert Descriptors.MolWt(mol_list[1]) == pytest.approx(308.3329999999997, 0.001)


class TestCreateVectorTable:

def test_create_vector_table_original_chemical_ids_as_index(self):
Expand All @@ -147,7 +154,7 @@ def test_create_vector_table_original_chemical_ids_as_index(self):
)
expected_output_df_original_chemical_ids_as_index = pd.read_pickle(df_file_path)
assert create_vector_table(
["Hydroxychloroquine", "Warfarin"], molfeat_descriptors=['maccs', "erg"]
["Hydroxychloroquine", "Warfarin"], molfeat_descriptors=["maccs", "erg"]
).equals(expected_output_df_original_chemical_ids_as_index)

def test_create_vector_table_smiles_as_index(self):
Expand All @@ -159,6 +166,6 @@ def test_create_vector_table_smiles_as_index(self):
expected_output_df_smiles_as_index = pd.read_pickle(df_file_path)
assert create_vector_table(
["Hydroxychloroquine", "Warfarin"],
molfeat_descriptors=['maccs', "erg"],
molfeat_descriptors=["maccs", "erg"],
use_original_chemical_ids_for_df_index=False,
).equals(expected_output_df_smiles_as_index)
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