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decompose_groups.py
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decompose_groups.py
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import pandas as pd
import pdb
import json
from rdkit import Chem
def count_substructures(radius,molecule):
"""Helper function for get the information of molecular signature of a
metabolite. The relaxed signature requires the number of each substructure
to construct a matrix for each molecule.
Parameters
----------
radius : int
the radius is bond-distance that defines how many neighbor atoms should
be considered in a reaction center.
molecule : Molecule
a molecule object create by RDkit (e.g. Chem.MolFromInchi(inchi_code)
or Chem.MolToSmiles(smiles_code))
Returns
-------
dict
dictionary of molecular signature for a molecule,
{smiles: molecular_signature}
"""
m = molecule
smi_count = dict()
atomList = [atom for atom in m.GetAtoms()]
for i in range(len(atomList)):
env = Chem.FindAtomEnvironmentOfRadiusN(m,radius,i)
atoms=set()
for bidx in env:
atoms.add(m.GetBondWithIdx(bidx).GetBeginAtomIdx())
atoms.add(m.GetBondWithIdx(bidx).GetEndAtomIdx())
# only one atom is in this environment, such as O in H2O
if len(atoms) == 0:
atoms = {i}
smi = Chem.MolFragmentToSmiles(m,atomsToUse=list(atoms),
bondsToUse=env,canonical=True)
if smi in smi_count:
smi_count[smi] = smi_count[smi] + 1
else:
smi_count[smi] = 1
return smi_count
def decompse_ac(db_smiles,radius=1):
non_decomposable = []
decompose_vector = dict()
for cid in db_smiles:
# print cid
smiles_pH7 = db_smiles[cid]
try:
mol = Chem.MolFromSmiles(smiles_pH7)
mol = Chem.RemoveHs(mol)
# Chem.RemoveStereochemistry(mol)
smi_count = count_substructures(radius,mol)
decompose_vector[cid] = smi_count
except Exception as e:
non_decomposable.append(cid)
with open('./data/decompose_vector_ac.json','w') as fp:
json.dump(decompose_vector,fp)
def get_rxn_rule():
"""calculate reaction rules based on the relaxed molecular signatures.
Parameters
----------
radius : int
the radius is bond-distance that defines how many neighbor atoms should
be considered in a reaction center.
Returns
-------
None
All of the reaction rules are saved in files (csv file)
"""
reaction_dict = json.load(open('./data/optstoic_v3_Sji_dict.json'))
molecular_signature = json.load(open('./data/decompose_vector_ac.json'))
molsigna_df = pd.DataFrame.from_dict(molecular_signature).fillna(0)
all_mets = molsigna_df.columns.tolist()
all_mets.append("C00080")
all_mets.append("C00282")
rule_df = pd.DataFrame(index=molsigna_df.index)
for rid, value in list(reaction_dict.items()):
# skip the reactions with missing metabolites
mets = list(value.keys())
flag = False
for met in mets:
if met not in all_mets:
flag = True
break
if flag: continue
rule_df[rid] = 0
for met, stoic in list(value.items()):
if met == "C00080" or met == "C00282":
continue # hydogen is zero
rule_df[rid] += molsigna_df[met] * stoic
rule_df.to_csv("./data/reaction_rule.csv", index=True)
def get_rxn_rule_no_stero():
"""calculate reaction rules based on the relaxed molecular signatures.
Parameters
----------
radius : int
the radius is bond-distance that defines how many neighbor atoms should
be considered in a reaction center.
Returns
-------
None
All of the reaction rules are saved in files (csv file)
"""
reaction_dict = json.load(open('./data/optstoic_v3_Sji_dict.json'))
molecular_signature = json.load(open('./data/decompose_vector_ac_nostereo.json'))
molsigna_df = pd.DataFrame.from_dict(molecular_signature).fillna(0)
all_mets = molsigna_df.columns.tolist()
all_mets.append("C00080")
all_mets.append("C00282")
rule_df = pd.DataFrame(index=molsigna_df.index)
for rid, value in list(reaction_dict.items()):
# skip the reactions with missing metabolites
mets = list(value.keys())
flag = False
for met in mets:
if met not in all_mets:
flag = True
break
if flag: continue
rule_df[rid] = 0
for met, stoic in list(value.items()):
if met == "C00080" or met == "C00282":
continue # hydogen is zero
rule_df[rid] += molsigna_df[met] * stoic
rule_df.to_csv("./data/reaction_rule_no_stero.csv", index=True)
def get_rxn_rule_remove_TECRDB_mets():
"""calculate reaction rules based on the relaxed molecular signatures.
Parameters
----------
radius : int
the radius is bond-distance that defines how many neighbor atoms should
be considered in a reaction center.
Returns
-------
None
All of the reaction rules are saved in files (csv file)
"""
reaction_dict = json.load(open('./data/optstoic_v3_Sji_dict.json'))
molecular_signature = json.load(open('./data/decompose_vector_ac.json'))
molsigna_df = pd.DataFrame.from_dict(molecular_signature).fillna(0)
all_mets = molsigna_df.columns.tolist()
all_mets.append("C00080")
all_mets.append("C00282")
mets_TECRDB_df = pd.read_csv('./data/TECRBD_mets.txt',header=None)
mets_TECRDB = mets_TECRDB_df[0].tolist()
# pdb.set_trace()
all_mets = list(set(all_mets + mets_TECRDB))
rule_df = pd.DataFrame(index=molsigna_df.index)
for rid, value in list(reaction_dict.items()):
# skip the reactions with missing metabolites
mets = list(value.keys())
flag = False
for met in mets:
if met not in all_mets:
flag = True
break
if flag: continue
rule_df[rid] = 0
for met, stoic in list(value.items()):
if met in mets_TECRDB:
continue # hydogen is zero
rule_df[rid] += molsigna_df[met] * stoic
rule_df.to_csv("./data/reaction_rule_remove_TECRDB_mets.csv", index=True)
def get_rxn_rule_no_stero_remove_TECRDB_mets():
"""calculate reaction rules based on the relaxed molecular signatures.
Parameters
----------
radius : int
the radius is bond-distance that defines how many neighbor atoms should
be considered in a reaction center.
Returns
-------
None
All of the reaction rules are saved in files (csv file)
"""
reaction_dict = json.load(open('./data/optstoic_v3_Sji_dict.json'))
molecular_signature = json.load(open('./data/decompose_vector_ac_nostereo.json'))
molsigna_df = pd.DataFrame.from_dict(molecular_signature).fillna(0)
all_mets = molsigna_df.columns.tolist()
all_mets.append("C00080")
all_mets.append("C00282")
mets_TECRDB_df = pd.read_csv('./data/TECRBD_mets.txt',header=None)
mets_TECRDB = mets_TECRDB_df[0].tolist()
# pdb.set_trace()
all_mets = list(set(all_mets + mets_TECRDB))
rule_df = pd.DataFrame(index=molsigna_df.index)
for rid, value in list(reaction_dict.items()):
# skip the reactions with missing metabolites
mets = list(value.keys())
flag = False
for met in mets:
if met not in all_mets:
flag = True
break
if flag: continue
rule_df[rid] = 0
for met, stoic in list(value.items()):
if met in mets_TECRDB:
continue # hydogen is zero
rule_df[rid] += molsigna_df[met] * stoic
rule_df.to_csv("./data/reaction_rule_nostereo_remove_TECRDB_mets.csv", index=True)
if __name__ == '__main__':
# db = pd.read_csv('./data/cache_compounds_20160818.csv',index_col='compound_id')
# db_smiles = db['smiles_pH7'].to_dict()
# decompse_ac(db_smiles)
# get_rxn_rule()
# get_rxn_rule_remove_TECRDB_mets()
get_rxn_rule_no_stero_remove_TECRDB_mets()