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# -*- coding: utf-8 -*-
"""
Created on Wed Sep 7 14:55:39 2022
@author: DELL
"""
import numpy as np
import pandas as pd
from tqdm import tqdm
from matchms.Spectrum import Spectrum
from matchms.similarity import CosineGreedy
from rdkit import Chem, DataStructs
from rdkit.Chem import AllChem
from rdkit.Chem import rdFMCS
def disable_rdkit_logging():
"""
Disables RDKit whiny logging.
"""
import rdkit.rdBase as rkrb
import rdkit.RDLogger as rkl
logger = rkl.logger()
logger.setLevel(rkl.ERROR)
rkrb.DisableLog('rdApp.error')
disable_rdkit_logging()
get_fp = lambda x: AllChem.GetMorganFingerprintAsBitVect(x, radius=2)
get_sim = lambda x, y: DataStructs.DiceSimilarity(x, y)
def get_tagged_atoms_from_mol(mol):
'''Takes an RDKit molecule and returns list of tagged atoms and their
corresponding numbers'''
atoms = []
atom_tags = []
for atom in mol.GetAtoms():
if atom.HasProp('molAtomMapNumber'):
atoms.append(atom)
atom_tags.append(int(atom.GetProp('molAtomMapNumber')))
return atom_tags
def calc_frag_mass(frag):
'''Takes an RDKit fragment and returns the exact mass of the fragment'''
mass = 0
for ad in frag.GetAtoms():
mass += ad.GetMass()
return mass
def calc_possible_spectrum_loss(smiles_1, smiles_2):
"""
Calculate mass difference between related fragments of two compounds.
Arguments:
smiles_1, smiles_2: str, two different smiles of compounds.
Returns:
DataFrame,
transform, transformation from smiles_1 to smiles_2.
loss, corresponding neutral loss of the transformations.
Example:
smiles_1 = 'COc1cc(O)c2c(c1)OC(c1ccc(O)cc1)CC2=O'
smiles_2 = 'CC1OC(OCC2OC(Oc3cc(O)c4c(c3)OC(c3ccc(O)cc3)CC4=O)C(O)C(O)C2O)C(O)C(O)C1O'
calc_possible_spectrum_loss(smiles_1, smiles_2)
"""
try:
x = Chem.MolToSmiles(Chem.MolFromSmiles(smiles_1))
y = Chem.MolToSmiles(Chem.MolFromSmiles(smiles_2))
except:
return None
mol1 = Chem.AddHs(Chem.MolFromSmiles(x))
mol2 = Chem.AddHs(Chem.MolFromSmiles(y))
if get_sim(get_fp(mol1), get_fp(mol2)) < 0.3:
return None
mcs = rdFMCS.FindMCS([mol1, mol2], bondCompare=rdFMCS.BondCompare.CompareOrderExact,
matchValences = True, ringMatchesRingOnly = True)
if mcs.numAtoms <= 5:
return None
mcs_str = mcs.smartsString
rdu1 = AllChem.DeleteSubstructs(mol1, Chem.MolFromSmarts(mcs_str))
rdu2 = AllChem.DeleteSubstructs(mol2, Chem.MolFromSmarts(mcs_str))
try:
rdu1 = Chem.GetMolFrags(rdu1, asMols=True)
except:
rdu1 = np.array([rdu1])
try:
rdu2 = Chem.GetMolFrags(rdu2, asMols=True)
except:
rdu2 = np.array([rdu2])
if (len(rdu1) == 0) and (len(rdu2) == 0):
return None
mass_1 = np.array([calc_frag_mass(m) for m in rdu1])
mass_2 = np.array([calc_frag_mass(m) for m in rdu2])
if len(mass_1) == 0:
mass_1 = np.array([0])
if len(mass_2) == 0:
mass_2 = np.array([0])
mass_diffs, mol_transform = [], []
for i in range(len(mass_1)):
for j in range(len(mass_2)):
try:
a = Chem.MolToSmiles(Chem.RemoveHs(rdu1[i]))
except:
a = 'None'
try:
b = Chem.MolToSmiles(Chem.RemoveHs(rdu2[j]))
except:
b = 'None'
mol_transform.append('{}>>{}'.format(a,b))
mass_diffs.append(mass_2[j] - mass_1[i])
return pd.DataFrame({'transform': mol_transform, 'loss': mass_diffs})
def calc_aligned_similarity(smiles_1, smiles_2, spectrum_1, spectrum_2, mz_tol=0.05, similarity_function=CosineGreedy()):
"""
Calculate dtw similarity between two spectrums.
Arguments:
smiles_1, smiles_2: str, two different smiles of compounds.
spectrum_1, spectrum_2: Two different spectrum of matchms.
Returns:
similarity: float, similarity between aligned spectrums.
matching_data: DataFrame, fragment matching information.
Example:
smiles_1 = 'CCCC=C1C2=CC=CC=C2C(=O)O1'
smiles_2 = 'CCCC=C1C2=C(C=CCC2)C(=O)O1'
spectrum_1 = Spectrum(mz = np.array([91.1, 115.1, 117.1, 128.1, 129.1, 143.1, 145.1, 152.1, 153.1, 171.1, 189.1]),
intensities = np.array([0.12314933, 0.10446688, 0.16478671, 0.56083889, 0.11087135,
0.43528005, 0.1149675 , 0.10339803, 0.51058281, 0.999999, 0.88490263]),
metadata={"precursor_mz": 189.0909})
spectrum_2 = Spectrum(mz = np.array([ 79.1, 93.1, 105.1, 117.1, 145.1, 173.1, 191.1]),
intensities = np.array([0.10704697, 0.10657389, 0.1382483 , 0.12679477, 0.16397634,
0.26150501, 0.999999]),
metadata={"precursor_mz": 191.1064})
calc_aligned_similarity(smiles_1, smiles_2, spectrum_1, spectrum_2)
"""
loss = calc_possible_spectrum_loss(smiles_1, smiles_2)
if loss is None:
loss_1 = loss_2 = 0
loss = pd.DataFrame({'transform': [], 'loss': []})
else:
loss_1 = -sum([l for l in list(loss['loss']) if l < 0])
loss_2 = sum([l for l in list(loss['loss']) if l > 0])
mcs1 = 9999
mcs2 = 9999
try:
mcs1 = spectrum_1.metadata['precursor_mz'] - loss_1
except:
pass
try:
mcs2 = spectrum_2.metadata['precursor_mz'] - loss_2
except:
pass
maxCS = min(mcs1, mcs2)
if (len(spectrum_1.mz) == 0) or (len(spectrum_2.mz) == 0):
return 0, None
x_mz, x_intensities = spectrum_1.mz, spectrum_1.intensities
y_mz, y_intensities = spectrum_2.mz, spectrum_2.intensities
y_mz_new, y_intensities_new = [], []
matching_data = []
for i, y_mz_ in enumerate(y_mz):
if y_intensities[i] < 0.01:
continue
if np.min(np.abs(y_mz_ - x_mz)) <= mz_tol:
if y_mz_ > maxCS + 2.006:
continue
a = y_mz_
b = x_mz[np.argmin(np.abs(y_mz_ - x_mz))]
c = abs(a - b)
d = y_intensities[i]
y_mz_new.append(y_mz_)
y_intensities_new.append(y_intensities[i])
matching_data.append([a, b, c, d])
else:
matched = False
for loss_ in loss['loss']:
'''
if y_mz_ - loss_ > maxCS - loss_ + 2.006:
continue
'''
if np.min(np.abs(y_mz_ - loss_ - x_mz)) <= mz_tol:
matched = True
a = y_mz_
b = x_mz[np.argmin(np.abs(y_mz_ - loss_ - x_mz))]
c = abs(a - b)
d = y_intensities[i]
y_mz_new.append(y_mz_ - loss_)
y_intensities_new.append(y_intensities[i])
matching_data.append([a, b, c, d])
break
if not matched:
y_mz_new.append(y_mz_)
y_intensities_new.append(y_intensities[i])
y_mz_new = np.array(y_mz_new)
y_intensities_new = np.array(y_intensities_new)
index = np.argsort(y_mz_new)
y_mz_new = y_mz_new[index]
y_intensities_new = y_intensities_new[index]
spectrum_2_aligned = Spectrum(mz = y_mz_new,
intensities = y_intensities_new,
metadata = spectrum_2.metadata)
similarity = float(similarity_function.pair(spectrum_1, spectrum_2_aligned)['score'])
matching_data = pd.DataFrame(matching_data, columns = ['reference', 'query', 'loss', 'intensity'])
return similarity, matching_data
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