def testSimilarityMapsMolDraw2D(self):
# nothing really sensible to test here, just make sure things run
mol = Chem.MolFromSmiles(
'COc1cccc2cc(C(=O)NCCCCN3CCN(c4cccc5nccnc54)CC3)oc21')
refmol = Chem.MolFromSmiles(
'CCCN(CCCCN1CCN(c2ccccc2OC)CC1)Cc1ccc2ccccc2c1')
d = Draw.MolDraw2DSVG(400, 400)
d.ClearDrawing()
_, maxWeight = sm.GetSimilarityMapForFingerprint(
refmol,
mol,
lambda m, i: sm.GetMorganFingerprint(m, i, radius=2, fpType='bv'),
draw2d=d)
d.FinishDrawing()
with open('similarityMap1_out.svg', 'w+') as outf:
outf.write(d.GetDrawingText())
# Github #2904: make sure we can provide our own colormap as a list:
colors = [(0, 1, 0, 0.5), (1, 1, 1), (0, 0, 1, 0.5)]
d = Draw.MolDraw2DSVG(400, 400)
d.ClearDrawing()
_, maxWeight = sm.GetSimilarityMapForFingerprint(
refmol,
mol,
lambda m, i: sm.GetMorganFingerprint(m, i, radius=2, fpType='bv'),
draw2d=d,
colorMap=colors)
d.FinishDrawing()
with open('similarityMap1_out2.svg', 'w+') as outf:
outf.write(d.GetDrawingText())
# Github #2904: make sure we can provide our own colormap as a matplotlib colormap:
try:
from matplotlib import cm
d = Draw.MolDraw2DSVG(400, 400)
d.ClearDrawing()
_, maxWeight = sm.GetSimilarityMapForFingerprint(
refmol,
mol,
lambda m, i: sm.GetMorganFingerprint(
m, i, radius=2, fpType='bv'),
draw2d=d,
colorMap=cm.PiYG)
d.FinishDrawing()
with open('similarityMap1_out3.svg', 'w+') as outf:
outf.write(d.GetDrawingText())
except ImportError:
pass
def _similarityMapSVG(ms,
width=500,
height=500,
radius=2,
fingerprint='morgan',
format='svg'):
if matplotlib is None:
raise ValueError('matplotlib not useable')
_call(ms, 'UpdatePropertyCache', strict=False)
_apply(ms, ct._sssr)
fn = None
if fingerprint == 'morgan':
fn = lambda x, i: SimilarityMaps.GetMorganFingerprint(
x, i, radius=radius)
elif fingerprint == 'tt':
fn = SimilarityMaps.GetAPFingerprint
elif fingerprint == 'ap':
fn = SimilarityMaps.GetTTFingerprint
SimilarityMaps.GetSimilarityMapForFingerprint(ms[0],
ms[1],
fn,
size=(width, height))
sio = io.StringIO()
pyplot.savefig(sio, format=format, bbox_inches='tight', dpi=100)
return sio.getvalue()
def sim_map_mf(results, query, fp_func, metric, out_file):
file_counter = 1
for m in query:
mol_keys = []
for i in results:
if results[i]["q_num"] == m:
Chem.Compute2DCoords(results[i]["mol"])
try:
fig, maxweight = SimilarityMaps.GetSimilarityMapForFingerprint(
query[results[i]["q_num"]]["mol"],
results[i]["mol"],
fp_func,
metric=fpsearch.sim_dict[metric])
fig.set_figwidth(3.255)
fig.set_figheight(3.255)
filename = f"{i}.png"
fig.savefig(filename, bbox_inches="tight")
mol_keys.append(i)
except:
d = rdMolDraw2D.MolDraw2DCairo(600, 600)
d.DrawMolecule(results[i]["mol"])
d.FinishDrawing()
d.WriteDrawingText(f"{i}.png")
mol_keys.append(i)
pages = gen_pdf_pages(mol_keys, results)
export_pdf(pages, f"{out_file}_{file_counter}.pdf")
file_counter += 1
def morgan_fingerprint_vis(product_smiles, sim_smiles):
mol = Chem.MolFromSmiles(sim_smiles)
refmol = Chem.MolFromSmiles(product_smiles)
fp1 = SimilarityMaps.GetMorganFingerprint(mol)
fp2 = SimilarityMaps.GetMorganFingerprint(refmol)
fig, maxweight = SimilarityMaps.GetSimilarityMapForFingerprint(
refmol, mol, SimilarityMaps.GetMorganFingerprint)
return DataStructs.FingerprintSimilarity(fp1, fp2)
def draw_mol_comparison(self, refmol, mol):
'''
Compare the structure of two molecules.
The function takes the molecule names as input.
'''
fig, maxweight = SimilarityMaps.GetSimilarityMapForFingerprint(
refMol=self.molecules(refmol),
probeMol=self.molecules(mol),
fpFunction=SimilarityMaps.GetMorganFingerprint,
metric=DataStructs.TanimotoSimilarity)
return fig, maxweight
def draw_map():
""" Creates similarity maps using the provided molecules (SMILES).
"""
m1 = Chem.MolFromSmiles('c1ccccc1O')
m2 = Chem.MolFromSmiles('c1ccccc1N')
# Morgan Fingerprint (with normalization)
# Can also be used with APFingerprint or TTFingerprint
fig1, maxweight = SimilarityMaps.GetSimilarityMapForFingerprint(
m1, m2, SimilarityMaps.GetMorganFingerprint)
fig1.savefig('/path/to/similaritymap.png', bbox_inches='tight')
# TT Fingerprint (with normalization)
fig2, maxweight = SimilarityMaps.GetSimilarityMapForFingerprint(
m1, m2, SimilarityMaps.GetTTFingerprint)
fig2.savefig('/path/to/similaritymap.png', bbox_inches='tight')
# Morgan Fingerprint (without normalization)
weights = SimilarityMaps.GetAtomicWeightsForFingerprint(
m1, m2, SimilarityMaps.GetMorganFingerprint)
fig3 = SimilarityMaps.GetSimilarityMapFromWeights(m2,
weights,
size=(150, 150))
fig3.savefig('/path/to/similaritymap.png', bbox_inches='tight')
# the degree of partial charge by using atomic charge
AllChem.ComputeGasteigerCharges(m1)
charges = [
float(atom.GetProp('_GasteigerCharge')) for atom in m1.GetAtoms()
]
fig4 = SimilarityMaps.GetSimilarityMapFromWeights(m2,
charges,
size=(150, 150),
scale=10)
fig4.savefig('/path/to/molcharge_similaritymap.png', bbox_inches='tight')
def draw_finger_print(smile_file):
with open(smile_file, 'r') as f:
for mol in f:
refmol = Chem.MolFromSmiles(
'CCCN(CCCCN1CCN(c2ccccc2OC)CC1)Cc1ccc2ccccc2c1'
) # this is the template here
ms = Chem.MolFromSmiles(mol)
fp = SimilarityMaps.GetAPFingerprint(ms, fpType='normal')
fp = SimilarityMaps.GetTTFingerprint(ms, fpType='normal')
fp = SimilarityMaps.GetMorganFingerprint(ms, fpType='bv')
fig, maxweight = SimilarityMaps.GetSimilarityMapForFingerprint(
refmol, ms, SimilarityMaps.GetMorganFingerprint)
fig.save('/home/nad/Desktop/navid22.png')
def _similarityMap(ms, width=100, height=100, radius=2, fingerprint = 'morgan'):
if matplotlib is None:
raise ValueError('matplotlib not useable')
if fingerprint=='morgan':
fn = lambda x,i:SimilarityMaps.GetMorganFingerprint(x,i,radius=radius)
elif fingerprint=='tt':
fn = SimilarityMaps.GetAPFingerprint
elif fingerprint=='ap':
fn = SimilarityMaps.GetTTFingerprint
fig, maxv = SimilarityMaps.GetSimilarityMapForFingerprint(ms[0], ms[1], fn, size=(width, height))
sio = StringIO.StringIO()
pyplot.savefig(sio, format='png', bbox_inches='tight', dpi=100)
return sio.getvalue()
def testSimilarityMapsMolDraw2D(self):
# nothing really sensible to test here, just make sure things run
mol = Chem.MolFromSmiles(
'COc1cccc2cc(C(=O)NCCCCN3CCN(c4cccc5nccnc54)CC3)oc21')
refmol = Chem.MolFromSmiles(
'CCCN(CCCCN1CCN(c2ccccc2OC)CC1)Cc1ccc2ccccc2c1')
d = Draw.MolDraw2DSVG(400, 400)
d.ClearDrawing()
_, maxWeight = sm.GetSimilarityMapForFingerprint(
refmol,
mol,
lambda m, i: sm.GetMorganFingerprint(m, i, radius=2, fpType='bv'),
draw2d=d)
d.FinishDrawing()
with open('similarityMap1_out.svg', 'w+') as outf:
outf.write(d.GetDrawingText())
def get_atom_pair_similarity(correct_ligand,
mol_to_fix,
type_fp='normal',
use_features=False):
type_fp = type_fp
correct_ligand_fingerprint = SimilarityMaps.GetAPFingerprint(
correct_ligand,
nBits=4096,
fpType=type_fp,
)
mol_to_fix_fingerprint = SimilarityMaps.GetAPFingerprint(
mol_to_fix,
nBits=4096,
fpType=type_fp,
)
test = 1
sim_func = DataStructs.FingerprintSimilarity
temp_autoinducer_TanimotoSimilarty = sim_func(
correct_ligand_fingerprint,
mol_to_fix_fingerprint,
metric=DataStructs.TanimotoSimilarity)
test = 1
curr_metric = DataStructs.TanimotoSimilarity
fig, maxweight = SimilarityMaps.GetSimilarityMapForFingerprint(
correct_ligand,
mol_to_fix,
lambda m, idx: SimilarityMaps.GetAPFingerprint(
m, atomId=idx, fpType='normal'),
metric=curr_metric)
# print(maxweight)
# fig.suptitle('test title', fontsize=20)
ax = fig.gca()
# ax.title
plt.title('test', fontsize=30)
fig.set_size_inches(7, 7)
fig.set_dpi(600)
fig.savefig('test_ap.png', bbox_inches='tight')
test = 1
def testSimilarityMap(self):
# Morgan2 BV
refWeights = [0.5, 0.5, 0.5, -0.5, 0.5, 0.5]
weights = sm.GetAtomicWeightsForFingerprint(self.mol1, self.mol2, lambda m, i: sm.GetMorganFingerprint(m, i, radius=2, fpType='bv'))
for w,r in zip(weights, refWeights): self.assertEqual(w, r)
fig, maxWeight = sm.GetSimilarityMapForFingerprint(self.mol1, self.mol2, lambda m, i: sm.GetMorganFingerprint(m, i, radius=2, fpType='bv'))
self.assertEqual(maxWeight, 0.5)
weights, maxWeight = sm.GetStandardizedWeights(weights)
self.assertEqual(maxWeight, 0.5)
refWeights = [1.0, 1.0, 1.0, -1.0, 1.0, 1.0]
for w,r in zip(weights, refWeights): self.assertEqual(w, r)
weights = sm.GetAtomicWeightsForFingerprint(self.mol1, self.mol2, lambda m, i: sm.GetMorganFingerprint(m, i, fpType='count'))
self.assertTrue(weights[3] < 0)
weights = sm.GetAtomicWeightsForFingerprint(self.mol1, self.mol2, lambda m, i: sm.GetMorganFingerprint(m, i, fpType='bv', useFeatures=True))
self.assertTrue(weights[3] < 0)
# hashed AP BV
refWeights = [0.09523, 0.17366, 0.17366, -0.23809, 0.17366, 0.17366]
weights = sm.GetAtomicWeightsForFingerprint(self.mol1, self.mol2, lambda m, i: sm.GetAPFingerprint(m, i, fpType='bv', nBits=1024))
for w,r in zip(weights, refWeights): self.assertAlmostEqual(w, r, 4)
weights = sm.GetAtomicWeightsForFingerprint(self.mol1, self.mol2, lambda m, i: sm.GetAPFingerprint(m, i, fpType='normal'))
self.assertTrue(weights[3] < 0)
weights = sm.GetAtomicWeightsForFingerprint(self.mol1, self.mol2, lambda m, i: sm.GetAPFingerprint(m, i, fpType='hashed'))
self.assertTrue(weights[3] < 0)
# hashed TT BV
refWeights = [0.5, 0.5, -0.16666, -0.5, -0.16666, 0.5]
weights = sm.GetAtomicWeightsForFingerprint(self.mol1, self.mol2, lambda m, i: sm.GetTTFingerprint(m, i, fpType='bv', nBits=1024, nBitsPerEntry=1))
for w,r in zip(weights, refWeights): self.assertAlmostEqual(w, r, 4)
weights = sm.GetAtomicWeightsForFingerprint(self.mol1, self.mol2, lambda m, i: sm.GetTTFingerprint(m, i, fpType='normal'))
self.assertTrue(weights[3] < 0)
weights = sm.GetAtomicWeightsForFingerprint(self.mol1, self.mol2, lambda m, i: sm.GetTTFingerprint(m, i, fpType='hashed'))
self.assertTrue(weights[3] < 0)
# RDK fingerprint BV
refWeights = [0.42105, 0.42105, 0.42105, -0.32895, 0.42105, 0.42105]
weights = sm.GetAtomicWeightsForFingerprint(self.mol1, self.mol2, lambda m, i: sm.GetRDKFingerprint(m, i, nBits=1024, nBitsPerHash=1))
for w,r in zip(weights, refWeights): self.assertAlmostEqual(w, r, 4)
def testGithub4763(self):
mol = Chem.MolFromSmiles(
'COc1cccc2cc(C(=O)NCCCCN3CCN(c4cccc5nccnc54)CC3)oc21')
refmol = Chem.MolFromSmiles(
'CCCN(CCCCN1CCN(c2ccccc2OC)CC1)Cc1ccc2ccccc2c1')
d = Draw.MolDraw2DSVG(400, 400)
d.ClearDrawing()
_, maxWeight = sm.GetSimilarityMapForFingerprint(
refmol,
mol,
lambda m, i: sm.GetMorganFingerprint(m, i, radius=2, fpType='bv'),
draw2d=d,
colorMap="coolwarm")
d.FinishDrawing()
svg = d.GetDrawingText()
with open('github4763.svg', 'w+') as outf:
outf.write(svg)
self.assertFalse('fill:#FBFCFB7F' in svg)
self.assertTrue('fill:#DDDCDB' in svg)
def sim_map(results, query, fp_func, metric, out_file):
for i in results:
Chem.Compute2DCoords(results[i]["mol"])
try:
fig, maxweight = SimilarityMaps.GetSimilarityMapForFingerprint(
query[results[i]["q_num"]]["mol"],
results[i]["mol"],
fp_func,
metric=fpsearch.sim_dict[metric])
fig.set_figwidth(3.255)
fig.set_figheight(3.255)
filename = f"{i}.png"
fig.savefig(filename, bbox_inches="tight")
except:
d = rdMolDraw2D.MolDraw2DCairo(600, 600)
d.DrawMolecule(results[i]["mol"])
d.FinishDrawing()
d.WriteDrawingText(f"{i}.png")
pages = gen_pdf_pages(list(results.keys()), results)
export_pdf(pages, out_file)
def _similarityMap(ms, params):
if matplotlib is None:
raise ValueError('matplotlib not useable')
fp = params.get('fingerprint', 'morgan')
if fp == 'morgan':
rad = int(params.get('radius', 2))
fn = lambda x, i: SimilarityMaps.GetMorganFingerprint(x, i, radius=rad)
elif fp == 'tt':
fn = SimilarityMaps.GetAPFingerprint
elif fp == 'ap':
fn = SimilarityMaps.GetTTFingerprint
w = int(params.get('w', 100))
h = int(params.get('h', 100))
fig, maxv = SimilarityMaps.GetSimilarityMapForFingerprint(ms[0],
ms[1],
fn,
size=(w, h))
sio = StringIO.StringIO()
pyplot.savefig(sio, format='png', bbox_inches='tight', dpi=100)
return sio.getvalue()
# 目标分子
# 指纹函数
# 相似性函数（默认是 Dice similarity）
# 目标分子
targetmol = Chem.MolFromSmiles(
'COc1cccc2cc(C(=O)NCCCCN3CCN(c4cccc5nccnc54)CC3)oc21')
# 参考分子
refmol = Chem.MolFromSmiles('CCCN(CCCCN1CCN(c2ccccc2OC)CC1)Cc1ccc2ccccc2c1')
d = Draw.MolDraw2DSVG(400, 400)
d.ClearDrawing()
target_mol_simi_fig, maxweight = SimilarityMaps.GetSimilarityMapForFingerprint(
refmol,
targetmol,
lambda m, i: SimilarityMaps.GetMorganFingerprint(
m, i, radius=2, fpType='bv'),
draw2d=d
)
print(target_mol_simi_fig) # Figure(250x250)
print(maxweight) # 0.12255947497949138
d.FinishDrawing()
with open('/drug_development/studyRdkit/st_rdcit/img/mol28.svg', 'w+') as outf:
outf.write(d.GetDrawingText())
# 原子颜色越绿，对相似性的贡献越大。
# 或者可以用以下方法
weights = SimilarityMaps.GetAtomicWeightsForFingerprint(
refmol, mol, SimilarityMaps.GetMorganFingerprint)
def calculate_similarities(gd, gd_mlc, request, sr):
user_id = request.user.id
print("user id of current user is ", user_id)
instances = Similar.objects.filter(user_id=user_id)
instances.delete()
results = Prediction.objects.filter(user_id=user_id)
results.delete()
gd.reset_index(inplace=True)
gd = gd.rename(columns={'index': 'Compounds'})
gd_mlc.reset_index(inplace=True)
gd_mlc = gd_mlc.rename(columns={'index': 'Compounds'})
print("calculating similarities", gd.columns, gd_mlc.columns)
binary_prob = gd
mlc_prob = gd_mlc
compounds = binary_prob["Compounds"].tolist()
prob_data = {}
targets = binary_prob.columns[1:len(binary_prob.columns)]
similar_compounds = {}
target_ids = {
'EGFR': 'CHEMBL203',
'IGF1R': 'CHEMBL1957',
'MIA-PaCa': 'CHEMBL614725',
'mTOR': 'CHEMBL2842'
}
train_data = pd.read_csv('Static/data/train-data.csv')
k = 0
start = time.time()
for c in compounds:
prob_data[c] = {}
img_name = uuid.uuid4().hex
id = uuid.uuid4().hex
j = 0
active = False
for i in range(0, len(targets)):
prob_data[c][targets[i]] = {}
prob_model1 = binary_prob[binary_prob["Compounds"] == c][
targets[i]].tolist()[0]
prob_model2 = mlc_prob[mlc_prob["Compounds"] == c][
targets[i]].tolist()[0]
prob_data[c][targets[i]]['probability'] = round(
((prob_model1 + prob_model2) / 2), 2)
if prob_data[c][targets[i]]['probability'] >= 50:
print("inside if")
active = True
tid = target_ids[targets[i]]
smiles = train_data[train_data["target_chembl_ID"] ==
tid]["canonical_smiles"].tolist()
mol1 = Chem.MolFromSmiles(c)
fp0 = FingerprintMols.FingerprintMol(mol1)
compound_fig = Draw.MolToMPL(mol1, size=(120, 120))
# image_cf = img_name + '_qmol_.png'
# compound_fig.savefig('Static/pictures/similarity/' + image_cf, bbox_inches='tight')
# #com = Compound()
# new_image = Similarity(name = image_cf, user_id = request.user.id, type = 'query',
# image_id = id, similarity = 0.0 )
# new_image.save()
similar_compounds[c] = []
for s in smiles:
if s != c:
mol2 = Chem.MolFromSmiles(s)
fp1 = FingerprintMols.FingerprintMol(mol2)
similarity = DataStructs.TanimotoSimilarity(fp0, fp1)
if similarity > 0.9:
fig, maxweight = SimilarityMaps.GetSimilarityMapForFingerprint(
mol2,
mol1,
SimilarityMaps.GetMorganFingerprint,
metric=DataStructs.TanimotoSimilarity)
name = img_name + '_' + str(j) + '.png'
fig.savefig('Static/pictures/similarity/' + name,
bbox_inches='tight',
pad_inches=0,
transparent=True,
orientation="landscape")
new_image = Similar(name=name,
user_id=user_id,
type='similar',
image_id=id,
similarity=similarity,
smile=s)
new_image.save()
r_compound_fig = Draw.MolToMPL(mol2,
size=(120, 120))
image_rf = img_name + '_rmol_' + str(j) + '.png'
r_compound_fig.savefig(
'Static/pictures/similarity/' + image_rf,
bbox_inches='tight')
new_image = Similar(name=image_rf,
user_id=user_id,
type='response',
image_id=id,
similarity=similarity,
smile=s)
new_image.save()
# new_sim = {'smile': s, 'similarity': round(similarity, 2), 'image': name, 'query': image_cf,
# 'result': image_rf}
# similar_compounds[c].append(new_sim)
j += 1
matplotlib.pyplot.clf()
k += 1
if active == True:
print("Saving active compound")
mol1 = Chem.MolFromSmiles(c)
compound_fig = Draw.MolToMPL(mol1, size=(120, 120))
image_cf = img_name + '_qmol_.png'
compound_fig.savefig('Static/pictures/similarity/' + image_cf,
bbox_inches='tight')
# com = Compound()
new_image = Similar(name=image_cf,
user_id=request.user.id,
type='query',
image_id=id,
similarity=0.0,
smile=c)
new_image.save()
new_results = Prediction(user_id=request.user.id, data=prob_data)
new_results.save()
end = time.time()
print('elapsed time ', end - start)
request.session['results'] = True
request.session.save()
request.session['set_res'] = True
request.session.modified = True
return redirect('dashboard')
#name: Similarity Maps Using Fingerprints
#description: Similarity Maps Using Fingerprints, RDKit based
#help-url: /help/domains/chem/functions/sim-maps.md
#language: python
#tags: demo, chem, rdkit
#input: string mol = COc1cccc2cc(C(=O)NCCCCN3CCN(c4cccc5nccnc54)CC3)oc21 {semType: Molecule} [Molecule, in SMILES format]
#input: string refmol = CCCN(CCCCN1CCN(c2ccccc2OC)CC1)Cc1ccc2ccccc2c1 {semType: Molecule} [Reference molecule, in SMILES format]
#input: int radius = 2 [Fingerprint function radius]
#output: double maxweight [The maximum weight that was found when creating the map]
#output: graphics simMap [Similarity Map]
from rdkit import Chem
from rdkit.Chem.Draw import SimilarityMaps
mol = Chem.MolFromSmiles(mol)
refmol = Chem.MolFromSmiles(refmol)
from rdkit import DataStructs
simMap, maxweight = SimilarityMaps.GetSimilarityMapForFingerprint(
refmol,
mol,
lambda m, idx: SimilarityMaps.GetMorganFingerprint(
m, atomId=idx, radius=radius, fpType='count'),
metric=DataStructs.TanimotoSimilarity)
from rdkit import Chem
from rdkit.Chem.Draw import SimilarityMaps
import matplotlib.pyplot as plt
if __name__ == '__main__':
mol = Chem.MolFromSmiles('COc1cccc2cc(C(=O)NCCCCN3CCN(c4cccc5nccnc54)CC3)oc21')
refmol = Chem.MolFromSmiles('CCCN(CCCCN1CCN(c2ccccc2OC)CC1)Cc1ccc2ccccc2c1')
fig, maxweight = SimilarityMaps.GetSimilarityMapForFingerprint(refmol, mol, SimilarityMaps.GetMorganFingerprint)
handlelim = [0, 1]
# plt.xlim(handlelim)
# plt.ylim(handlelim)
# plt.show()