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 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 testSimilarityMapKWArgs(self):
# Morgan2 BV
m1 = Chem.MolFromSmiles('CC[C@](F)(Cl)c1ccccc1')
m2 = Chem.MolFromSmiles('CC[C@@](F)(Cl)c1ccccc1')
weights = sm.GetAtomicWeightsForFingerprint(
m1, m2, lambda m, i: sm.GetAPFingerprint(
m, atomId=i, includeChirality=False))
for w in weights:
self.assertAlmostEqual(w, 0.100, 4)
weights = sm.GetAtomicWeightsForFingerprint(
m1, m2, lambda m, i: sm.GetAPFingerprint(
m, atomId=i, includeChirality=True))
for i, w in enumerate(weights):
if i != 2:
self.assertAlmostEqual(w, 0.098, 3)
else:
self.assertAlmostEqual(w, -0.082, 3)
weights = sm.GetAtomicWeightsForFingerprint(
m1, m2, lambda m, i: sm.GetTTFingerprint(
m, atomId=i, includeChirality=False))
for w in weights:
self.assertTrue(w > 0.0)
weights = sm.GetAtomicWeightsForFingerprint(
m1, m2, lambda m, i: sm.GetTTFingerprint(
m, atomId=i, includeChirality=True))
for i, w in enumerate(weights):
if i > 4:
self.assertTrue(w > 0.0)
else:
self.assertTrue(w < 0.0)
weights = sm.GetAtomicWeightsForFingerprint(
m1, m2, lambda m, i: sm.GetMorganFingerprint(
m, radius=1, atomId=i, useChirality=False))
weights2 = sm.GetAtomicWeightsForFingerprint(
m1, m2, lambda m, i: sm.GetMorganFingerprint(
m, radius=1, atomId=i, useChirality=True))
# testing explicit values here seems silly, just check that the contribution of the
# chiral center drops:
self.assertTrue(weights[2] > weights2[2])
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)
print(['%.2f' % w for w in weights])
# ['0.11', '0.11', '0.08', '0.07', '-0.03', '0.07', '0.02']
target_mol_simi_fig = SimilarityMaps.GetSimilarityMapFromWeights(mol, weights)