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 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 main():
# load data
cells = load_1col(cell2idfile, 1)
# load input data
inputfile = sys.argv[1]
inputdrug = load_1col(inputfile, 0)[0]
outputdir = sys.argv[2] + "/"
# build morgan fingerprint for the input drug
d = Chem.MolFromSmiles(inputdrug)
f = list(
map(
str,
list(SimilarityMaps.GetMorganFingerprint(d, fpType='bv',
radius=2))))
# print out new genotype input file
with open(outputdir + "input_drug_fingerprint.txt", 'w') as fo:
fo.write("%s\n" % ','.join(f))
fo.write("%s\n" % ','.join(f))
# generate new input cell2ind file
with open(outputdir + "input_drug2id.txt", 'w') as fo:
fo.write("0\t%s\n" % inputdrug)
fo.write("1\tdummy_%s\n" % inputdrug)
# generate new input data file for prediction
with open(outputdir + "input.txt", 'w') as fo:
for c in cells:
fo.write("%s\t%s\t-1\n" % (c, inputdrug))
def get_circular_similarity(correct_ligand,
mol_to_fix,
type_fp='bv',
use_features=False):
type_fp = type_fp
correct_ligand_fingerprint = SimilarityMaps.GetMorganFingerprint(
correct_ligand,
radius=1,
nBits=4096,
fpType=type_fp,
useFeatures=use_features)
mol_to_fix_fingerprint = SimilarityMaps.GetMorganFingerprint(
mol_to_fix,
radius=1,
nBits=4096,
fpType=type_fp,
useFeatures=use_features)
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.GetMorganFingerprint(
m, atomId=idx, radius=1, fpType='bv'),
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.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 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])
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 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 fp_maps(results, query, fingerprint, fpradius, nbits, features, metric,
out_file, multfile):
if fingerprint == "ecfp" or fingerprint == "fcfp":
fp_func = lambda m, idx: SimilarityMaps.GetMorganFingerprint(
m,
atomId=idx,
radius=fpradius,
fpType='bv',
nBits=nbits,
useFeatures=features)
elif fingerprint == "rdkit":
fp_func = lambda m, idx: SimilarityMaps.GetRDKFingerprint(
m, atomId=idx, fpType="bv", nBits=nbits)
elif fingerprint == "ap":
fp_func = lambda m, idx: SimilarityMaps.GetAPFingerprint(
m, atomId=idx, fpType="bv", nBits=nbits)
else:
fp_func = lambda m, idx: SimilarityMaps.GetTTFingerprint(
m, atomId=idx, fpType="bv", nBits=nbits)
if multfile:
sim_map_mf(results, query, fp_func, metric, out_file)
else:
sim_map(results, query, fp_func, metric, 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()
def fpFunction(mol,atomId=-1):
fp = SimilarityMaps.GetMorganFingerprint(mol,
atomId=atomId,
radius=radius,
nBits=nBits)
return fp
# 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)
print(['%.2f' % w for w in weights])
#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)
def getCircular(mol, nBits=1024):
if nBits == 1024:
return AllChem.GetMorganFingerprintAsBitVect(mol, 2, nBits=1024)
else:
return SimilarityMaps.GetMorganFingerprint(mol, fpType='bv')
def genFP(mol, Dummy=-1):
fp = SimilarityMaps.GetMorganFingerprint(mol)
fp_vect = np.zeros((1, ))
DataStructs.ConvertToNumpyArray(fp, fp_vect)
return fp_vect
def genFP(mol,Dummy=-1):
# Helper function to convert to Morgan type fingerprint in Numpy Array
fp = SimilarityMaps.GetMorganFingerprint(mol)
fp_vect = np.zeros((1,))
DataStructs.ConvertToNumpyArray(fp, fp_vect)
return fp_vect