def get_2dpharm(mols, fp_size=2000):
factory = Gobbi_Pharm2D.factory
fps = list()
for mol in mols:
try:
sig = Generate.Gen2DFingerprint(mol,factory)
indices = np.array([mmh3.hash(str(i)) for i in sig.GetOnBits()])%fp_size
fp = np.zeros(fp_size, dtype=int)
if len(indices)>0:
fp[indices]=1
fps.append(fp)
except Exception:
print('ERROR')
print(Chem.MolToSmiles(mol))
fps = np.array(fps)
return sparse.csr_matrix(fps).astype('int')
def testDefaultFactory(self):
from rdkit.Chem import Pharm2D
factory = Pharm2D.DefaultSigFactory()
self.assertEqual(factory.GetNumBins(), 7)
# Generate._verbose=True
mol = Chem.MolFromSmiles('OCCC(=O)')
sig = Generate.Gen2DFingerprint(mol, factory)
self.assertEqual(len(sig), 19355)
self.assertEqual(tuple(sig.GetOnBits()), (
2,
16,
21,
84,
1274,
4361,
))
nPts, combo, scaffold, labels, dMat = factory._GetBitSummaryData(21)
self.assertEqual(nPts, 2)
self.assertEqual(labels, ['Acceptor', 'Hydrophobe'])
self.assertEqual(list(dMat[0]), [0, 0])
self.assertEqual(list(dMat[1]), [0, 0])
txt = factory.GetBitDescription(21)
self.assertEqual(txt, 'Acceptor Hydrophobe |0 0|0 0|')
txt = factory.GetBitDescription(21)
self.assertEqual(txt, 'Acceptor Hydrophobe |0 0|0 0|')
nPts, combo, scaffold, labels, dMat = factory._GetBitSummaryData(2)
self.assertEqual(nPts, 2)
self.assertEqual(labels, ['Acceptor', 'Acceptor'])
self.assertEqual(list(dMat[0]), [0, 2])
self.assertEqual(list(dMat[1]), [2, 0])
nPts, combo, scaffold, labels, dMat = factory._GetBitSummaryData(4361)
self.assertEqual(nPts, 3)
self.assertEqual(labels, ['Acceptor', 'Donor', 'Hydrophobe'])
self.assertEqual(list(dMat[0]), [0, 2, 0])
self.assertEqual(list(dMat[1]), [2, 0, 0])
self.assertEqual(list(dMat[2]), [0, 0, 0])
self.assertEqual(factory.GetBitDescription(4361),
'Acceptor Donor Hydrophobe |0 2 0|2 0 0|0 0 0|')
self.assertRaises(NotImplementedError, factory.GetBitDescriptionAsText,
21)
def Fingerprints(mols, fingerprint):
# Indigo fingerprints
if fingerprint in indigofps:
return [mol.fingerprint(fingerprint) for mol in mols]
# RDKit fingerprints
if fingerprint in rdkitfps:
if fingerprint == "atompair":
return [Pairs.GetAtomPairFingerprintAsBitVect(mol) for mol in mols]
elif fingerprint == "avalon":
return [pyAvalonTools.GetAvalonFP(mol) for mol in mols]
elif fingerprint == "daylight":
return [Chem.RDKFingerprint(mol, fpSize=2048) for mol in mols]
elif fingerprint == "maccs":
return [MACCSkeys.GenMACCSKeys(mol) for mol in mols]
elif fingerprint == "morgan":
return [(AllChem.GetMorganFingerprintAsBitVect(mol, 2, nBits=1024))
for mol in mols]
elif fingerprint == "pharm2d":
return [
Generate.Gen2DFingerprint(mol, Gobbi_Pharm2D.factory)
for mol in mols
]
elif fingerprint == "topological":
return [FingerprintMols.FingerprintMol(mol) for mol in mols]
# RDKit non-bit (integer or float) fingerprints
if fingerprint in rdkitnonbitfps:
if fingerprint == "sheridan":
return [Sheridan.GetBPFingerprint(mol) for mol in mols]
elif fingerprint == "topotorsion":
return [
Torsions.GetTopologicalTorsionFingerprint(mol) for mol in mols
]
# E-state fingerprints
if fingerprint in rdkitestatefps:
if fingerprint == "estate1":
return [Fingerprinter.FingerprintMol(mol)[0] for mol in mols]
elif fingerprint == "estate2":
return [Fingerprinter.FingerprintMol(mol)[1] for mol in mols]
# unknown fingerprint
return None
def get_2Dfp(self, rdmols):
#: ファーマコフォアの初期設定
fdefName = r'ensemble/BaseFeatures.fdef'
featFactory = ChemicalFeatures.BuildFeatureFactory(fdefName)
sigFactory = SigFactory(featFactory, minPointCount=2, maxPointCount=3)
#: ファーマコフォア間の距離を離散化する
sigFactory.SetBins([(0, 2), (2, 4)])
sigFactory.Init()
fps1 = [
Generate.Gen2DFingerprint(mol, sigFactory).ToBitString()
for mol in rdmols
]
fps2 = [list(map(int, list(fps))) for fps in fps1]
fps3 = np.array(fps2)
return fps3
def test1_simple(self):
mol = Chem.MolFromSmiles('OCC(=O)CCCN')
self.factory.skipFeats = ['Donor']
self.factory.Init()
self.assertEqual(self.factory.GetSigSize(), 510)
Generate._verbose = False
sig = Generate.Gen2DFingerprint(mol, self.factory)
Generate._verbose = False
tgt = (1, 2, 11, 52, 117)
onBits = sig.GetOnBits()
self.assertEqual(tuple(onBits), tgt)
self.assertEqual(len(onBits), len(tgt))
bitMatches = ([((0, ), (3, ))],
[((0, ), (7, )), ((3, ), (7, ))],
[((0, ), (3, ), (7, ))], )
for i, bit in enumerate(onBits):
matches = Matcher.GetAtomsMatchingBit(self.factory, bit, mol)
def test3Roundtrip(self):
""" longer-running Bug 28 test
"""
from rdkit import RDConfig
import os
nToDo=20
with open(os.path.join(RDConfig.RDDataDir,'NCI','first_5K.smi'),'r') as inF:
inD = inF.readlines()[:nToDo]
factory = Gobbi_Pharm2D.factory
factory.SetBins([(2,3),(3,4),(4,5),(5,8),(8,100)])
for line in inD:
smi = line.split('\t')[0]
mol = Chem.MolFromSmiles(smi)
sig = Generate.Gen2DFingerprint(mol,factory)
onBits = sig.GetOnBits()
for bit in onBits:
atoms = Matcher.GetAtomsMatchingBit(factory,bit,mol,justOne=1)
assert len(atoms),'bit %d failed to match for smi %s'%(bit,smi)
def read_file(fname, fcfp4, fdef_fname):
if not fcfp4:
featFactory = ChemicalFeatures.BuildFeatureFactory(fdef_fname)
sigFactory = SigFactory(featFactory, minPointCount=2, maxPointCount=3, trianglePruneBins=False)
sigFactory.SetBins([(0, 2), (2, 5), (5, 8)])
sigFactory.Init()
d = defaultdict(list)
with open(fname) as f:
for row in f:
smiles, ids, aff = row.strip().split('\t')
if smiles is not None:
mol = Chem.MolFromSmiles(smiles)
d['mol_name'].append(ids)
d['smiles'].append(smiles)
if fcfp4:
d['fingerprint'].append(AllChem.GetMorganFingerprint(mol, 2, useFeatures=True))
else:
d['fingerprint'].append(Generate.Gen2DFingerprint(mol, sigFactory))
return d
def CalculatePharm2D3pointFingerprint(mol, featFactory=featFactory):
"""
Calculate Pharm2D3point Fingerprints
"""
sigFactory_3point = SigFactory(featFactory,
minPointCount=3,
maxPointCount=3)
sigFactory_3point.SetBins([(0, 2), (2, 4), (4, 6), (6, 10)])
sigFactory_3point.Init()
res = Generate.Gen2DFingerprint(mol, sigFactory_3point)
res_keys = tuple(res.GetOnBits())
init_list = [0] * 2135
for res_key in res_keys:
init_list[res_key] = 1
BitVect = tuple(init_list)
return BitVect, res_keys, res
def CalculatePharm2D2pointFingerprint(mol, featFactory=featFactory):
"""
Calculate Pharm2D2point Fingerprints
"""
sigFactory_2point = SigFactory(featFactory,
minPointCount=2,
maxPointCount=2)
sigFactory_2point.SetBins([(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6),
(6, 7), (7, 8), (8, 9)])
sigFactory_2point.Init()
res = Generate.Gen2DFingerprint(mol, sigFactory_2point)
res_keys = tuple(res.GetOnBits())
init_list = [0] * 135
for res_key in res_keys:
init_list[res_key] = 1
BitVect = tuple(init_list)
return BitVect, res_keys, res
def calcfp(self, fptype="rdkit", opt=None):
"""Calculate a molecular fingerprint.
Optional parameters:
fptype -- the fingerprint type (default is "rdkit"). See the
fps variable for a list of of available fingerprint
types.
opt -- a dictionary of options for fingerprints. Currently only used
for radius and bitInfo in Morgan fingerprints.
"""
if opt == None:
opt = {}
fptype = fptype.lower()
if fptype == "rdkit":
fp = Fingerprint(Chem.RDKFingerprint(self.Mol))
elif fptype == "layered":
fp = Fingerprint(Chem.LayeredFingerprint(self.Mol))
elif fptype == "maccs":
fp = Fingerprint(Chem.MACCSkeys.GenMACCSKeys(self.Mol))
elif fptype == "atompairs":
# Going to leave as-is. See Atom Pairs documentation.
fp = Chem.AtomPairs.Pairs.GetAtomPairFingerprintAsIntVect(self.Mol)
elif fptype == "torsions":
# Going to leave as-is.
fp = Chem.AtomPairs.Torsions.GetTopologicalTorsionFingerprintAsIntVect(
self.Mol)
elif fptype == "morgan":
info = opt.get('bitInfo', None)
radius = opt.get('radius', 4)
fp = Fingerprint(
Chem.rdMolDescriptors.GetMorganFingerprintAsBitVect(
self.Mol, radius, bitInfo=info))
elif fptype == "pharm2d":
fp = Fingerprint(
Generate.Gen2DFingerprint(self.Mol, Gobbi_Pharm2D.factory))
else:
raise ValueError("%s is not a recognised RDKit Fingerprint type" %
fptype)
return fp
def _exampleCode():
import os
from rdkit import RDConfig
from rdkit.Chem import ChemicalFeatures
from rdkit.Chem.Pharm2D import SigFactory, Generate
fdefFile = os.path.join(RDConfig.RDCodeDir, 'Chem', 'Pharm2D', 'test_data',
'BaseFeatures.fdef')
featFactory = ChemicalFeatures.BuildFeatureFactory(fdefFile)
factory = SigFactory.SigFactory(featFactory)
factory.SetBins([(1, 2), (2, 5), (5, 8)])
factory.Init()
mol = Chem.MolFromSmiles('OCC(=O)CCCN')
sig = Generate.Gen2DFingerprint(mol, factory)
print('onbits:', list(sig.GetOnBits()))
_verbose = 0
for bit in sig.GetOnBits():
atoms = GetAtomsMatchingBit(factory, bit, mol)
print('\tBit %d: ' % (bit), atoms)
print('finished')
def get_PHCO(self, mol: Mol):
return Generate.Gen2DFingerprint(mol, Gobbi_Pharm2D.factory)
def __init__(self, query_smi: utils.SMILES):
self.sigFactory = factory
query = Chem.MolFromSmiles(query_smi)
self.query_fp = Generate.Gen2DFingerprint(query, self.sigFactory)
lambda s: DataStructs.FingerprintSimilarity(
Chem.RDKFingerprint(Chem.MolFromSmiles(s)), doxrub_fingerprint))
data_block_1["exact_mol_weight"] = data_block_1.SMILES.apply(
lambda s: Descriptors.ExactMolWt(Chem.MolFromSmiles(s)))
data_block_1["ring_count"] = data_block_1.SMILES.apply(
lambda s: Descriptors.RingCount(Chem.MolFromSmiles(s)))
data_block_1["fingerprint"] = data_block_1.SMILES.apply(
lambda s: list(Chem.RDKFingerprint(Chem.MolFromSmiles(s)).GetOnBits()))
fingerprint_block = data_block_1[[
"fingerprint"
]].explode("fingerprint").assign(value=1).pivot(columns="fingerprint")
fingerprint_block.columns = fingerprint_block.columns.get_level_values(1)
fingerprint_block = fingerprint_block.add_prefix("fp.").fillna(value=0)
data_block_1 = pd.concat([data_block_1, fingerprint_block],
axis="columns").drop(labels="fingerprint",
axis="columns")
data_block_1["pharmacophores"] = data_block_1.SMILES.apply(lambda s: list(
Generate.Gen2DFingerprint(Chem.MolFromSmiles(s), Gobbi_Pharm2D.factory).
GetOnBits()))
pharmacophores_block = data_block_1[[
"pharmacophores"
]].explode("pharmacophores").assign(value=1).pivot(columns="pharmacophores")
pharmacophores_block.columns = pharmacophores_block.columns.get_level_values(1)
pharmacophores_block = pharmacophores_block.add_prefix("ph.").fillna(value=0)
data_block_1 = pd.concat([data_block_1, pharmacophores_block],
axis="columns").drop(labels="pharmacophores",
axis="columns")
data_block_1.to_excel("bcl-xl_1_descriptors.xlsx", index=False)
def molfp(smile):
m = Chem.MolFromSmiles(smile)
if m is None: raise ValueError('None mol in function "molfp" (line 58)')
if options.mw is not None and Descriptors.MolWt(m) > options.mw: raise ValueError('Mol too small')
if not options.gobbifp: return AllChem.GetMorganFingerprintAsBitVect(m,options.radius, nBits=options.bits)
else: return Generate.Gen2DFingerprint(m,Gobbi_Pharm2D.factory)
#make new molecules from fragments
import random
random.seed(127)
fragms = [Chem.MolFromSmiles(x) for x in allfrags]
ms = BRICS.BRICSBuild(fragms)
prods = [ms.next() for x in range(10000)]
#clean up generated molecules
for prod in prods:
prod.UpdatePropertyCache(strict=False)
#srpin340 is a low affinity but selective SRPK1 inhibitor
srpin340Mol = Chem.MolFromSmiles(
'C1CCN(CC1)C2=C(C=C(C=C2)C(F)(F)F)NC(=O)C3=CC=NC=C3')
srpin340fps = Generate.Gen2DFingerprint(srpin340Mol, sigFactory)
#sphinx is a higher affinity but selective SRPK1 inhibitor
sphinxMol = Chem.MolFromSmiles(
'C1(=CC=C(C(=C1)N(C(=O)C2=CC=C(O2)C)[H])N3CCOCC3)C(F)(F)F')
sphinxfps = Generate.Gen2DFingerprint(sphinxMol, sigFactory)
#CHEMBL2000345 is a broad spectrum kinase inhibitor but also has highest
#affinity to srpk1 of all ligands
#not selective - try to do negative design
CHEMBL2000345Mol = Chem.MolFromSmiles(
'COc1cc(C=C(C#N)c2nc3cc(C)ccc3[nH]2)c(Br)cc1O')
CHEMBL2000345fps = Generate.Gen2DFingerprint(CHEMBL2000345Mol, sigFactory)
def get_Gobbi_Pharm2D(x, from_smiles):
if from_smiles:
mol = Chem.MolFromSmiles(x)
else:
mol = x
return Generate.Gen2DFingerprint(mol, Gobbi_Pharm2D.factory)
if justOne: break
return res
if __name__ == '__main__':
from rdkit import Chem
from rdkit.Chem.Pharm2D import SigFactory,Generate
factory = SigFactory.SigFactory()
factory.SetBins([(1,2),(2,5),(5,8)])
factory.SetPatternsFromSmarts(['O','N'])
factory.SetMinCount(2)
factory.SetMaxCount(3)
sig = factory.GetSignature()
mol = Chem.MolFromSmiles('OCC(=O)CCCN')
Generate.Gen2DFingerprint(mol,sig)
print 'onbits:',list(sig.GetOnBits())
_verbose=0
for bit in sig.GetOnBits():
atoms = GetAtomsMatchingBit(sig,bit,mol)
print '\tBit %d: '%(bit),atoms
print '--------------------------'
sig = factory.GetSignature()
sig.SetIncludeBondOrder(1)
Generate.Gen2DFingerprint(mol,sig)
print 'onbits:',list(sig.GetOnBits())
for bit in sig.GetOnBits():
def _2DFingerprint(molecule):
desc = Generate.Gen2DFingerprint(Chem.MolFromSmiles(molecule), Gobbi_Pharm2D.factory)
arr = np.array(desc)
return arr
def get_Pharm2D(x):
mol = Chem.MolFromSmiles(x)
if (mol is None) or (len(mol.GetAtoms()) == 0):
return [0] * sigFactory.GetSigSize()
else:
return Generate.Gen2DFingerprint(mol, sigFactory)
smi.strip()
mols[i] = Chem.MolFromSmiles(smi)
sig = factory.GetSignature()
nBits = 300
random.seed(23)
bits = [random.randint(0, sig.GetSize() - 1) for x in range(nBits)]
print('Using the Lazy Generator')
t1 = time.time()
for i in range(len(mols)):
if not i % 10:
print('done mol %d of %d' % (i, len(mols)))
gen = Generator(factory, mols[i])
for bit in bits:
v = gen[bit]
t2 = time.time()
print('\tthat took %4.2f seconds' % (t2 - t1))
print('Generating and checking signatures')
t1 = time.time()
for i in range(len(mols)):
if not i % 10:
print('done mol %d of %d' % (i, len(mols)))
sig = Generate.Gen2DFingerprint(mols[i], factory)
for bit in bits:
v = sig[bit]
t2 = time.time()
print('\tthat took %4.2f seconds' % (t2 - t1))
# 每次修改参数后，都要初始化一下
sigFactory.Init()
# 计算指纹的长度
print('指纹长度=', sigFactory.GetSigSize()) # 指纹长度= 2988
# # 4.2 生成2D药效团指纹
# 指纹工厂中的参数设置完毕，接下来就可以生成2D指纹了。
# 计算2D药效团指纹 ：
Gen2DFingerprint(
mol, # 要计算指纹的mol对象
sigFactory, # 设置了参数的指纹工厂
bitinfo # 获取指纹id及对应的原子
)
mol = Chem.MolFromSmiles('OCC(=O)CCCN')
fp = Generate.Gen2DFingerprint(mol, sigFactory)
print(len(fp)) # 2988
print(fp.GetNumOnBits()) # 23
# 关于指纹每一位所代表特征的信息、特征的距离矩阵等信息，都可以通过signature factory来查看
print(list(fp.GetOnBits())[:5]) #
print(sigFactory.GetBitDescription(1)) # Acceptor Acceptor |0 1|1 0|
# # 4.3 修改FDef设置
# 如果不需要某个特征，可以直接通过signature factory来跳过某个特征，而不用去修改FDef文件。
# 查看现有药效团（列表）列表：GetFeatureFamilies()
featureFamilies = featFactory.GetFeatureFamilies()
print(featureFamilies)
# ('Donor', 'Acceptor', 'NegIonizable', 'PosIonizable',
def calc_p4_fp(mol):
fp = Generate.Gen2DFingerprint(mol, sigFact)
return fp