Python rdkit.Chem.rdMolDescriptors.GetMorganFingerprint() Examples

def main() :
    model = models.KeyedVectors.load_word2vec_format("vec.txt")
    embeddings = list()

    # Using canonical smiles for glycine, as in original research paper
    mol = Chem.MolFromSmiles("C(C(=O)O)N")
    try:
        info = {}
        rdMolDescriptors.GetMorganFingerprint(mol, 0, bitInfo=info)
        keys = info.keys()
        keys_list = list(keys)
        totalvec = np.zeros(200)
        for k in keys_list:
            wordvec = model.wv[str(k)]
            totalvec = np.add(totalvec, wordvec)
        embeddings.append(totalvec)
    except Exception as e:
        print(e)
        pass

    print(embeddings[0]) 

def NP_score(mol, fscore=None):
    if fscore is None:
        fscore =readNPModel()
    if mol is None:
            raise ValueError('invalid molecule')
    fp = rdMolDescriptors.GetMorganFingerprint(mol, 2)
    bits = fp.GetNonzeroElements()

    # calculating the score
    score = 0.
    for bit in bits:
        score += fscore.get(bit, 0)
    score /= float(mol.GetNumAtoms())

    # preventing score explosion for exotic molecules
    if score > 4:
        score = 4. + math.log10(score - 4. + 1.)
    if score < -4:
        score = -4. - math.log10(-4. - score + 1.)
    return score 

def _featurize(self, mol):
    """
    Calculate circular fingerprint.

    Parameters
    ----------
    mol : RDKit Mol
        Molecule.
    """
    from rdkit import Chem
    from rdkit.Chem import rdMolDescriptors
    if self.sparse:
      info = {}
      fp = rdMolDescriptors.GetMorganFingerprint(
          mol,
          self.radius,
          useChirality=self.chiral,
          useBondTypes=self.bonds,
          useFeatures=self.features,
          bitInfo=info)
      fp = fp.GetNonzeroElements()  # convert to a dict

      # generate SMILES for fragments
      if self.smiles:
        fp_smiles = {}
        for fragment_id, count in fp.items():
          root, radius = info[fragment_id][0]
          env = Chem.FindAtomEnvironmentOfRadiusN(mol, radius, root)
          frag = Chem.PathToSubmol(mol, env)
          smiles = Chem.MolToSmiles(frag)
          fp_smiles[fragment_id] = {'smiles': smiles, 'count': count}
        fp = fp_smiles
    else:
      fp = rdMolDescriptors.GetMorganFingerprintAsBitVect(
          mol,
          self.radius,
          nBits=self.size,
          useChirality=self.chiral,
          useBondTypes=self.bonds,
          useFeatures=self.features)
    return fp 

def main() :

    sdf_root_path = "/media/data/pubchem/SDF"

    for path, dirs, filenames in os.walk(sdf_root_path) :
        for filename in filenames:
            filepath = os.path.join(sdf_root_path, filename)

            # This SDF file fails to parse with RDKit on Ubuntu 16.04
            if "Compound_102125001_102150000" in filename:
                continue

            with gzip.open(filepath, 'rb') as myfile:
                suppl = Chem.ForwardSDMolSupplier(myfile)

                for mol in suppl:

                    if not mol:
                        continue

                    try :
                        info = {}
                        rdMolDescriptors.GetMorganFingerprint(mol,1,bitInfo=info)
                        keys = info.keys()
                        keys_list = list(keys)
                        for k in keys_list:
                            print(k,end=' ')
                        print()
                    except Exception:
                        pass 

def _featurize(self, mol, smiles=None):
    """
    Calculate circular fingerprint.

    Parameters
    ----------
    mol : RDKit Mol
        Molecule.
    """
    if self.sparse:
      info = {}
      fp = rdMolDescriptors.GetMorganFingerprint(
          mol, self.radius, useChirality=self.chiral,
          useBondTypes=self.bonds, useFeatures=self.features,
          bitInfo=info)
      fp = fp.GetNonzeroElements()  # convert to a dict

      # generate SMILES for fragments
      if self.calc_smiles:
        fp_smiles = {}
        for fragment_id, count in fp.items():
          root, radius = info[fragment_id][0]
          env = Chem.FindAtomEnvironmentOfRadiusN(mol, radius, root)
          frag = Chem.PathToSubmol(mol, env)
          smiles = Chem.MolToSmiles(frag)
          fp_smiles[fragment_id] = {'smiles': smiles, 'count': count}
        fp = fp_smiles
    else:
      fp = ComparableFingerprint(mol, self.radius, nBits=self.size, useChirality=self.chiral,
          useBondTypes=self.bonds, useFeatures=self.features, smiles=smiles)
    return fp 

def scoreMolWConfidence(mol, fscore):
    """Next to the NP Likeness Score, this function outputs a confidence value
    between 0..1 that descibes how many fragments of the tested molecule
    were found in the model data set (1: all fragments were found).

    Returns namedtuple NPLikeness(nplikeness, confidence)"""

    if mol is None:
        raise ValueError('invalid molecule')
    fp = rdMolDescriptors.GetMorganFingerprint(mol, 2)
    bits = fp.GetNonzeroElements()

    # calculating the score
    score = 0.0
    bits_found = 0
    for bit in bits:
        if bit in fscore:
            bits_found += 1
            score += fscore[bit]

    score /= float(mol.GetNumAtoms())
    confidence = float(bits_found / len(bits))

    # preventing score explosion for exotic molecules
    if score > 4:
        score = 4. + math.log10(score - 4. + 1.)
    elif score < -4:
        score = -4. - math.log10(-4. - score + 1.)
    NPLikeness = namedtuple("NPLikeness", "nplikeness,confidence")
    return NPLikeness(score, confidence) 

def calculateScore(m):
  if _fscores is None: readFragmentScores()

  # fragment score
  fp = rdMolDescriptors.GetMorganFingerprint(m,2)  #<- 2 is the *radius* of the circular fingerprint
  fps = fp.GetNonzeroElements()
  score1 = 0.
  nf = 0
  for bitId,v in iteritems(fps):
    nf += v
    sfp = bitId
    score1 += _fscores.get(sfp,-4)*v
  score1 /= nf

  # features score
  nAtoms = m.GetNumAtoms()
  nChiralCenters = len(Chem.FindMolChiralCenters(m,includeUnassigned=True))
  ri = m.GetRingInfo()
  nBridgeheads,nSpiro=numBridgeheadsAndSpiro(m,ri)
  nMacrocycles=0
  for x in ri.AtomRings():
    if len(x)>8: nMacrocycles+=1

  sizePenalty = nAtoms**1.005 - nAtoms
  stereoPenalty = math.log10(nChiralCenters+1)
  spiroPenalty = math.log10(nSpiro+1)
  bridgePenalty = math.log10(nBridgeheads+1)
  macrocyclePenalty = 0.
  # ---------------------------------------
  # This differs from the paper, which defines:
  #  macrocyclePenalty = math.log10(nMacrocycles+1)
  # This form generates better results when 2 or more macrocycles are present
  if nMacrocycles > 0: macrocyclePenalty = math.log10(2)

  score2 = 0. -sizePenalty -stereoPenalty -spiroPenalty -bridgePenalty -macrocyclePenalty

  # correction for the fingerprint density
  # not in the original publication, added in version 1.1
  # to make highly symmetrical molecules easier to synthetise
  score3 = 0.
  if nAtoms > len(fps):
    score3 = math.log(float(nAtoms) / len(fps)) * .5

  sascore = score1 + score2 + score3

  # need to transform "raw" value into scale between 1 and 10
  min = -4.0
  max = 2.5
  sascore = 11. - (sascore - min + 1) / (max - min) * 9.
  # smooth the 10-end
  if sascore > 8.: sascore = 8. + math.log(sascore+1.-9.)
  if sascore > 10.: sascore = 10.0
  elif sascore < 1.: sascore = 1.0 

  return sascore 

def calculateScore(m):
  if _fscores is None: readFragmentScores()

  # fragment score
  fp = rdMolDescriptors.GetMorganFingerprint(m,2)  #<- 2 is the *radius* of the circular fingerprint
  fps = fp.GetNonzeroElements()
  score1 = 0.
  nf = 0
  for bitId,v in iteritems(fps):
    nf += v
    sfp = bitId
    score1 += _fscores.get(sfp,-4)*v
  score1 /= nf

  # features score
  nAtoms = m.GetNumAtoms()
  nChiralCenters = len(Chem.FindMolChiralCenters(m,includeUnassigned=True))
  ri = m.GetRingInfo()
  nBridgeheads,nSpiro=numBridgeheadsAndSpiro(m,ri)
  nMacrocycles=0
  for x in ri.AtomRings():
    if len(x)>8: nMacrocycles+=1

  sizePenalty = nAtoms**1.005 - nAtoms
  stereoPenalty = math.log10(nChiralCenters+1)
  spiroPenalty = math.log10(nSpiro+1)
  bridgePenalty = math.log10(nBridgeheads+1)
  macrocyclePenalty = 0.
  # ---------------------------------------
  # This differs from the paper, which defines:
  #  macrocyclePenalty = math.log10(nMacrocycles+1)
  # This form generates better results when 2 or more macrocycles are present
  if nMacrocycles > 0: macrocyclePenalty = math.log10(2)

  score2 = 0. -sizePenalty -stereoPenalty -spiroPenalty -bridgePenalty -macrocyclePenalty

  # correction for the fingerprint density
  # not in the original publication, added in version 1.1
  # to make highly symmetrical molecules easier to synthetise
  score3 = 0.
  if nAtoms > len(fps):
    score3 = math.log(float(nAtoms) / len(fps)) * .5

  sascore = score1 + score2 + score3

  # need to transform "raw" value into scale between 1 and 10
  min = -4.0
  max = 2.5
  sascore = 11. - (sascore - min + 1) / (max - min) * 9.
  # smooth the 10-end
  if sascore > 8.: sascore = 8. + math.log(sascore+1.-9.)
  if sascore > 10.: sascore = 10.0
  elif sascore < 1.: sascore = 1.0 

  return sascore 

def calculateScore(m):
  if _fscores is None: readFragmentScores()

  # fragment score
  fp = rdMolDescriptors.GetMorganFingerprint(m,2)  #<- 2 is the *radius* of the circular fingerprint
  fps = fp.GetNonzeroElements()
  score1 = 0.
  nf = 0
  for bitId,v in iteritems(fps):
    nf += v
    sfp = bitId
    score1 += _fscores.get(sfp,-4)*v
  score1 /= nf

  # features score
  nAtoms = m.GetNumAtoms()
  nChiralCenters = len(Chem.FindMolChiralCenters(m,includeUnassigned=True))
  ri = m.GetRingInfo()
  nBridgeheads,nSpiro=numBridgeheadsAndSpiro(m,ri)
  nMacrocycles=0
  for x in ri.AtomRings():
    if len(x)>8: nMacrocycles+=1

  sizePenalty = nAtoms**1.005 - nAtoms
  stereoPenalty = math.log10(nChiralCenters+1)
  spiroPenalty = math.log10(nSpiro+1)
  bridgePenalty = math.log10(nBridgeheads+1)
  macrocyclePenalty = 0.
  # ---------------------------------------
  # This differs from the paper, which defines:
  #  macrocyclePenalty = math.log10(nMacrocycles+1)
  # This form generates better results when 2 or more macrocycles are present
  if nMacrocycles > 0: macrocyclePenalty = math.log10(2)

  score2 = 0. -sizePenalty -stereoPenalty -spiroPenalty -bridgePenalty -macrocyclePenalty

  # correction for the fingerprint density
  # not in the original publication, added in version 1.1
  # to make highly symmetrical molecules easier to synthetise
  score3 = 0.
  if nAtoms > len(fps):
    score3 = math.log(float(nAtoms) / len(fps)) * .5

  sascore = score1 + score2 + score3

  # need to transform "raw" value into scale between 1 and 10
  min = -4.0
  max = 2.5
  sascore = 11. - (sascore - min + 1) / (max - min) * 9.
  # smooth the 10-end
  if sascore > 8.: sascore = 8. + math.log(sascore+1.-9.)
  if sascore > 10.: sascore = 10.0
  elif sascore < 1.: sascore = 1.0 

  return sascore 

def calculateScore(m):
  if _fscores is None: readFragmentScores()

  # fragment score
  fp = rdMolDescriptors.GetMorganFingerprint(m,2)  #<- 2 is the *radius* of the circular fingerprint
  fps = fp.GetNonzeroElements()
  score1 = 0.
  nf = 0
  for bitId,v in iteritems(fps):
    nf += v
    sfp = bitId
    score1 += _fscores.get(sfp,-4)*v
  score1 /= nf

  # features score
  nAtoms = m.GetNumAtoms()
  nChiralCenters = len(Chem.FindMolChiralCenters(m,includeUnassigned=True))
  ri = m.GetRingInfo()
  nBridgeheads,nSpiro=numBridgeheadsAndSpiro(m,ri)
  nMacrocycles=0
  for x in ri.AtomRings():
    if len(x)>8: nMacrocycles+=1

  sizePenalty = nAtoms**1.005 - nAtoms
  stereoPenalty = math.log10(nChiralCenters+1)
  spiroPenalty = math.log10(nSpiro+1)
  bridgePenalty = math.log10(nBridgeheads+1)
  macrocyclePenalty = 0.
  # ---------------------------------------
  # This differs from the paper, which defines:
  #  macrocyclePenalty = math.log10(nMacrocycles+1)
  # This form generates better results when 2 or more macrocycles are present
  if nMacrocycles > 0: macrocyclePenalty = math.log10(2)

  score2 = 0. -sizePenalty -stereoPenalty -spiroPenalty -bridgePenalty -macrocyclePenalty

  # correction for the fingerprint density
  # not in the original publication, added in version 1.1
  # to make highly symmetrical molecules easier to synthetise
  score3 = 0.
  if nAtoms > len(fps):
    score3 = math.log(float(nAtoms) / len(fps)) * .5

  sascore = score1 + score2 + score3

  # need to transform "raw" value into scale between 1 and 10
  min = -4.0
  max = 2.5
  sascore = 11. - (sascore - min + 1) / (max - min) * 9.
  # smooth the 10-end
  if sascore > 8.: sascore = 8. + math.log(sascore+1.-9.)
  if sascore > 10.: sascore = 10.0
  elif sascore < 1.: sascore = 1.0 

  return sascore 

def calculateScore(m):
  if _fscores is None: readFragmentScores()

  # fragment score
  fp = rdMolDescriptors.GetMorganFingerprint(m,2)  #<- 2 is the *radius* of the circular fingerprint
  fps = fp.GetNonzeroElements()
  score1 = 0.
  nf = 0
  for bitId,v in iteritems(fps):
    nf += v
    sfp = bitId
    score1 += _fscores.get(sfp,-4)*v
  score1 /= nf

  # features score
  nAtoms = m.GetNumAtoms()
  nChiralCenters = len(Chem.FindMolChiralCenters(m,includeUnassigned=True))
  ri = m.GetRingInfo()
  nBridgeheads,nSpiro=numBridgeheadsAndSpiro(m,ri)
  nMacrocycles=0
  for x in ri.AtomRings():
    if len(x)>8: nMacrocycles+=1

  sizePenalty = nAtoms**1.005 - nAtoms
  stereoPenalty = math.log10(nChiralCenters+1)
  spiroPenalty = math.log10(nSpiro+1)
  bridgePenalty = math.log10(nBridgeheads+1)
  macrocyclePenalty = 0.
  # ---------------------------------------
  # This differs from the paper, which defines:
  #  macrocyclePenalty = math.log10(nMacrocycles+1)
  # This form generates better results when 2 or more macrocycles are present
  if nMacrocycles > 0: macrocyclePenalty = math.log10(2)

  score2 = 0. -sizePenalty -stereoPenalty -spiroPenalty -bridgePenalty -macrocyclePenalty

  # correction for the fingerprint density
  # not in the original publication, added in version 1.1
  # to make highly symmetrical molecules easier to synthetise
  score3 = 0.
  if nAtoms > len(fps):
    score3 = math.log(float(nAtoms) / len(fps)) * .5

  sascore = score1 + score2 + score3

  # need to transform "raw" value into scale between 1 and 10
  min = -4.0
  max = 2.5
  sascore = 11. - (sascore - min + 1) / (max - min) * 9.
  # smooth the 10-end
  if sascore > 8.: sascore = 8. + math.log(sascore+1.-9.)
  if sascore > 10.: sascore = 10.0
  elif sascore < 1.: sascore = 1.0 

  return sascore