rdkit.Chem.AllChem module — The RDKit 2016.09.1 documentation

# rdkit.Chem.AllChem module

Import all RDKit chemistry modules

`rdkit.Chem.AllChem.` `AssignBondOrdersFromTemplate`(refmol, mol)
  assigns bond orders to a molecule based on the
    bond orders in a template molecule
  Arguments
    - refmol: the template molecule
    - mol: the molecule to assign bond orders to

    An example, start by generating a template from a SMILES and read in the PDB structure of the molecule >>> import os >>> from rdkit.Chem import AllChem >>> template = AllChem.MolFromSmiles(“CN1C(=NC(C1=O)(c2ccccc2)c3ccccc3)N”) >>> mol = AllChem.MolFromPDBFile(os.path.join(RDConfig.RDCodeDir, ‘Chem’, ‘test_data’, ‘4DJU_lig.pdb’)) >>> len([1 for b in template.GetBonds() if b.GetBondTypeAsDouble() == 1.0]) 8 >>> len([1 for b in mol.GetBonds() if b.GetBondTypeAsDouble() == 1.0]) 22

    Now assign the bond orders based on the template molecule >>> newMol = AllChem.AssignBondOrdersFromTemplate(template, mol) >>> len([1 for b in newMol.GetBonds() if b.GetBondTypeAsDouble() == 1.0]) 8

    Note that the template molecule should have no explicit hydrogens else the algorithm will fail.

    It also works if there are different formal charges (this was github issue 235): >>> template=AllChem.MolFromSmiles(‘CN(C)C(=O)Cc1ccc2c(c1)NC(=O)c3ccc(cc3N2)c4ccc(c(c4)OC)[N+](=O)[O-]’) >>> mol = AllChem.MolFromMolFile(os.path.join(RDConfig.RDCodeDir, ‘Chem’, ‘test_data’, ‘4FTR_lig.mol’)) >>> AllChem.MolToSmiles(mol) ‘COC1CC(C2CCC3C(O)NC4CC(CC(O)N(C)C)CCC4NC3C2)CCC1N(O)O’ >>> newMol = AllChem.AssignBondOrdersFromTemplate(template, mol) >>> AllChem.MolToSmiles(newMol) ‘COc1cc(-c2ccc3c(c2)Nc2ccc(CC(=O)N(C)C)cc2NC3=O)ccc1[N+](=O)[O-]’

`rdkit.Chem.AllChem.` `ComputeMolShape`(mol, confId=-1, boxDim=(20, 20, 20), spacing=0.5, **kwargs)

  returns a grid representation of the molecule’s shape

`rdkit.Chem.AllChem.` `ComputeMolVolume`(mol, confId=-1, gridSpacing=0.2, boxMargin=2.0)

  Calculates the volume of a particular conformer of a molecule based on a grid-encoding of the molecular shape.

`rdkit.Chem.AllChem.` `ConstrainedEmbed`(mol, core, useTethers=True, coreConfId=-1, randomseed=2342, getForceField=<Boost.Python.function object at 0x2f67e30>, **kwargs)

  generates an embedding of a molecule where part of the molecule is constrained to have particular coordinates

  Arguments

    - mol: the molecule to embed

    - core: the molecule to use as a source of constraints

    - useTethers: (optional) if True, the final conformation will be

        optimized subject to a series of extra forces that pull the matching atoms to the positions of the core atoms. Otherwise simple distance constraints based on the core atoms will be used in the optimization.

    - coreConfId: (optional) id of the core conformation to use

    - randomSeed: (optional) seed for the random number generator

    An example, start by generating a template with a 3D structure: >>> from rdkit.Chem import AllChem >>> template = AllChem.MolFromSmiles(“c1nn(Cc2ccccc2)cc1”) >>> AllChem.EmbedMolecule(template) 0 >>> AllChem.UFFOptimizeMolecule(template) 0

    Here’s a molecule: >>> mol = AllChem.MolFromSmiles(“c1nn(Cc2ccccc2)cc1-c3ccccc3”)

    Now do the constrained embedding >>> newmol=AllChem.ConstrainedEmbed(mol, template)

    Demonstrate that the positions are the same: >>> newp=newmol.GetConformer().GetAtomPosition(0) >>> molp=mol.GetConformer().GetAtomPosition(0) >>> list(newp-molp)==[0.0,0.0,0.0] True >>> newp=newmol.GetConformer().GetAtomPosition(1) >>> molp=mol.GetConformer().GetAtomPosition(1) >>> list(newp-molp)==[0.0,0.0,0.0] True

`rdkit.Chem.AllChem.` `EnumerateLibraryFromReaction`(reaction, sidechainSets, returnReactants=False)
  Returns a generator for the virtual library defined by
    a reaction and a sequence of sidechain sets

      >>> from rdkit import Chem
      >>> from rdkit.Chem import AllChem
      >>> s1=[Chem.MolFromSmiles(x) for x in ('NC','NCC')]
      >>> s2=[Chem.MolFromSmiles(x) for x in ('OC=O','OC(=O)C')]
      >>> rxn = AllChem.ReactionFromSmarts('[O:2]=[C:1][OH].[N:3]>>[O:2]=[C:1][N:3]')
      >>> r = AllChem.EnumerateLibraryFromReaction(rxn,[s2,s1])
      >>> [Chem.MolToSmiles(x[0]) for x in list(r)]
      ['CNC=O', 'CCNC=O', 'CNC(C)=O', 'CCNC(C)=O']

  Note that this is all done in a lazy manner, so “infinitely” large libraries can be done without worrying about running out of memory. Your patience will run out first:

  Define a set of 10000 amines: >>> amines = (Chem.MolFromSmiles(‘N’+’C’*x) for x in range(10000))

  ... a set of 10000 acids >>> acids = (Chem.MolFromSmiles(‘OC(=O)’+’C’*x) for x in range(10000))

  ... now the virtual library (1e8 compounds in principle): >>> r = AllChem.EnumerateLibraryFromReaction(rxn,[acids,amines])

  ... look at the first 4 compounds: >>> [Chem.MolToSmiles(next(r)[0]) for x in range(4)] [‘NC=O’, ‘CNC=O’, ‘CCNC=O’, ‘CCCNC=O’]

`rdkit.Chem.AllChem.` `GetBestRMS`(ref, probe, refConfId=-1, probeConfId=-1, maps=None)

  Returns the optimal RMS for aligning two molecules, taking symmetry into account. As a side-effect, the probe molecule is left in the aligned state.

  Arguments:
    - ref: the reference molecule
    - probe: the molecule to be aligned to the reference
    - refConfId: (optional) reference conformation to use
    - probeConfId: (optional) probe conformation to use
    - maps: (optional) a list of lists of (probeAtomId,refAtomId) tuples with the atom-atom mappings of the two molecules. If not provided, these will be generated using a substructure search.

  Note: This function will attempt to align all permutations of matching atom orders in both molecules, for some molecules it will lead to ‘combinatorial explosion’ especially if hydrogens are present. Use ‘rdkit.Chem.AllChem.AlignMol’ to align molecules without changing the atom order.

`rdkit.Chem.AllChem.` `GetConformerRMS`(mol, confId1, confId2, atomIds=None, prealigned=False)

  Returns the RMS between two conformations. By default, the conformers will be aligned to the first conformer of the molecule (i.e. the reference) before RMS calculation and, as a side-effect, will be left in the aligned state.

  Arguments:

    - mol: the molecule

    - confId1: the id of the first conformer

    - confId2: the id of the second conformer

    - atomIds: (optional) list of atom ids to use a points for

        alingment - defaults to all atoms

    - prealigned: (optional) by default the conformers are assumed

        be unaligned and will therefore be aligned to the first conformer

`rdkit.Chem.AllChem.` `GetConformerRMSMatrix`(mol, atomIds=None, prealigned=False)

  Returns the RMS matrix of the conformers of a molecule. As a side-effect, the conformers will be aligned to the first conformer (i.e. the reference) and will left in the aligned state.

  Arguments:

    - mol: the molecule

    - atomIds: (optional) list of atom ids to use a points for

        alingment - defaults to all atoms

    - prealigned: (optional) by default the conformers are assumed

        be unaligned and will therefore be aligned to the first conformer

  Note that the returned RMS matrix is symmetrically, i.e. it is the lower half of the matrix, e.g. for 5 conformers: rmsmatrix = [ a,

  > b, c, d, e, f, g, h, i, j]

  This way it can be directly used as distance matrix in e.g. Butina clustering.

`rdkit.Chem.AllChem.` `TransformMol`(mol, tform, confId=-1, keepConfs=False)

  Applies the transformation (usually a 4x4 double matrix) to a molecule if keepConfs is False then all but that conformer are removed