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

Parameters:

  • mol (-) – the molecule

  • confId (-) – (optional) the conformer id to use

  • boxDim (-) – (optional) the dimensions of the box to use

  • spacing (-) – (optional) the spacing to use

  • kwargs (-) – additional arguments to pass to the encoding function

Returns:

a UniformGrid3D object

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.

Parameters:

  • mol (-) – the molecule

  • confId (-) – (optional) the conformer id to use

  • gridSpacing (-) – (optional) the spacing to use

  • boxMargin (-) – (optional) the margin to use around the molecule

  • #1883 (A bit of demo as well as a test of github) –

  • Chem (>>> from rdkit import) –

  • AllChem (>>> from rdkit.Chem import) –

  • Chem.AddHs (>>> mol =) –

  • AllChem.EmbedMolecule (>>>) –

  • 0 –

  • ComputeMolVolume (>>>) –

  • 28... –

  • Chem.AddHs –

  • AllChem.EmbedMolecule –

  • 0 –

  • ComputeMolVolume –

  • 20... –

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

  • getForceField: (optional) a function to use to get a force field

    for the final cleanup

  • kwargs: additional arguments to pass to the embedding function

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

>>> mol = AllChem.ConstrainedEmbed(mol, template)

Demonstrate that the positions are nearly the same with template:

>>> import math
>>> molp = mol.GetConformer().GetAtomPosition(0)
>>> templatep = template.GetConformer().GetAtomPosition(0)
>>> all(math.isclose(v, 0.0, abs_tol=0.01) for v in molp-templatep)
True
>>> molp = mol.GetConformer().GetAtomPosition(1)
>>> templatep = template.GetConformer().GetAtomPosition(1)
>>> all(math.isclose(v, 0.0, abs_tol=0.01) for v in molp-templatep)
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

Parameters:

  • reaction (-) – the reaction

  • sidechainSets (-) – a sequence of sequences of sidechains

  • returnReactants (-) – (optional) if True, the generator will return information about the reactants as well as the products

>>> 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']

Here’s what returnReactants does:

>>> l = list(AllChem.EnumerateLibraryFromReaction(rxn,[s2,s1],returnReactants=True))
>>> type(l[0])
<class 'rdkit.Chem.AllChem.ProductReactants'>
>>> [Chem.MolToSmiles(x) for x in l[0].reactants]
['O=CO', 'CN']
>>> [Chem.MolToSmiles(x) for x in l[0].products]
['CNC=O']
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 before the RMS calculation and, as a side-effect, the second will be left in the aligned state.

Parameters:

  • 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 the second conformer be aligned to the first

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.

Parameters:

  • 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 symmetrical, 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]

where a is the RMS between conformers 0 and 1, b is the RMS between conformers 0 and 2, etc. 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

Parameters:

  • mol (-) – the molecule to be transformed

  • tform (-) – the transformation to apply

  • confId (-) – (optional) the conformer id to transform

  • keepConfs (-) – (optional) if keepConfs is False then all but that conformer are removed