Trees | Indices | Help |
|
---|
|
Import all RDKit chemistry modules
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|
|||
logger = logger()
|
Imports: rdBase, RDConfig, DataStructs, rdGeometry, ForceField, numpy, os, warnings
|
Applies the transformation (usually a 4x4 double matrix) to a molecule if keepConfs is False then all but that conformer are removed |
returns a grid representation of the molecule's shape |
Generates a depiction for a molecule where a piece of the molecule is constrained to have the same coordinates as a reference. This is useful for, for example, generating depictions of SAR data sets so that the cores of the molecules are all oriented the same way. Arguments: - mol: the molecule to be aligned, this will come back with a single conformer. - reference: a molecule with the reference atoms to align to; this should have a depiction. - confId: (optional) the id of the reference conformation to use - referencePattern: (optional) an optional molecule to be used to generate the atom mapping between the molecule and the reference. - acceptFailure: (optional) if True, standard depictions will be generated for molecules that don't have a substructure match to the reference; if False, a ValueError will be raised |
Generates a depiction for a molecule where a piece of the molecule is constrained to have coordinates similar to those of a 3D reference structure. Arguments: - mol: the molecule to be aligned, this will come back with a single conformer. - reference: a molecule with the reference atoms to align to; this should have a depiction. - confId: (optional) the id of the reference conformation to use |
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. |
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 |
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. |
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'] |
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 |
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 >>> 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-]' |
Trees | Indices | Help |
|
---|
Generated by Epydoc 3.0.1 on Sat Apr 23 18:49:15 2016 | http://epydoc.sourceforge.net |