Ligand-Based Pharmacophore Creation in LigandScout
To understand how the settings influence the pharmacophore creation,
the process is explained shortly. First, conformations of the Training-Set
molecules are generated (if not available). After ranking the
molecules according to their number of conformations (flexibility), pharmacophore features
(lipophilic points, hydrogen bond donors and acceptors, positive and negative ionizable groups)
are projected on these molecules and all their conformations. All conformations of the two top
ranked (i.e. the least flexible) molecules are then aligned using Inte:Ligand’s
molecular alignment algorithm. For a configurable number of best alignment solutions
(“
intermediate alignment solutions
”) common pharmacophoric features are interpolated
and intermediate pharmacophore models are created and stored for further processing
(“
intermediate pharmacophore models
”). These intermediate pharmacophore models are
now ranked using several adjustable scoring functions taking into account chemical feature
overlap, steric overlap, or both. The intermediate pharmacophore models are then
aligned to all conformations of the third molecule, etc., and a new set of intermediate combined feature
pharmacophores is created until all molecules have been processed. If at any stage no conformation can be
found that can be matched on any intermediate solution, the process is stopped. If at least three common
chemical features can be identified throughout the whole alignment and interpolation process, the feature
pharmacophore combination is considered to be successful. All these steps can be logged in a file verbosely.
The output can either be written in LigandScout’s internal file format (PML or PMZ)
or e.g. in Catalyst/Discovery Studio hypoedit format for use in Catalyst.
If the pharmacophore is exported to Catalyst, one must take
into account that Catalyst is unable to map two features on one single
ligand atom - in this case the Catalyst hypothesis can be reduced by deleting features or
create several hypotheses for sequential virtual screening.
For forming intermediate solutions, two modes for sequential pairwise alignment
are provided: shared and merged feature pharmacophore combination mode. While with shared feature combination mode an
intersection between all pharmacophoric features of the underlying molecules is built (logical AND), with
merged feature pharmacophore building mode, the features of the aligned molecules will be merged (logical OR).
To avoid huge pharmacophores from merged feature pharmacophores, a parameter “
number of omitted features
”
must be specified. This parameter defines how many features per molecule may be omitted by the
algorithm with respect to the final pharmacophore solution. Specifying a value of “
1
” for this parameter makes
sure that a maximum of one feature is not matched by each of the Training-Set molecules.
If you want to generate exclusion volumes to the ligand-based pharmacophore model, just toggle on the
Create Excluded Volume
check box in the Ligand-Based Pharmacophore Creation dialog.
To sterical limit the pharmacophore, excluded volumes are placed around the best alignment model.
