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Pharmacophoric features include all those binding related structural or
chemical properties of chemical compounds that are thought to be responsible
for a specific pharmacological action. Chemical features taken into
account usually include hydrogen bond donor/acceptor, charge, hydrophobicity and
aromacity.
These features (or pharmacophoric properties) are arbitrary in that sense
that users of the software
can select the pharmacophoric features to be considered and can even provide
the custom definition of the particular feature (for example specify under which
circumstances is an atom aromatic).
In the present approach individual atoms1
are labeled with pharmacophore properties. These labeled atoms are often
referred to as pharmacophore points.
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There are numerous methods available to perceive pharmacophore points in chemical compounds. One family consists of methods that are based on calculation (quantitative approximation) of chemical properties (for example pKa, electronic potential). Another big family is the knowledge- or rule-based approaches. ChemAxon's pharmacophore point identification tool uses a custom expression evaluator module enabling any of these methods and even their combination. The pharmacophore definitions are provided in an XML configuration file.
The basic observation that makes rule based methods feasible is that the pharmacophore character of an atom is highly determined by the atom itself and its local neighborhood3. The mainly local nature of pharmacophore characteristic of atoms enables the use of structural patterns to capture a certain local arrangement of atoms (in an arbitrary larger molecular environment). For example consider primary amides:
The oxygen atom shows hydrogen bond acceptor characteristic, while the nitrogen atom exhibits hydrogen bond donor characteristic independent from the rest of the molecule.
However, the situation is not always that simple. Chemistry is an infinite source of exceptions to such simple rules, not surprisingly there are always exceptions to generic rules in the case of pharmacophore characterization too. Proper handling of such exceptions needs the specialization of rules, which at the end necessitates the use of logical (or boolean) expressions in constructing rules. For example a viable rule that holds in most cases is that nitrogen in tertiary amines is hydrogen bond donor. However, there are exceptions to this rule, thus it has to be extended: nitrogen in tertiary amines is hydrogen bond donor, except when attached to an sp2 atom or, for instance, when part of N-cyano-methyl piperidine.
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At the end of the pharmacophore point type perception process the chemical graph of the molecule to be perceived is labeled with pharmacophoric features. This information can be visualized in MarvinView using pharmacophore type dependent coloring or simply be printed as a pharmacophore point list, which can also be stored in MDL SDfiles.
| SMILES | name | pharmacophore map |
|---|---|---|
| CCC=O | 1-propanal | h;h;h;a |
| CCCO | 1-propanol | h;h;h;a/d |
| C1=CC=CC=C1C(C)Cl | 1-chloro-ethyl-benzene | r;r;r;r;r;r;h;h;h |
| SC(CN)C(O)=O | cystein | d;h;h;+/d;-;-/a;-/a |
Pharmacophore fingerprints attempt to model binding related structural or chemical properties of chemical compounds with the use of simple statistics of chemical features. In the case of pharmacophore fingerprints generated these features are always assigned to individual atoms of the molecule thus these fingerprints are atom based pharmacophore fingerprints.
Another characteristic of the particular pharmacophore fingerprints used in our software is their two-dimensional (2D) nature. Although, the widely used term, 2D pharmacophore fingerprint is misleading as fingerprints themselves are inherently one-dimensional constructs. Not fingerprints, but molecular structures are, within this context, considered as being two-dimensional: spatial position of atoms is either not known or is neglected in fingerprint generation.
Pharmacophore models assemble the set of pharmacophore points along with their relative arrangement, which, in the simplest case, is the three-dimensional Euclidean distance between each point pair. However, in the two-dimensional case no spatial information is available, thus topological relations are used to represent the relative position of pharmacophore points. The most apparent counterpart of Euclidean distance in the three dimensional space is the topological distance in the topological space of the chemical graphs. This distance is equal to the length of the shortest path between two nodes (atoms) of the chemical graph, that is the smallest number of graph edges (bonds) connecting the two atoms.
A further choice in constructing pharmacophore models is how these relative positions are built into the model. Common approaches consider either all pharmacophore point pairs or all pharmacophore point triplets (triangles). In the approach taken by GenerateMD only pharmacophore pairs are used.
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Atom-pair based 2D pharmacophore fingerprints are
defined as the collection of all atom-atom pharmacophore feature pairs along
with their topological distances. In order to make the handling of this often
large amount of data easier, the data set is represented in histograms. One
histogram is associated with each pharmacophore feature pair (for example
acceptor-acceptor, donor-positive, negative-hydrophobic etc.), thus the total
number of histograms is f(f+1)/2, where f denotes the
number of pharmacophore features used.
These histograms have the same number of bins. Bins are associated with
the distances between pharmacophore points.
Since topological distances are discrete values one bin belongs to one certain
distance value.
The bin labeled with d stores the number of pharmacophore point pairs
(of the corresponding
type) that are d bonds apart from each other. The distance range of the
histogram can be set by specifying a minimal (m) and maximal (M)
distance. Values below minimum are put in the first bin (i.e. the one labeled
with m), while those
greater than the maximum are put in the last one (labeled with M).
The pharmacophore fingerprint is the ordered sequence of the feature pair
distance histograms.
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