For
many
therapeutically established drug targets the 3D structure is not known.
There is however often experimental information on ligands active
against the target or target class. A key goal in generalizing this
information and in generating predictive models is the elucidation of
pharmacophore hypotheses common to subsets of these ligands.
Here we
specifically address the problem of three-dimensional superposition of
small molecules using the technique of Markov Random Fields (MRF). This
as well as many other problems in computational chemistry and biology
can be reduced to finding a match of chemical features and of distances
between them in the form of three dimensional graphs.
We have developed a
general purpose computational engine for such graph-matching problems
that makes use of a Markov Random Fields representation. Convergent
solutions of MRF's is achieved using one of several methods such as
Beliefs Propagation to minimize a free energy function associated with
MRF. Such partial weighted graph matching solutions are ideally suited
to the difficult problem of small-molecule superposition. The
formulation of the MRF problem and of resulting MRF solutions is
probabilistic and multiple solutions can be obtained.
A related
but distinct application of this computational engine to the problem of
docking is described in **
Pharmacophore M****atch
for Docking-Based Virtual**__ Screen__.
At
the start every molecule is broken into constituent parts called
fragments, and every fragment is represented by a reduced set of
chemical vectors and points that represent hydrogen bonds, hydrophobic
centers, formal charges and etc. |