Cancer Research - Europa

descriptors of the molecules to be tested, but pharmacophore
searches (ligand-based design) and virtual
docking and scoring (structure based design) serve as
subsequent fi ltering processes in 3D that cover the ‘affi nity’
part of drug action, while the other fi lters mostly deal with
‘drug transport’ issues.
These two properties are, to a large extend, orthogonal.
Thus, one may regard the fi ltering process as beginning with
huge numbers of molecules, which are reduced to a smaller
set by chemical descriptors. This smaller set may then be
studied with more detailed conformations at the pharmacophore
level, reducing it further to a group of molecules
which may be docked virtually to the assumed target, fi nally
leaving a small set of substantially ‘focused’ or ‘targeted’
lead candidates.
But, even that approach suff ers from many drawbacks.
Lipinski and Veber rules can not distinguish well between
drugs and non-drugs, and are clearly not appropriate indicators
of ‘drug-likeness’. Neural networks have been applied
specifi cally to this problem and managed to distinguish
properly between drugs and non-drugs, but have the disadvantage
of ‘hidden layers’ which do not enable to plan and
design novel molecules.
A drug-like index has been suggested but is based on fragment
identifi cation and therefore limited in its ability to
discover novel structures. Structure-based approaches can
consider small molecule fl exibility, but are still inappropriate
for dealing with the fl exibility of the protein targets, especially
with the fl exibility of backbone and of larger loops.
The scorings in docking methods have recently been
exposed to much criticism. Using single conformations in
pharmacophore searches is clearly inappropriate, because it
has been shown that small molecules bind to proteins in
conformations that are higher in energy than their global
minima. Toxicity predictions have not yet reached enough
reliability to prevent major toxicity threats by drugs. The
need for selectivity has not yet been properly addressed in
the preparation of focused libraries.
Therefore, although many companies nowadays are off ering
focused libraries for kinases, GPCRs and other families
of molecules, there is a great need to improve the production
of such libraries in order to shorten the time for
discovery and to save enormous expense. A main stumbling
block on the way to solving such issues is the complex
combinatorial nature of the problem of library construction
and drug design.
In this proposal, we include methods that deal directly with
the combinatorial nature of the problems, that have been
shown to solve combinatorial problems in a highly satisfactory
manner, that discover the global minimum in most
cases and retain a large set of best results, many of them
excellent alternatives to the global minimum.
TREATMENT
Typical fold of matrix metalloproteinases structured in 3 α-helices (red) and 4 parallel
and 1 antiparallel β-sheets (yellow). The binding site is represented by a white surface while
the zinc ion is shown as a light-gray sphere and the three catalytic histidines are rendered
as ball-and-stick.
Expected results
Novelties and added values of the project:
• virtual focused libraries of anti-cancer agents;
• potential anti-cancer agents;
• HTS technology;
• data for model building purposes;
• models able to predict anti-cancer properties;
• CancerGrid System: a grid-based computer aided tool
that able to provide anti-cancer candidates faster and in
a more effi cient way, also suitable to develop candidates
for other targets.
Potential applications
The models developed within the framework of this project
can be used for fi ltering large discovery libraries to fi nd
anti-cancer drug candidates, and to design anti-cancer
focused libraries. The CancerGrid computer system will be
able to support the design of lead compounds in general,
not only in the anti-cancer fi eld, but in any other activity
area. Thanks to its grid-based architecture, the system will
be able to predict molecular descriptors for compound
libraries, containing a large number of molecular structures,
in a short time. When calculating 3D molecular descriptors,
the system will take all major conformers into account.
This enables the calculation of information-rich molecular
descriptors, and the development of reliable linear and nonlinear
models. The system will also be able to apply these
models to predict the biological activity or chemical/physical
property of the compounds.
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