Download Dokument 1 - E-Dissertationen der UHH

Abstract
Virtual de novo design is employed in pharmaceutical research to help in the pursuit of
new ligands for a chosen target by creating (partially) novel molecular scaffolds. The
methods which are applied in this field recently shifted from atom-based to fragmentbased approaches, where so-called chemical fragment spaces are used to generate molecular structures. In this work, a new method for de novo design is presented under the name
„Qsearch“. It makes use of stochastic methods to navigate chemical fragment spaces under
the constraints of three-dimensional pharmacophores, and augments the fragment space
approach by modifying the input such that the fragment space is adapted intelligently
for a specific pharmacophore.
Pharmacophores are an established method in medicinal chemistry which allow to characterize key interactions, which are essential for molecular recognition of small molecules
by their biological target in an intuitive way. A particular strength of them is that they
represent chemical information independently from specific molecular motifs. This is especially useful for de novo design methods, since one of their key goals is to incorporate
as much novelty into the generated molecules as possible.
The input for Qsearch consists of a pharmacophore and a fragment space. The fragment space is navigated with a Simulated Annealing procedure, which iteratively creates
molecules by changing their constituting fragments. Every created molecule is subjected
to a three-dimensional alignment to the input pharmacophore. In a following undecoration step, the complexity of the generated molecules is reduced by clipping terminal
functional groups and removing hetero atoms in aromatic heterocycles—provided that
the pharmacophore allows for these changes. This step allows the algorithm to access
molecules which do not result directly from connecting the input fragments, and to extend the number of accessible molecules in a meaningful way. Furthermore, slight modifications to the workflow of Qsearch enable it to grow complete molecules from anchor
fragments, which is a common task in medicinal chemistry.
Qsearch has been tested in several scenarios and has been shown to be able to generate
relevant ideas for novel molecular scaffolds.