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GAUK 174615
Adaptive virtual screening
2015 - 2017

Biological screening is used to detect the ability of small molecules to trigger a response in a macromolecular target by binding to it. The main disadvantage of physical screening is its price and the need to own the tested molecules. An alternative to the physicial screening is its in-silico variant - virtual screening (VS). VS commonly takes place in the early stages of drug discovery as a molecular filter before physical screening. One of the similarity principle-based types of VS is the ligand-based VS (LBVS). The similarity principle correlates function of a molecule with its structural and physico-chemical properties. If there exist known active molecules (ligands) to given target we can utilize molecular similarity to identify novel ligands. LBVS requires a suitable molecular representation and similarity function. Then the candidate molecules can be sorted based on similarity to known ligand(s) and thus, by association, by activity. The parameters of LBVS (similarity, representation and its parametrization) greatly influence its effectivity. The parameters are often static despite the fact that they are target dependent. A wrong parametrizations results in sub-optimal efficiency. Our goal is to develop a modular LBVS framework with generic representation and automated parameterization based on existing information about target.

Principal investigator : Petr Škoda
Co-Investigator : Jan Jelinek, Radoslav Krivak
GAUK 154613
Efficient Molecular Representation
2013 - 2014

Many areas of chemical biology, e.g. drug discovery, largely rely on libraries of molecules for production processes. Since the space of all molecules, called the chemical space, is huge, those libraries contain only small subspace of the whole space. An alternative to statical libraries is dynamic exploration of the space. The exploration generates molecules dynamically based on paths between given molecules in the chemical space. One of a few tools to explore the chemical space is Molpher which has been developed at Charles University in Prague. Molpher’s drawback is the limited possibility of influencing the exploration process. Possible approach to affect the exploration is to minimize the exploration of the subspaces that are not interesting. This calls for effective molecule representation allowing the distinction of interested molecules based on their structure. Here, by effectiveness we understand both accuracy and low computation complexity. Because a lot of molecules are being generated during the exploration processes it is crucial to be able to compare two molecules very fast which helps to restrict the chemical space that needs to be explored. Our goal is to develop a new molecular representation and related algorithms enabling its use in Molpher and similar projects.

Principal investigator : Petr Škoda