0436402 - ÚMG 2015 RIV GB eng J - Článek v odborném periodiku
Hoksza, D. - Škoda, P. - Voršilák, M. - Svozil, Daniel
Molpher: a software framework for systematic chemical space exploration.
Journal of Cheminformatics. Roč. 6, č. 1 (2014). ISSN 1758-2946. E-ISSN 1758-2946
Grant CEP: GA TA ČR TA02010212; GA ČR(CZ) GAP202/11/0968; GA ČR(CZ) GP14-29032P
Klíčová slova: Chemical space exploration * De-novo design * In silico ligand design * Chemical biology tools
Kód oboru RIV: EB - Genetika a molekulární biologie
Impakt faktor: 4.547, rok: 2014

Background: Chemical space is virtual space occupied by all chemically meaningful organic compounds. It is an important concept in contemporary chemoinformatics research, and its systematic exploration is vital to the discovery of either novel drugs or new tools for chemical biology. Results: In this paper, we describe Molpher, an open-source framework for the systematic exploration of chemical space. Through a process we term ‘molecular morphing’, Molpher produces a path of structurally-related compounds. This path is generated by the iterative application of so-called ‘morphing operators’ that represent simple structural changes, such as the addition or removal of an atom or a bond. Molpher incorporates an optimized parallel exploration algorithm, compound logging and a two-dimensional visualization of the exploration process. Its feature set can be easily extended by implementing additional morphing operators, chemical fingerprints, similarity measures and visualization methods. Molpher not only offers an intuitive graphical user interface, but also can be run in batch mode. This enables users to easily incorporate molecular morphing into their existing drug discovery pipelines. Conclusions: Molpher is an open-source software framework for the design of virtual chemical libraries focused on a particular mechanistic class of compounds. These libraries, represented by a morphing path and its surroundings, provide valuable starting data for future in silico and in vitro experiments. Molpher is highly extensible and can be easily incorporated into any existing computational drug design pipeline.
Trvalý link: http://hdl.handle.net/11104/0241964