4.6 Article

Molpher: a software framework for systematic chemical space exploration

Journal

JOURNAL OF CHEMINFORMATICS
Volume 6, Issue -, Pages -

Publisher

BIOMED CENTRAL LTD
DOI: 10.1186/1758-2946-6-7

Keywords

Chemical space exploration; De-novo design; Lead discovery; Structure generation; In silico ligand design; Chemical biology tools

Funding

  1. Technology Agency of the Czech Republic [TA02010212]
  2. Czech Science Foundation [P202/11/0968, 14-29032P]
  3. Grant Agency of of Charles University [154613]

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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.

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