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Molpher: a software framework for systematic chemical space exploration.

David Hoksza1, Petr Skoda, Milan Voršilák

  • 1Laboratory of Informatics and Chemistry, Faculty of Chemical Technology, Institute of Chemical Technology Prague, Technická 5, CZ-166 28 Prague, Czech Republic. hoksza@ksi.mff.cuni.cz.

Journal of Cheminformatics
|March 25, 2014
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Summary
This summary is machine-generated.

Molpher is an open-source software tool that systematically explores chemical space using "molecular morphing." This method generates paths of related compounds, aiding in the discovery of new drugs and chemical biology tools.

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Area of Science:

  • Chemoinformatics
  • Computational Chemistry
  • Drug Discovery

Background:

  • Chemical space represents all possible organic compounds.
  • Systematic exploration of chemical space is crucial for discovering novel drugs and chemical biology tools.
  • Chemoinformatics research relies on understanding and navigating chemical space.

Purpose of the Study:

  • To introduce Molpher, an open-source framework for systematic chemical space exploration.
  • To enable the generation of structurally related compound paths via molecular morphing.
  • To provide a versatile tool for drug discovery and chemical biology research.

Main Methods:

  • Utilizes 'molecular morphing' with iterative application of structural change operators.
  • Incorporates an optimized parallel exploration algorithm.
  • Features compound logging and 2D visualization of the exploration process.

Main Results:

  • Molpher generates paths of structurally related compounds through molecular morphing.
  • The framework is extensible, allowing for new morphing operators, fingerprints, similarity measures, and visualizations.
  • Offers both a graphical user interface and batch mode for integration into existing pipelines.

Conclusions:

  • Molpher is an open-source framework for designing virtual chemical libraries.
  • Facilitates the creation of libraries focused on specific mechanistic compound classes.
  • Provides valuable data for in silico and in vitro experiments and integrates into computational drug design pipelines.