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Graph-Based Approach to Systematic Molecular Coarse-Graining.

Michael A Webb1, Jean-Yves Delannoy2, Juan J de Pablo1,3

  • 1Institute for Molecular Engineering , University of Chicago , Chicago , Illinois 60637 , United States.

Journal of Chemical Theory and Computation
|December 18, 2018
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Summary
This summary is machine-generated.

A new graph-based coarse-graining (GBCG) method automatically generates molecular models for simulations. This approach offers an efficient and robust way to create hierarchical representations, preserving chemical topology.

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Area of Science:

  • Computational chemistry
  • Molecular modeling
  • Materials science

Background:

  • Coarse-grained (CG) models simplify molecular systems for simulations.
  • Current CG mapping schemes often rely on arbitrary or empirical approaches.
  • Automated and systematic methods for generating CG representations are needed.

Purpose of the Study:

  • Introduce a novel, automated methodology for generating coarse-grained (CG) molecular representations.
  • Develop a graph-theoretic approach for creating hierarchical CG mappings.
  • Provide an algorithmic alternative to conventional, arbitrary CG mapping schemes.

Main Methods:

  • Graph-based coarse-graining (GBCG) utilizes graph-theoretic principles.
  • Atoms are represented as nodes in a molecular graph.
  • Edge contractions combine nodes into CG sites based on chemical connectivity, creating hierarchical resolutions.

Main Results:

  • GBCG successfully generated intuitive CG representations for diverse systems (toluene, pentadecane, polysaccharide dimer, rhodopsin).
  • The method preserves the chemical topology of the original molecular models.
  • Demonstrated automated, hierarchical, and systematic generation of CG mappings.

Conclusions:

  • GBCG is an efficient, robust, and unambiguous method for generating CG molecular models.
  • The algorithmic approach offers a systematic alternative to traditional CG mapping.
  • GBCG is a valuable tool for advancing CG molecular modeling and simulations.