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BigSMARTS: A Topologically Aware Query Language and Substructure Search Algorithm for Polymer Chemical Structures.

Nathan J Rebello1, Tzyy-Shyang Lin1, Heeba Nazeer2

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A new polymer search algorithm and BigSMARTS query language enable precise molecular structure identification. This advances chemical data accessibility and innovation by accurately capturing polymer connectivity and topology.

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Area of Science:

  • Polymer chemistry
  • Computational chemistry
  • Cheminformatics

Background:

  • Molecular search is crucial for data discovery in chemistry and biology.
  • Current polymer search methods are limited, relying on broad names or incomplete substructure matching.
  • Existing methods lack awareness of polymer connectivity and topology.

Purpose of the Study:

  • Introduce a novel query language and search algorithm for comprehensive polymer structure identification.
  • Enable precise localization of monomers and functional groups within complex polymer architectures.
  • Improve the findability, accessibility, interoperability, and reusability (FAIR) of chemical data.

Main Methods:

  • Developed BigSMARTS, an extension of SMARTS, for detailed polymer querying.
  • Implemented a graph traversal search algorithm using polymer generating functions.
  • Algorithm identifies monomers, end groups, and performs depth-first search for subgraph matching.

Main Results:

  • Validated the algorithm with extensive testing against diverse polymer chemistries and topologies.
  • Demonstrated the ability to fully capture chemical structures, including monomer connectivity and topology.
  • Processed approximately 440,000 query-target pairs.

Conclusions:

  • The BigSMARTS query language and novel algorithm offer the first complete polymer structure search capability.
  • This tool enhances polymer data analysis and facilitates knowledge discovery.
  • The algorithm is suitable for implementation in search engines to improve polymer data retrieval.