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Canonicalizing BigSMILES for Polymers with Defined Backbones.

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This study introduces two methods to create unique identifiers for polymers using BigSMILES notation. These canonical representations simplify polymer comparison and searching in chemical databases.

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Area of Science:

  • Polymer Chemistry
  • Computational Chemistry
  • Cheminformatics

Background:

  • BigSMILES notation offers a compact way to represent polymer structures.
  • Current BigSMILES representations lack uniqueness, leading to multiple valid strings for the same polymer.
  • This ambiguity complicates polymer comparison and database searching.

Purpose of the Study:

  • To develop canonicalization schemes for BigSMILES strings representing linear polymers.
  • To generate unique, structure-based text identifiers for polymers.
  • To enhance polymer informatics and chemical information systems.

Main Methods:

  • Developed a canonicalization scheme to standardize BigSMILES expression.
  • Applied formal language theory by converting linear polymers into regular languages.
  • Utilized minimal deterministic finite automata to create unique polymer identifiers.

Main Results:

  • Proposed two distinct strategies for deriving canonical BigSMILES representations.
  • Demonstrated algorithms for converting linear polymers into unique text identifiers.
  • Established a method for generating unique identifiers based on polymer structure.

Conclusions:

  • The developed algorithms provide unique structure-based text identifiers for linear polymers.
  • These identifiers can streamline structural comparison and molecular searches.
  • The findings will benefit polymer informatics and chemical information systems.