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This study introduces a generalized method for molecular structure characterization using relation frequency matrices and discrete derivatives. New indices accurately predict physicochemical properties like logP and logK, proving valuable for chemoinformatics.

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Area of Science:

  • Computational Chemistry
  • Chemoinformatics
  • Quantitative Structure-Property Relationships (QSPR)

Background:

  • Molecular structure characterization is crucial for understanding chemical properties.
  • Existing methods often rely on specific subgraph definitions.
  • A generalized approach is needed to capture diverse structural information.

Purpose of the Study:

  • To introduce a generalized approach for molecular structure characterization.
  • To develop novel local vertex invariants (LOVIs) based on relation frequency matrices and discrete derivatives.
  • To apply these LOVIs for calculating global and local chemical indices and building QSPR models.

Main Methods:

  • Utilized relation frequency matrix (F) representation of molecular graphs.
  • Introduced eleven new 'events' (topological, fingerprint, atomic contribution) for F generation.
  • Calculated discrete derivatives over atom pairs to obtain LOVIs, then aggregated them into chemical indices using statistical methods.

Main Results:

  • Developed a generalized method incorporating diverse structural information through eleven new events.
  • Implemented indices in DIVATI software, showing orthogonality and diverse information capture.
  • Achieved high correlations in QSPR models for logP and logK prediction, with specific events excelling (Sach's subgraphs for logK, Multiplicity for logP).

Conclusions:

  • The generalized approach and resulting event-based indices offer a powerful tool for chemoinformatics.
  • The developed LOVIs and derived indices effectively characterize molecular structures.
  • The method demonstrates significant potential for accurate prediction of physicochemical properties.