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Updated: Feb 28, 2026

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Improving the SSIR Method: Implementation of an Exhaustive Multilevel Scan for Categorical Variables.

Emili Besalú1

  • 1Institut de Química Computacional i Catàlisi (IQCC) and Departament de Química, Universitat de Girona, 17071 Girona, Spain.

International Journal of Molecular Sciences
|February 27, 2026
PubMed
Summary

The Superposing Significant Interaction Rules (SSIR) method was extended to analyze multilevel substituent conditions in chemical families. This enhanced approach expands rule definition capabilities and aids in predicting new molecular structures, including anti-HIV compounds.

Keywords:
SSIR methodSSIR with multilevelsanalogue seriesanti-HIV compoundsin silico synthesisrankingstructure–activity relationships

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Area of Science:

  • Computational chemistry
  • Medicinal chemistry
  • Drug discovery

Background:

  • The Superposing Significant Interaction Rules (SSIR) method originally evaluated single substituent presence/absence in molecular structures.
  • Combinatorial chemistry relies on systematic exploration of molecular variations.

Purpose of the Study:

  • To extend the SSIR method for analyzing multilevel substituent conditions.
  • To expand the scope of definable rules in chemical structure analysis.
  • To demonstrate the method's utility in predicting novel molecular structures.

Main Methods:

  • Revisiting and extending the original SSIR algorithm.
  • Incorporating multilevel conditions for residue groups at substitution sites.
  • Applying the extended SSIR to a family of anti-HIV compounds.

Main Results:

  • The extended SSIR framework encompasses the original version as a specific case.
  • The method allows for a broader universe of definable rules.
  • Illustrative example shows potential for predicting new in silico molecular structures.

Conclusions:

  • The extended SSIR method provides a more comprehensive framework for analyzing chemical families.
  • This approach enhances the prediction of novel compounds with potential therapeutic applications.
  • The method is applicable to drug discovery and virtual screening processes.