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Computational Modelling of Antimicrobial Efficacy against Staphylococcus aureus Using Structure-Activity Analysis

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Author Affiliations

  • 1GCRC, P.G. Department of Chemistry, Govt Dungar College (NAAC ‘A’ Grade), MGS University, Bikaner (Raj.) 334001 India
  • 2GCRC, P.G. Department of Chemistry, Govt Dungar College (NAAC ‘A’ Grade), MGS University, Bikaner (Raj.) 334001 India
  • 3GCRC, P.G. Department of Chemistry, Govt Dungar College (NAAC ‘A’ Grade), MGS University, Bikaner (Raj.) 334001 India
  • 4GCRC, P.G. Department of Chemistry, Govt Dungar College (NAAC ‘A’ Grade), MGS University, Bikaner (Raj.) 334001 India

Res. J. Forensic Sci., Volume 14, Issue (1), Pages 7-11, January,29 (2026)

Abstract

The field of computational drug discovery employs Quantitative Structure-Activity Relationship (QSAR) methodologies to construct mathematical models that correlate molecular characteristics with biological effectiveness. These computational approaches enable researchers to understand how specific chemical features influence therapeutic outcomes, thereby facilitating rational drug development strategies. This investigation focused on constructing a predictive framework to estimate the antibacterial potency of 24 chemical entities against Staphylococcus aureus. The antimicrobial effectiveness was quantified through Minimum Inhibitory Concentration (MIC) measurements, while computational molecular parameters were utilized to establish the predictive framework. The entire QSAR analysis was conducted using ChemMaster 1.2 computational platform.

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