Molecular Insight into Anti-Biofilm Agents: Unraveling Mechanisms with Topological Descriptors and QSPR Analysis

This study explores an innovative method to improve the efficiency of anti-biofilm drug design by utilizing the potential of topological indices which is computed by the development of Python algorithm. This work explores the accuracy of topological indices for anti-biofilm activity, with insights from graph theory to measure molecular complexity as well as structural characteristics associated with biofilm inhibition. In order to determine the best regression strategy, we use linear regression and compare them with logarithmic regression which estimates the link between molecular descriptors and anti-biofilm structural characteristics. Furthermore, to provide insight into the molecular mechanisms underlying anti-biofilm activity and the structural prerequisites for successful biofilm suppression, we also employ Quantitative Structure–Property Relationship (QSPR) analysis. By using an integrative strategy, our study provides important insights for the development of successful treatment against biofilm-associated infections and helps rationally develop new anti-biofilm drugs.