QSPR Analysis of Eye Conjunctivitis Drops Using Regression Model via Degree Based Topological Indices

Abstract

Introduction: Topological indices (TIs) are numerical values derived from the structural graph of a molecule, vital in cheminformatics for predicting various properties. In chemical graph theory, graphs represent molecular structures where vertices correspond to atoms and edges to bonds. Degree-based indices, reflecting vertex connectivity, are used to predict properties such as boiling point and molar refractivity. This study analyses eight eye drops—ciprofloxacin, gentamicin, moxifloxacin, norfloxacin, tobramycin, levofloxacin, gatifloxacin, and chloramphenicol—using these indices.

Objectives: To analyse the degree-based topological indices of eight eye drops and predict their physicochemical properties, including boiling point, enthalpy, mass, flash point, molar refractivity, and volume, using a linear regression model.

Methods: Degree-based topological indices were computed through edge partitioning. A Quantitative Structure-Property Relationship (QSPR) model was developed with linear regression to predict the properties of the eye drops. Prediction accuracy was evaluated by comparing predicted values to actual values and analysing the associated errors.

Results: The study found a strong correlation between the topological indices and the physicochemical properties of the eye drops. The linear regression model accurately predicted these properties, with minimal error, demonstrating the effectiveness of using TIs in this context.

Conclusions This research highlights the effectiveness of topological indices and linear regression models in predicting the properties of eye drops. These tools offer valuable insights for drug design and development, paving the way for more effective treatments for eye conditions.