# Quantitative structure–property relationship modeling and ranking of necrotizing fasciitis drugs via degree-based topological indices

## TL;DR

This paper uses computational methods to predict and rank antibiotics for treating necrotizing fasciitis based on their molecular structures.

## Contribution

The novel integration of degree-based topological indices with regression and decision-making techniques for drug ranking in necrotizing fasciitis.

## Key findings

- Degree-based topological indices effectively model the physicochemical properties of NF antibiotics.
- Regression analysis identifies significant indices for drug property prediction.
- Multi-criteria decision-making ranks antibiotics based on predicted properties.

## Abstract

The increasing incidence and high mortality rate of necrotizing fasciitis (NF), a rapidly progressing infection of the fascia and subcutaneous tissue, highlights the urgent need for effective drug evaluation strategies. Traditional clinical trials for NF antibiotics are costly and time-consuming, necessitating the development of computational approaches that can reliably capture drug behavior.

The study employs degree-based topological indices to represent molecular structures of NF antibiotics and develops QSPR models to predict their physicochemical properties. Calculating topological indices, performing regression analyses to identify significant indices, and using these indices in multi-criteria decision-making techniques to rank the antibiotics.

This study demonstrates the potential of degree-based TIs combined with regression and multi-criteria decision-making techniques to predict and rank the physicochemical properties of antibiotics used to treat necrotizing fasciitis (NF).

This integrated approach demonstrates the utility of topological indices in predicting drug properties, prioritizing candidates, and supporting the rational design and repurposing of NF therapeutics.