# Structure–efficiency relationship of access group antibiotics via SK chromatic descriptors

## TL;DR

This paper explores how SK chromatic descriptors can predict the properties of antibiotics using graph theory.

## Contribution

The novel use of SK chromatic indices to model physicochemical properties of antibiotics is introduced.

## Key findings

- SK chromatic indices effectively predict physicochemical properties of antibiotics.
- Linear regression analysis links topological indices to molecular characteristics.
- The study provides a new approach for drug development using graph theory.

## Abstract

In graph theory, topological indices play a significant role as numerical descriptors of a graph, helping to summarize the physicochemical properties of a molecular graph. By capturing the molecular structure, they encode various aspects, including connectivity, complexity, molecular branching, and shape. Therefore, these indices are crucial in the initial stages of drug development for identifying potential drugs. In this study, quantitative structure–property relationship (QSPR) models were designed using SK chromatic indices to predict the physicochemical attributes of some access group antibiotics. Linear regression is used to analyze the physicochemical properties and the topological indices.