On Novel Approach for Computing Distance based Indices of Anti-tuberculosis Drugs

TL;DR

This paper introduces new distance-based indices derived from molecular graphs of anti-tuberculosis drugs, improving the accuracy of structure-property predictions and potentially reducing the need for clinical trials.

Contribution

It presents novel distance-based indices using actual bond lengths and 3D molecular graphs, enhancing the correlation with physical properties of drugs.

Findings

Strong correlation between indices and drug properties

Improved accuracy over previous models

Potential to predict drug characteristics without clinical trials

Abstract

This work aims to assess the molecular architectures of anti-tuberculosis drugs using both degree-based topological indices and novel distance based indices. We can represent the chemical arrangement as a graph, with atoms serving as the vertices and connections as the edges. Here, the multi bonds were considered as multi edges and included all the hydrogen atoms. Also, we consider three dimensional molecular graph. As a result, the actual bond lengths have been used for computation of new distance based indices. Compared to numerous studies, this is a significant improvement. Furthermore, the investigation of these indices includes a study on the quantitative structure-property relationship (QSPR). The research demonstrates a notable correlation between these indicators and the physical attributes of anti-tuberculosis drugs. Here, Since we reduced the some of existing critical assumptions in the literature, chemists and pharmaceutical professionals might potentially eliminate the need for clinical trials by employing this theoretical model. These models would enable them to predict the characteristics of anti-tuberculosis medications.