PyTIE: A Python Program for the Evaluation of Degree-Based Topological Descriptors and Molecular Entropy

TL;DR

PyTIE is an open-source Python package that efficiently computes topological indices and molecular entropy, aiding researchers in chemistry and chemical engineering for structure-property analyses.

Contribution

It introduces a comprehensive, user-friendly Python toolkit for calculating degree-based topological descriptors and entropy, streamlining molecular analysis workflows.

Findings

PyTIE enables rapid computation of topological indices.

The package supports visualization of molecular descriptors.

Demonstrated efficiency through illustrative case studies.

Abstract

We have developed PyTIE (Python Topological Indices Expressions) which is defined as the collections of Python packages such as PyTIE D, PyTIE DS, PyTIE SMS DE, and PyTIE SMS DSE, which are open-source software packages and cross-platform Python package designed to expedite the retrieval of results for mathematics, chemistry and chemical engineering researchers within constant time. This open-source tool extends its utility to chemistry and chemical engineering researchers with limited mathematical proficiency. PyTIE facilitates the loading of molecular graphs, specifying parameters such as minimum degree, maximum degree, and the number of vertex pairs (edge partitions). The edge partitions of a molecular graph based on degree sum also plays a crucial role in predicting heat of formation and enthalpy of formation along with DFT techniques. It systematically computes expressions and numerical values for various topological indices, including degree-based and neighborhood degree-based indices, as well as Shannon's entropy, providing visual representations of the results. Emphasizing topological indices for Quantitative Structure-Activity Relationship and Quantitative Structure-Property Relationship analyses, PyTIE proves particularly relevant in these studies. Serving as a Python package, it seamlessly integrates with libraries such as NumPy, math and SymPy offering extensive options for data analysis. The efficiency of PyTIE is demonstrated through illustrative examples in various contexts.