ArticleFrontiers in chemistry2025
Predictive modeling of molecular interaction energies using topological and spectral entropies of zeolite AWW.
Article in Frontiers in chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Degree-Based Topological Indices and Machine Learning for QSPR Modeling of Arthritis Drugs.ACS omega · 2026Article
- Structural identifiability in crystalline molecular networks using strong metric dimension.Scientific reports · 2026Article
- Investigating topological indices and entropy measures for titanium diboride network.Discover nano · 2026Article
- ChemGraphX: an open-source web tool for computing topological indices and entropy measures.Journal of cheminformatics · 2026Article
- QSPR analysis of the drugs used to treat major depressive disorder using degree and neighborhood degree based indices.Frontiers in chemistry · 2026Article
- Face reverse degree topological analysis of TP-COFs, existence of isentropic COFs and spectral characteristics.Frontiers in chemistry · 2026Article
- Independent set-based multivariate graph polynomials for fractal-type silicate triangle structures.Frontiers in chemistry · 2026Article
- Predictive modeling of copper iodide properties using graph-theoretical descriptors.Scientific reports · 2025Article
- A graph-based computational approach for modeling physicochemical properties in drug design.Scientific reports · 2025Article
- Multifractal based topological characterization of perovskite crystal and predictive analysis on its physical properties.Frontiers in chemistry · 2025Article
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Authors and funding
4 authors.
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Abstract
Zeolites are extremely massive mineral crystals with complex frameworks, composed of internal porous structures with channels and cages. Open-framework aluminophosphates (AlPOs) are a significant class of inorganic crystalline compounds known for their diverse properties. Our study focuses on the topological aspects of zeolite frameworks using graph theoretical techniques, providing insights into computational chemistry. In this article, we compute various degree-based topological indices, information entropy, and spectral entropies of zeolite AWW using the bond partitioning approach to assess the complexity of the framework. Additionally, we present the HOMO-LUMO gap measures to evaluate the global chemical descriptors using the eigenvalues of the adjacency matrix of zeolite structures. Furthermore, we developed exponential and polynomial regression models using the obtained information entropy and spectral values to predict various potential energies of the framework. Based on the outcomes of the study, we infer that the information entropies and spectral value have a significant relationship with the potential energies.
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