Insights into network properties: spectrum-based analysis with Laplacian and signless Laplacian spectra

Raza, Ali; Munir, Muhammad Mobeen

doi:10.1140/epjp/s13360-023-04441-z

Cited by 6 publications

(3 citation statements)

References 38 publications

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“…Simultaneously, we aim to identify descriptors that warrant the attention of researchers, effectively refining the scope of this field. Our recent work describes the applications of some spectrum descriptors in the field of computer sciences [5,6].…”

Section: Introductionmentioning

confidence: 99%

Exploring spectrum-based descriptors in pharmacological traits through quantitative structure property (QSPR) analysis

Raza,

Munir

2024

Front. Phys.

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The study centered on Quantitative Structure Property Relationship (QSPR) analysis with a focus on various graph energies, investigating drugs like Mefloquinone, Sertraline, Niclosamide, Tizoxanide, PHA-690509, Ribavirin, Emricasan, and Sofosbuvir. Employing computational modeling techniques, the research aimed to uncover the correlations between the chemical structures of these medications and their unique properties. The results illuminated the quantitative relationships between structural characteristics and pharmacological traits, advancing our predictive capabilities. This research significantly contributes to medication discovery and design by providing essential insights into the structure-property connections of these medicinal compounds. Notably, certain spectrum-based descriptors, such as positive inertia energy, adjacency energy, arithmetic-geometric energy, first zegrab energy, and the harmonic index, exhibited strong correlation coefficients above 0.999. In contrast, well-known descriptors like the Extended adjacency, Laplacian and signless Laplacian spectral radii, and the first and second Zagreb Estrada indices showed weaker performance. The article emphasizes the application of graph energies and a linear regression model to predict pharmacological features effectively, enhancing the drug discovery process and aiding in targeted drug design by elucidating the relationship between molecular structure and pharmacological characteristics.

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Section: Introductionmentioning

confidence: 99%

Exploring spectrum-based descriptors in pharmacological traits through quantitative structure property (QSPR) analysis

Raza,

Munir

2024

Front. Phys.

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“…Additionally, in material science, topological indices play a role in predicting various physicochemical properties, contributing to the development of novel materials with tailored characteristics [14]. The usefulness of topological indices can be seen in bioinformatics, where they are used to study molecular networks and figure out how living things are connected [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Neighbourhood Degree-Based Graph Descriptors: A Comprehensive Analysis of Connectivity Patterns in Diverse Graph Families and Their Applicapability

Alameri,

Mahboob,

Karash

2024

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In the field of graph theory, the exploration of connectivity patterns within various graph families is paramount. This study is dedicated to the examination of the neighbourhood degree-based topological index, a quantitative measure devised to elucidate the structural complexities inherent in diverse graph families. An initial overview of existing topological indices sets the stage for the introduction of the mathematical formulation and theoretical underpinnings of the neighbourhood degree-based index. Through meticulous analysis, the efficacy of this index in delineating unique connectivity patterns and structural characteristics across graph families is demonstrated. The utility of the neighbourhood degree-based index extends beyond theoretical graph theory, finding applicability in network science, chemistry, and social network analysis, thereby underscoring its interdisciplinary relevance. By offering a novel perspective on topological indices and their role in deciphering complex network structures, this research makes a significant contribution to the advancement of graph theory. The findings not only underscore the versatility of the neighbourhood degree-based topological index but also highlight its potential as a tool for understanding connectivity patterns in a wide array of contexts. This comprehensive analysis not only enriches the theoretical landscape of graph descriptors but also paves the way for practical applications in various scientific domains, illustrating the profound impact of graph theoretical studies on understanding the intricacies of networked systems.

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“…Researchers have also looked into how to combine topological indices with other molecular descriptors to make hybrid models that give a fuller picture of how molecules behave [22]. Beyond their role in drug design, isomer discrimination, chemical documentation, and biological characterization, TIs find special applications in mathematical chemistry [23][24][25][26]. This diverse range of applications underscores the versatility and importance of TIs in understanding and manipulating molecular structures, making them indispensable tools in various scientific and industrial pursuits.…”

Section: Introductionmentioning

confidence: 99%

Exploring Novel Topological Descriptors: Geometric-Harmonic and Harmonic-Geometric Descriptors for HAC and HAP Conjugates

Raza,

Tolasa

2024

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In this investigation, the exact formulas for geometric-harmonic (GH), neighborhood geometric-harmonic (NGH), harmonic-geometric (HG), and neighborhood harmonic-geometric (NHG) indices were systematically evaluated for hyaluronic acid-curcumin (HAC) and hyaluronic acid-paclitaxel (HAP) conjugates. Through this evaluation, a comprehensive quantitative assessment was conducted to elucidate the structural characteristics of these conjugates, highlighting the intricate geometric and harmonic relationships present within their molecular graphs. The study leveraged these indices to illuminate the complex interplay between geometric and harmonic properties, providing a novel perspective on the molecular architecture of HAC and HAP conjugates. This analytical approach not only sheds light on the structural nuances of these compounds but also offers a unique lens through which their potential in drug delivery applications can be assessed. Graphical analyses of the results further enhance the understanding of these molecular properties, presenting a detailed visualization that complements the quantitative findings. The integration of these topological descriptors into the study of HAC and HAP conjugates represents a significant advance in the field of medicinal chemistry, offering valuable insights for researchers engaged in the development of innovative drug delivery systems. The findings underscore the utility of these descriptors in characterizing the molecular topology of complex conjugates, setting the stage for further exploration of their applications in therapeutic contexts.

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Insights into network properties: spectrum-based analysis with Laplacian and signless Laplacian spectra

Cited by 6 publications

References 38 publications

Exploring spectrum-based descriptors in pharmacological traits through quantitative structure property (QSPR) analysis

Exploring spectrum-based descriptors in pharmacological traits through quantitative structure property (QSPR) analysis

Neighbourhood Degree-Based Graph Descriptors: A Comprehensive Analysis of Connectivity Patterns in Diverse Graph Families and Their Applicapability

Exploring Novel Topological Descriptors: Geometric-Harmonic and Harmonic-Geometric Descriptors for HAC and HAP Conjugates

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