Implementing QSPR modeling via multiple linear regression analysis to operations research: a study toward nanotubes

Hui, Zhi‐hao; Aslam, Adnan; Kanwal, Salma; Saeed, Saadia; Sarwar, K. S.

doi:10.1140/epjp/s13360-023-03817-5

Cited by 15 publications

(10 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They are successfully being utilized for predicting absorption, distribution, metabolism, excretion and toxicity. Further, they are used to predict flash point, energy, and many such properties of chemical compounds which can be found in various literatures [51] , [52] , [53] , [54] , [55] , [56] , [57] , [58] , [59] , [60] , [61] .…”

Section: Discussionmentioning

confidence: 99%

QSPR analysis of distance-based structural indices for drug compounds in tuberculosis treatment

Arockiaraj,

Campena,

Berin Greeni

et al. 2024

Heliyon

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

QSPR analysis of distance-based structural indices for drug compounds in tuberculosis treatment

Arockiaraj,

Campena,

Berin Greeni

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…Ranking of different chemical structure using different multi-criteria decision making technique is currently an active area of research. The study of some networks [23], metallic crystals [24] and nanotubes [25] through multi-criteria decision making is done by different researchers. The study of different drugs structures by using different multi-criteria decision making can be done in the future.…”

Section: Discussionmentioning

confidence: 99%

“…Different multi-criteria decision making techniques are used by researchers to rank chemical structures. For more details see [22][23][24][25][26][27].…”

Section: Multi-criteria Decision Makingmentioning

confidence: 99%

Investigating hexagonal closed packed crystal lattice through QSPR modeling via linear regression analysis and Topsis

Aslam,

Saeed,

Kanwal

et al. 2024

Phys. Scr.

Self Cite

View full text Add to dashboard Cite

This research work covers three highly dominating reverse degree-based topological descriptors evaluated for the three-dimensional hexagonal close-packed (HCP) crystal structure lattice. HCP crystal lattice has a highly symmetrical and elegant crystal structure, and we have observed that results obtained from symmetrical structures draw symmetry in the numerical formulation obtained from topological descriptors. For this purpose, we will investigate whether the HCP lattice formed by an odd pair of unit cells is the best or an even pair of unit cells forming the lattice and how they behave in a dominating sense when investigated through the conclusions obtained from QSPR modeling. The QSPR model has been proposed to check the efficiency of each specified topological descriptor for five highly commendable physio-chemical properties: melting or boiling point, density, molar heat capacity, and enthalpy. Further, we have discovered the ideology to obtain the best possible structure when investigated and established on a statistically and mathematically strong proving and to what extent we can extract from QSPR to implement MCDM techniques, here considering TOPSIS that provide the best HCP structure lattice rankings. The obtained results may be helpful for researchers to better understand the structure and studying different physical/chemical properties of HCP crystal lattice

show abstract

“…Further, we have incorporated the preeminent decision-making method, multi-criteria decision making (MCDM), into the realm of topological indices to attain optimality and ideality in carbon nanostructures. Recent papers employing MCDM methodologies for structural analysis encompass nanotubes, cancer drug compounds, oxide and silicate networks, and fuzzy graphs [35][36][37][38]. The article's structure is outlined as follows: In Section 2, we compute modified reverse degree-based indices for carbon nanosheets.…”

mentioning

confidence: 99%

Comparative analysis of modified reverse degree topological indices for certain carbon nanosheets using entropy measures and multi criteria decision‐making analysis

Abul Kalaam,

Berin Greeni

2024

Int J of Quantum Chemistry

View full text Add to dashboard Cite

The diverse applications of various carbon nanostructures have sparked significant research interest. The potential significance of carbon nanosheets lies in their exceptional mechanical, electrical, and thermal properties, enabling advancements in various technological applications such as energy storage, electronics, and engineering, playing important roles in material science and nanotechnology. In the field of mathematical chemistry, topological indices play a crucial role by providing numerical insights into molecular structures. These insights facilitate predictive correlations with chemical properties and reactivity, ultimately finding utility in areas like drug design and material sciences. Expanding on this, the study delves into the application of entropy‐based methodologies that originates from the arrangement of chemical structures. By assessing the complexity and other features of these structures, graph entropies are translated into information‐theoretic metrics. This article examines modified reverse degree‐based topological indices with its universal applicability to all degree‐based indices. The standout feature is the adaptable parameter “” effectively shaping the molecular graphs degree sequence to best fit each dataset with their unique physicochemical properties, distinguishing it from conventional fixed degree methodologies. Additionally, we investigate graph entropies and examine the impact of varying the parameter “” on entropy measures across a range of nanosheet structures. The research focuses on characterizing carbon nanosheets, employing effective (MCDM) multiple criteria decision‐making methods like VIKOR, TOPSIS, and SAW. Through these techniques, a comprehensive comparative analysis of the nanosheets is conducted with the aim of establishing optimized rankings for each type based on their unique attributes and characteristics.

show abstract

Implementing QSPR modeling via multiple linear regression analysis to operations research: a study toward nanotubes

Cited by 15 publications

References 38 publications

QSPR analysis of distance-based structural indices for drug compounds in tuberculosis treatment

QSPR analysis of distance-based structural indices for drug compounds in tuberculosis treatment

Investigating hexagonal closed packed crystal lattice through QSPR modeling via linear regression analysis and Topsis

Comparative analysis of modified reverse degree topological indices for certain carbon nanosheets using entropy measures and multi criteria decision‐making analysis

Contact Info

Product

Resources

About