Neighborhood Face Index: A New Quantitative Structure Property Relationship (QSPR) Approach for Predicting Physical Properties of Polycyclic Chemical Compounds

被引:0
|
作者
Raza, Ali [1 ]
Rasheed, Muhammad Waheed [2 ]
Mahboob, Abid [3 ]
Ismaeel, Mishal [3 ]
机构
[1] Univ Punjab, Dept Math, Lahore, Pakistan
[2] COMSATS Univ Islamabad, Dept Math, Vehari Campus, Vehari, Pakistan
[3] Univ Educ, Dept Math, Div Sci & Technol, Lahore, Pakistan
关键词
nanosheets; neighborhood degree; regression models; topological descriptor;
D O I
10.1002/qua.27524
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Topological indices (TIs) are numerical parameters that characterize the biochemical and physio-chemical properties of compounds. These graph-based descriptors are valuable tools for predicting key attributes, such as melting points, boiling points, bond energies, and bond lengths, based on the molecular structures of the compounds. A variety of TIs have been developed, including the Randi & cacute; index, Zagreb index, atom-bond connectivity index, geometric index, and harmonic index. In this work, we introduce a new topological index called the neighborhood face index, which demonstrates a strong correlation with various physical properties such as bond energies and boiling points, achieving a correlation coefficient of R >= 0.9994$$ R\ge 0.9994 $$. This indicates its robust predictive capability. Furthermore, the results are thoroughly analyzed using graphical tools to provide deeper insights.
引用
收藏
页数:12
相关论文
共 50 条
  • [21] Prediction of viscosity index and pour point in ester lubricants using quantitative structure-property relationship (QSPR)
    Nasab, Shima Ghanavati
    Semnani, Abolfazl
    Marini, Federico
    Biancolillo, Alessandra
    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2018, 183 : 59 - 78
  • [22] Prediction of physical and chemical properties by quantitative structure-property relationships
    Liang, C
    Gallagher, DA
    AMERICAN LABORATORY, 1997, 29 (06) : 34 - +
  • [23] Evaluating the properties of ionic liquid at variable temperatures and pressures by quantitative structure-property relationship (QSPR)
    Zhang, Shuying
    Jia, Qingzhu
    Yan, Fangyou
    Xia, Shuqian
    Wang, Qiang
    CHEMICAL ENGINEERING SCIENCE, 2021, 231
  • [24] Flash Point and Cetane Number Predictions for Fuel Compounds Using Quantitative Structure Property Relationship (QSPR) Methods
    Saldana, Diego Alonso
    Starck, Laurie
    Mougin, Pascal
    Rousseau, Bernard
    Pidol, Ludivine
    Jeuland, Nicolas
    Creton, Benoit
    ENERGY & FUELS, 2011, 25 (09) : 3900 - 3908
  • [25] A fuzzy ARTMAP based quantitative structure-property relationship (QSPR) for predicting aqueous solubility of organic compounds (vol 41, pg 1177, 2001)
    Yaffe, D
    Cohen, Y
    Espinosa, G
    Arenas, A
    Giralt, F
    JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES, 2002, 42 (03): : 768 - 768
  • [26] General quantitative structure-property relationship treatment of the refractive index of organic compounds
    Katritzky, AR
    Sild, S
    Karelson, M
    JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES, 1998, 38 (05): : 840 - 844
  • [27] Multiobjective Feature Selection Approach to Quantitative Structure Property Relationship Models for Predicting the Octane Number of Compounds Found in Gasoline
    Liu, Zhefu
    Zhang, Linzhou
    Elkamel, Ali
    Liang, Dong
    Zhao, Suoqi
    Xu, Chunming
    Ivanov, Stanislav Y.
    Ray, Ajay K.
    ENERGY & FUELS, 2017, 31 (06) : 5828 - 5839
  • [28] Predicting the Decomposition Temperature of Ionic Liquids by the Quantitative Structure-Property Relationship Method Using a New Topological Index
    Yan, Fangyou
    Xia, Shuqian
    Wang, Qiang
    Ma, Peisheng
    JOURNAL OF CHEMICAL AND ENGINEERING DATA, 2012, 57 (03): : 805 - 810
  • [29] A quantitative structure-property relationship (QSPR) for estimating solid material-air partition coefficients of organic compounds
    Huang, Lei
    Jolliet, Olivier
    INDOOR AIR, 2019, 29 (01) : 79 - 88
  • [30] Quantitative Structure-Property Relationship (QSPR) of Plant Phenolic Compounds in Rapeseed Oil and Comparison of Antioxidant Measurement Methods
    Platzer, Melanie
    Kiese, Sandra
    Asam, Tobias
    Schneider, Franziska
    Tybussek, Thorsten
    Herfellner, Thomas
    Schweiggert-Weisz, Ute
    Eisner, Peter
    PROCESSES, 2022, 10 (07)