Investigating the properties of octane isomers by novel neighborhood product degree-based topological indices

被引:4
作者
Rasheed, Muhammad Waheed [1 ]
Mahboob, Abid [1 ]
Hanif, Iqra [1 ]
机构
[1] Univ Educ, Dept Math, Div Sci & Technol, Lahore, Pakistan
关键词
np-degrees; vertex degrees; QSPR analysis; linear regression model; quadratic regression model; MOLECULAR-ORBITALS; GRAPH-THEORY;
D O I
10.3389/fphy.2024.1369939
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
A topological index is a real number calculated from the structure of a chemical compound to describe its topology. The use of molecular descriptors has been increasing in recent years, helping to determine the physicochemical and biological properties of drugs. The main purpose of this article is to investigate the properties of the octane isomers using the theoretical method. To study the structures of octane isomers, we have introduced a new approach called "neighborhood product degree" to calculate all the classical degree-based topological indices. The np-degree approach is applied to approximate eight properties of octane isomers, such as the acentric factor, density, refractive index, critical volume, molar volume, radius of curvature, critical pressure, and LogP. The np-degree-based topological indices are the estimated values of the properties of octane structures, so the linear and quadratic regression models and correlation coefficients are applied to check the validity of the estimated results. The quantitative structure property relation are obtained by using the linear, quadratic, exponential, logarithmic and sinusoidal regression methods with the help of SPSS. Two models are applied to all the compuations and three regression models are applied to the np-degree Randic index. The computation showed that quadratic regression model is suitable for study octane isomers and np-degree based graph invariants. If the values of the correlation coefficient r > 0.7, p-values <= 0.05, and F-values > 2.5, then the results are significant. The results of np-degree-based topological indices satisfy all the criteria for being significant, so these newly introduced indices are valid to study octane isomers. The information determined in this article is beneficial for chemists and pharmacists.
引用
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页数:19
相关论文
共 39 条
[1]   Novel molecular hybrid geometric-harmonic-Zagreb degree based descriptors and their efficacy in QSPR studies of polycyclic aromatic hydrocarbons [J].
Arockiaraj, M. ;
Paul, D. ;
Clement, J. ;
Tigga, S. ;
Jacob, K. ;
Balasubramanian, K. .
SAR AND QSAR IN ENVIRONMENTAL RESEARCH, 2023, 34 (07) :569-589
[2]   Comparative Analysis of Reverse Degree and Entropy Topological Indices for Drug Molecules in Blood Cancer Treatment through QSPR Regression Models [J].
Arockiaraj, Micheal ;
Greeni, A. Berin ;
Kalaam, A. R. Abul .
POLYCYCLIC AROMATIC COMPOUNDS, 2024, 44 (09) :6024-6041
[3]   Linear versus cubic regression models for analyzing generalized reverse degree based topological indices of certain latest corona treatment drug molecules [J].
Arockiaraj, Micheal ;
Greeni, A. Berin ;
Kalaam, A. R. Abul .
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY, 2023, 123 (16)
[4]   On some neighbourhood degree-based topological indices with QSPR analysis of asthma drugs [J].
Balasubramaniyan, Deepa ;
Chidambaram, Natarajan .
EUROPEAN PHYSICAL JOURNAL PLUS, 2023, 138 (09)
[5]   On ve-degrees and ev-degrees in graphs [J].
Chellali, Mustapha ;
Haynes, Teresa W. ;
Hedetniemi, Stephen T. ;
Lewis, Thomas M. .
DISCRETE MATHEMATICS, 2017, 340 (02) :31-38
[6]   On neighborhood inverse sum indeg index of molecular graphs with chemical significance [J].
Das, Kinkar Chandra ;
Mondal, Sourav .
INFORMATION SCIENCES, 2023, 623 :112-131
[7]  
Ediz S., 2017, Predicting some physicochemical properties of octane isomers: a topological approach using ev-degree and ve-degree Zagreb indices, P1, DOI [10.48550/arXiv.1701.02859s, DOI 10.48550/ARXIV.1701.02859S]
[8]  
Fajtlowicz S., 1987, C NUMER, V60, P187, DOI DOI 10.4236/APM.2014.45021
[9]  
Gayathri A., 2024, J.Sci.Multidiscip, V6, p2024ss0112, DOI [10.31893/multiscience.2024ss0112, DOI 10.31893/MULTISCIENCE.2024SS0112]
[10]   Machine learning, a powerful tool for the prediction of BiVO4 nanoparticles efficiency in photocatalytic degradation of organic dyes [J].
Gnanaprakasam, A. ;
Thirumarimurugan, M. ;
Shanmathi, N. .
JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH PART A-TOXIC/HAZARDOUS SUBSTANCES & ENVIRONMENTAL ENGINEERING, 2024, 59 (01) :15-24