A conceptual DFT and information-theoretic approach towards QSPR modeling in polychlorobiphenyls

被引：3

作者：

Poddar, Arpita ^{[1
]}

Pal, Ranita ^{[2
]}

Rong, Chunying ^{[3
]}

Chattaraj, Pratim Kumar ^{[1
]}

机构：

[1] Indian Inst Technol Kharagpur, Dept Chem, Kharagpur 721302, India

[2] Indian Inst Technol Kharagpur, Adv Technol Dev Ctr, Kharagpur 721302, India

[3] Hunan Normal Univ, Coll Chem & Chem Engn, Changsha 410081, Peoples R China

来源：

JOURNAL OF MATHEMATICAL CHEMISTRY | 2023年 / 61卷 / 05期

关键词：

Quantitative structure-property relationship; Conceptual density functional theory; Information theory; Multilinear regression; POLYCHLORINATED-BIPHENYLS; CHEMICAL-REACTIVITY; FUNCTIONAL THEORY; DENSITY; TOXICITY; ELECTROPHILICITY; DESCRIPTORS; PHILICITY;

D O I：

10.1007/s10910-023-01457-9

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

Quantitative structure-property relationship (QSPR) of various chlorine substituted biphenyl systems on the basis of linear and multi-linear regression (MLR) analysis is presented in this study. The determination of lipophilicity (log K-OW) of the selected 133 polychlorobiphenyl (PCB) congeners is carried out taking the experimental log K-OW as the dependent variable and the conceptual density functional theory (CDFT) and information theory (IT) based descriptors (global electrophilicity index (omega), its square term (omega(2)), and Shannon entropy (S-S), GBP entropy (S-GBP)) as independent variables. These are used to map the relationship between experimental log K-OW and predicted log K-OW. The best model is obtained using CDFT descriptor (omega) along with IT quantities (S-S, S-GBP) when combined linearly. The results show a very good coefficient of determination value (R-2 = 0.9261) along with a high internal predicting ability (R-CV(2) = 0.9208) which indicates the importance of the mentioned descriptors for the quantitative structure-property analysis of selected PCBs.

引用

页码：1143 / 1164

页数：22

共 50 条

[21] INFORMATION-THEORETIC APPROACH TO UNIMODAL DENSITY-ESTIMATION [J].

BROCKETT, PL ;

CHARNES, A ;

PAICK, KH .

IEEE TRANSACTIONS ON INFORMATION THEORY, 1995, 41 (03) :824-829

[22] Finding the number of clusters in a dataset: An information-theoretic approach [J].

Sugar, CA ;

James, GM .

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2003, 98 (463) :750-763

[23] An information-theoretic approach for heterogeneous differentiable causal discovery [J].

Zhou, Wanqi ;

Bai, Shuanghao ;

Xie, Yuqing ;

He, Yicong ;

Zhao, Qibin ;

Chen, Badong .

NEURAL NETWORKS, 2025, 188

[24] Information-Theoretic Approach in Density Functional Reactivity Theory [J].

Liu Shu-Bin .

ACTA PHYSICO-CHIMICA SINICA, 2016, 32 (01) :98-118

[25] Information-Theoretic Approach to Strategic Communication as a Hierarchical Game [J].

Akyol, Emrah ;

Langbort, Cedric ;

Basar, Tamer .

PROCEEDINGS OF THE IEEE, 2017, 105 (02) :205-218

[26] Privacy-Preserving OLAP: An Information-Theoretic Approach [J].

Zhang, Nan ;

Zhao, Wei .

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2011, 23 (01) :122-138

[27] An algorithmic and information-theoretic approach to multimetric index construction [J].

Schoolmaster, Donald R., Jr. ;

Grace, James B. ;

Schweiger, E. William ;

Guntenspergen, Glenn R. ;

Mitchell, Brian R. ;

Miller, Kathryn M. ;

Little, Amanda M. .

ECOLOGICAL INDICATORS, 2013, 26 :14-23

[28] Testing for nonlinearity of exchange rates: an information-theoretic approach [J].

Zhang, Yuqin ;

Soofi, Abdol S. ;

Wang, Shouyang .

JOURNAL OF ECONOMIC STUDIES, 2011, 38 (06) :637-657

[29] An information-theoretic approach to estimate the capacity of processing units [J].

Ryabko, Boris .

PERFORMANCE EVALUATION, 2012, 69 (06) :267-273

[30] Data modeling in machine learning based on information-theoretic measures [J].

Liu, YH ;

Li, AJ ;

Luo, SW .

2002 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-4, PROCEEDINGS, 2002, :1219-1222

← 1 2 3 4 5 →