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Updated: May 10, 2026

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
Published on: August 28, 2019
On the use of electronic descriptors for QSAR modelling of PCDDs, PCDFs and dioxin-like PCBs
M Larsson1, B Kumar Mishra, M Tysklind
1Department of Chemistry, Umeå University, SE-901 87 Umeå, Sweden.
Abstract:
The electronic properties of 29 polychlorinated dibenzo-p-dioxins and dibenzofurans and dioxin-like polychlorinated biphenyls that have been included in the toxic equivalency factor system have been investigated and used to derive quantum mechanical (QM) chemical descriptors for QSAR modelling. Their utility in this context was investigated alongside descriptors based on ultraviolet absorption data and traditional 2D descriptors including log K(ow), polarizability, molecular surface properties, van der Waals volume and selected connectivity indices. The QM descriptors were calculated using the semi-empirical AM1 method and the density functional theory method B3-LYP/6-31G**. Atom-specific and molecular quantum chemical descriptors were calculated to compare the electronic properties of dioxin-like compounds regardless of their chemical class, with particular emphasis on the lateral positions. Multivariate analysis revealed differences between the chemical classes in terms of their electronic properties and also highlighted differences between congeners. The results obtained demonstrated the importance of considering molecular orbital energies, but also indicated that the ratios of the coefficients of the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) at the lateral carbons were important. In addition, the digitalized UV spectra contained chemical information that provided crucial insights into dioxin-like activity.
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