Description of the Electronic Structure of Organic Chemicals Using Semiempirical and Ab Initio Methods for Development of Toxicological QSARs

Abstract

The quality of quantitative structure-activity relationship (QSAR) models depends on the quality of their constitutive elements including the biological activity, statistical procedure applied, and the physicochemical and structural descriptors. The aim of this study was to assess the comparative use of ab initio and semiempirical quantum chemical calculations for the development of toxicological QSARs applied to a large and chemically diverse data set. A heterogeneous collection of 568 organic compounds with 96 h acute toxicity measured to the fish fathead minnow (Pimephales promelas) was utilized. A total of 162 descriptors were calculated using the semiempirical AM1 Hamiltonian, and 121 descriptors were compiled using an ab initio (B3LYP/6-31G**) method. The QSARs were derived using multiple linear regression (MLR) and partial least squares (PLS) analyses. Statistically similar models were obtained using AM1 and B3LYP calculated descriptors supported by the use of the logarithm of the octanol-water partition coefficient (log K(ow)). The main difference between the models derived by both MLR and PLS with the two sets of quantum chemical descriptors was concentrated on the type of descriptors selected. It was concluded that for large-scale predictions, irrespective of the mechanism of toxic action, the use of precise but time-consuming ab initio methods does not offer considerable advantage compared to the semiempirical calculations and could be avoided.

DOI: 10.1021/ci049747p

Extracted Key Phrases

11 Figures and Tables

Showing 1-10 of 31 references

A framework for promoting the acceptance and regulatory use of (quantitative) structureactivity relationships

  • A P Worth, M T D Cronin, C J Van Leeuwen, D J Livingstone
  • 2004

Quantum chemical descriptors in structure-activity relationships -calculation, interpretation and comparison of methods

  • G Schüürmann
  • 2004

QSARs: EValuation of the Commercially AVailable Software for Human Health and EnVironmental Endpoints with Respect to Chemical Management Applications

  • 2003