Ecotoxicity prediction by adaptive fuzzy partitioning: comparing descriptors computed on 2D and 3D structures

Abstract

Classification models were established on four endpoints, i.e. trout, daphnia, quail and bee, including from 100 to 300 pesticides subdivided into 3 toxicity classes. For each species, five separate sets of molecular descriptors, computed by several software, were compared, including parameters related to 2D or 3D structures. The most relevant descriptors were selected with help of a procedure based on genetic algorithms. Then, structure-activity relationships were built by Adaptive Fuzzy Partition (AFP), a recursive partitioning method derived from Fuzzy Logic concepts.

Globally, satisfactory results were obtained for each animal species. The best cross-validation and test set scores reached values of about 70–75%. More important, the relationships derived from the descriptors calculated from 2D structures were superior or similar to those computed from 3D structures. These results underline that the long computational time employed to compute 3D descriptors is often useless to improve the prediction ability of the ecotoxicity models. Finally, the differences in the prediction ability between the different software used were quite reduced and show the possibility to use different descriptor packages for obtaining similar satisfactory models.

†Presented at CMTPI 2005: Computational Methods in Toxicology and Pharmacology Integrating Internet resources (Shanghai, China, October 29–November 1 2005).

Keywords:

• Ecotoxicity
• Fuzzy logic
• Classification models
• 2D and 3D descriptors

Acknowledgement

We acknowledge financial support from the European Commission for the DEMETRA project (Development of Environmental Modules for Evaluation of Toxicity of Pesticides Residues in Agriculture), contract QLK5-CT-2002-00691. Our thanks also go to all partners of DEMETRA for their collaboration in establishing the data sets, optimising the 3D structures and, more generally, for the helpful comments. Finally, we thank Prof. A. Katritzky (University of Florida) and Prof. M. Karelson (University of Tartu) for the use of CODESSA.

Notes

†Presented at CMTPI 2005: Computational Methods in Toxicology and Pharmacology Integrating Internet resources (Shanghai, China, October 29–November 1 2005).