Abstract
The set of 95 aromatic amines and their mutagenic potency was treated with counter propagation neural network, which enables analysis of self-organising maps (SOMs) and also the prediction of mutagenicity. Compounds were described with four classes of descriptors: topostructural (TS), topochemical (TC), geometrical, and quantum chemical (QC). The models were tested on their prediction ability with leave-one-out (LOO) cross-validation method. The squares of correlation coefficient lie between 0.65 and 0.75 and are comparable with models obtained by linear methods. In addition, we analysed self-organising maps and found clusters of structurally similar compounds.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 25-36 |
| Number of pages | 12 |
| Journal | Environmental Toxicology and Pharmacology |
| Volume | 16 |
| Issue number | 1-2 |
| DOIs | |
| State | Published - Mar 2004 |
Bibliographical note
Funding Information:MV gratefully thanks Ministry of Education, Science and Sport of the Republic of Slovenia that supports this work under contracts P1-034507 and SLO-US 021. This is contribution number 348 from the Center for Water and the Environment of the Natural Resources Research Institute. Research reported in this paper was supported in part by Grant F49620-02-1-0138 from the United States Air Force and Grant/Cooperative Agreement Number 572112 from the Agency for Toxic Substances and Disease Registry.
Keywords
- Aromatic amines
- Counter propagation neural network
- Hierarchical clustering
- Mutagenicity
- QSAR modelling
- Self-organising map