Predictive models for aquatic toxicity of aldehydes designed for various model chemistries

Martin Smiesko, Emilio Benfenati

Research output: Contribution to journalArticlepeer-review

Abstract

Predictive models for the aquatic toxicity of aldehydes were designed for a set of 50 aromatic or aliphatic compounds containing at least one aldehyde group, for which the acute toxicity data for the fathead minnow (Pimephales promelas) are available (96 h test assessing 50% lethal waterborne concentration). The molecular descriptors were based on calculations with various semiempirical or ab initio model chemistries. The resulting four-parameter models were evaluated according to the correlation coefficients R2. The best predictive model was obtained with the HF/STO-3G model chemistry (R2 = 0.868), while the models designed for descriptors based on ab initio calculations of higher level showed a slightly worse predictivity (the HF/3-21G(d) based model R2 = 0.800, the HF/6-31G(d) based model R2 = 0.808, the B3LYP/6-31G(d,p) based model R 2 = 0.812). With the semiempirical methods a good predictivity was observed with the PM3 based model (R2 = 0.811) and the AMI based model (R2 = 0.791), but the MNDO based model showed the worst predictivity (R2 = 0.760). In all ab initio models and the PM3 model very similar descriptors were involved. The importance of the descriptor logarithm of the partition coefficient logP for toxicity prediction was confirmed. Additionally, the descriptors encoding the negatively charged molecular surface area, hydrogen bonding molecular surface area, and reactivity of aldehyde group were identified as essential for the toxicity prediction of aldehydes.

Original languageEnglish
Pages (from-to)976-984
Number of pages9
JournalJournal of Chemical Information and Computer Sciences
Volume44
Issue number3
DOIs
Publication statusPublished - May 2004

ASJC Scopus subject areas

  • Chemistry(all)
  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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