Prediction of aquatic toxicity: Use of optimization of correlation weights of local graph invariants

Andrey Andreevich Toropov, Terry Wayne Schultz

Research output: Contribution to journalArticlepeer-review

Abstract

Quantitative structure-activity relationships (QSARs) were developed for three sets of toxicity data. Chemicals in each set represented a number of narcoses and electrophilic mechanisms of toxic action. A series of quantitative structure-toxicity models correlating toxic potency with a number of optimization of correlation weights of local graph invariants were developed. In the case of the toxicity of a heterogeneous set of benzene derivatives to Tetrahymena pyriformis, the QSARs were based on the Descriptor of Correlation Weights (DCW) using atoms and extended connectivity (EC) graph invariants. The model [log (IGC50 -1 = 0.0813 DCW(ak,3ECk) + 2.636; n = 157, r2 = 0.883, s = 0.27, F = 1170, Pr > F = 0.0001] based on third-order EC of 89 descriptors was observed to be best for the benzene data. However, fits for these data of > 0.800 were achieved ECs with as few as 23 variables. The relationship between the toxicity predicted by this model and experimental toxicity values for the test set [obs. log(IGC50 -1)) = 0.991 (pred. (log(IGC50 -1)) - 0.012; n = 60, r2 = 0.863, s = 0.28, F = 372, Pr > F = 0.0001] is excellent. The utility of the approach was demonstrated by the model [log (IGC50 -1) = 0.1744(DCW (ak, 2EC) - 3.505; n = 39, r2 = 0.900, s = 0.35, F = 333, Pr > F = 0.0001] for the toxicity data for T. pyriformis exposed to halo-substituted aliphatic compounds and the model [log (IC50 -1) = 0.1699(DCW (ak, 2EC)) - 2.610; n = 66, r2 = 0.901, s = 0.31,F = 583, Pr > F = 0.0001] for the Vibrio fischen toxicity data.

Original languageEnglish
Pages (from-to)560-567
Number of pages8
JournalJournal of Chemical Information and Computer Sciences
Volume43
Issue number2
DOIs
Publication statusPublished - Mar 2003

ASJC Scopus subject areas

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

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