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

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 (IGC(50)(-1)) = 0.0813 DCW(a(k),(3)EC(k)) + 2.636; n = 157, r(2) = 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(IGC(50)(-1))) = 0.991 (pred. (log(IGC(50)(-1))) - 0.012; n = 60, r(2) = 0.863, s = 0.28, F = 372, Pr > F = 0.0001] is excellent. The utility of the approach was demonstrated by the model [log (IGC(50)(-1)) = 0.1744(DCW (a(k), (2)EC) - 3.505; n = 39, r(2) = 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 (IC(50)(-1)) = 0.1699(DCW (a(k), (2)EC)) - 2.610; n = 66, r(2) = 0.901, s = 0.31, F = 583, Pr > F = 0.0001] for the Vibrio fischeri toxicity data.