MIA-QSAR evaluation of a series of sulfonylurea herbicides
2008
Bitencourt, Michelle | Freitas, Matheus P.
BACKGROUND: A series of sulfonylurea herbicides has been modelled using a 2D image-based QSAR approach known as MIA-QSAR (Multivariate Image Analysis applied to QSAR), and highly predictive models have been built.RESULTS: Two MIA-QSAR models were built, one group being divided into training and test sets, and the other composed of the entire series of compounds. Statistically significant MIA-QSAR models rendered high correlation coefficients of experimental versus fitted p$K_{\rm {i}}^{\rm {app}}$ (AHAS apparent inhibition constant) and satisfactory parameters of external validation and leave-one-out cross-validation. Comparison with the results obtained from classical 2D QSAR demonstrated some advantages of the modelling using MIA descriptors.CONCLUSION: Both MIA-QSAR models showed high predictive ability, comparable with that of a reference methodology based on 3D descriptors. The method is suggested as a suitable tool for predicting novel herbicides.
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