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.
The activities of a series of benzothiazole derivatives, some Candida albicans N-myristoyltransferase (Nmt) inhibitors, were modeled through MIA-QSAR (multivariate image analysis applied to quantitative structure-activity relationship) by using two different regression methods: N-PLS, applied to the three-way array, and PLS, applied to the unfolded array. Both models demonstrated excellent predictive ability, with results comparable to those obtained through 3D approaches. In order to compare the results obtained through MIA descriptors with the predictions of a classical 2D QSAR, some representative physicochemical descriptors were calculated and regressed against the experimental pIC50 values through multiple linear regression, demonstrating that MIA-QSAR was superior for this series of compounds.
This paper describes the use of the multivariate image analysis-quantitative structure-activity relationship (MIA-QSAR) methodology, which is a method capable of relating chemical structures with their biological activities. This in silico method has been shown to be extremely reliable and provides excellent results, even compared to those obtained by costly and time-consuming methods. Here, a class of acetylcholinesterase inhibitors was studied and three new compounds exhibiting high estimated pIC(50) values were found. They may be promising inhibitors for use in the treatment of Alzheimer's disease.
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