Nonlinear Quantitative Structure−Activity Relationship for the Inhibition of Dihydrofolate Reductase by Pyrimidines

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Abstract
A novel method for quantitative structure−activity relationship (QSAR) analysis is presented. The method, which does not assume any particular functional form for the QSAR, develops nonlinear relationships between parameters describing a set of molecules and the activity of the molecules. For the QSAR of the inhibition of Escherichia coli dihydrofolate reductase by 2,4-diamino-5-(substituted benzyl)pyrimidines, the method compares favorably to other nonlinear methods. Cross-validation trials demonstrate that the predictive ability is as accurate as other methods, and the method is simpler and faster than neural network and machine-learning methods. Consequently, its implementation is much easier, and interpretation of the generated QSAR is more straightforward.