A neural network-based prediction model of AR inhibitory activity from a sparse set of compounds

In this paper, we present a mechanism to obtain a neural network-based model that predicts an enzyme inhibitory activity of a group of compounds. The mechanism selects the compounds, among a sparse set of, that should be used to obtain models of the inhibitory activity of interest. That is, the mechanism is aimed at the selection of a training set of compounds which ensures that the training of a neural network-based model results in a system capable of generalization.