Abstract
Here, we report on the development of a novel methodology to aid in design of Hsp90 inhibitors, using molecular docking combined with artificial neural network (ANN) modelling. Inhibitors are first docked into the ATPase site of the Human Hsp90α crystal structures and the thermodynamic properties of the complexes together with various physical–chemical properties of the ligands are used as input to train a simple feed-forward, back propagation ANN, to predict the inhibitors’ pIC50s. For an objective test set of 60 known Hsp90 inhibitors for which there are no crystallographic data available, the trained ANN is shown to give pIC50s accurate to within ±1 log unit, and the predictions are sufficiently good as to allow the majority of the inhibitors to be ranked correctly according to their potency.
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