Modeling MEK4 Kinase Inhibitors through Perturbed Electrostatic Potential Charges

MEK4,
mitogen-activated protein kinase kinase 4, is overexpressed
and induces metastasis in advanced prostate cancer lesions. However,
the value of MEK4 as an oncology target has not been pharmacologically
validated because selective chemical probes targeting MEK4 have not
been developed. With advances in both computer and biological high-throughput
screening, selective chemical entities can be discovered. Structure-based
quantitative structure–activity relationship (QSAR) modeling
often fails to generate accurate models due to poor alignment of training
sets containing highly diverse compounds. Here we describe a highly
predictive, nonalignment based robust QSAR model based on a data set
of strikingly diverse MEK4 inhibitors. We computed the electrostatic
potential (ESP) charges using a density functional theory (DFT) formalism
of the donor and acceptor atoms of the ligands and hinge residues.
Novel descriptors were then generated from the perturbation of the
charge densities of the donor and acceptor atoms and were used to
model a diverse set of 84 compounds, from which we built a robust
predictive model.