Visually Interpretable Models of Kinase Selectivity Related Features Derived from Field-Based Proteochemometrics

Achieving selectivity for small organic
molecules toward biological
targets is a main focus of drug discovery but has been proven difficult,
for example, for kinases because of the high similarity of their ATP
binding pockets. To support the design of more selective inhibitors
with fewer side effects or with altered target profiles for improved
efficacy, we developed a method combining ligand- and receptor-based
information. Conventional QSAR models enable one to study the interactions
of multiple ligands toward a single protein target, but in order to
understand the interactions between multiple ligands and multiple
proteins, we have used proteochemometrics, a multivariate statistics
method that aims to combine and correlate both ligand and protein
descriptions with affinity to receptors. The superimposed binding
sites of 50 unique kinases were described by molecular interaction
fields derived from knowledge-based potentials and Schrödinger’s
WaterMap software. Eighty ligands were described by Mold2, Open Babel, and Volsurf descriptors. Partial least-squares regression
including cross-terms, which describe the selectivity, was used for
model building. This combination of methods allows interpretation
and easy visualization of the models within the context of ligand
binding pockets, which can be translated readily into the design of
novel inhibitors.