Design and exploration of target-selective chemical space representations

We report the design of target-selective chemical spaces using CA-DynaMAD, a mapping algorithm that generates and navigates flexible space representations for the identification of active or selective compounds. The algorithm iteratively increases the dimensionality of reference spaces in a controlled manner by evaluating a single descriptor per iteration. For seven sets of closely related biogenic amine G protein coupled receptor (GPCR) antagonists with different selectivity, target-selective reference spaces were designed and used to identify selective compounds by screening a biologically annotated database. Combinations of descriptors that constitute target-selective reference spaces identified with CA-DynaMAD can also be used to build other computational models for the prediction of compound selectivity.