A classification study of human β₃-adrenergic receptor agonists using BCUT descriptors.

Experimental EC(50)s for 202 human β(3)-AR agonists are used to develop classification models as a potential screening tool for a large library of target compounds before synthesis. A variable selection approach from random forests (VS-RF) is used to extract the structural information most relevant to the human β(3)-AR activation properties of the collected data set. The obtained results indicate that the VS-RF method can be used for variable selection with smallest sets of non-redundant descriptors with highly predictive accuracy (Q (ex)% = 96% for the external prediction set). Thus, the proposed VS-RF models should be helpful for screening of potential human β(3)-AR agonists before chemical synthesis in drug development.