科研之友
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摘要
Experimental EC(50)s for 202 human beta(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 beta(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 beta(3)-AR agonists before chemical synthesis in drug development.
