Development of decision trees to discriminate HDAC8 inhibitors and non-inhibitors using recursive partitioning

Histone deacetylase 8 (HDAC8) is involved in malignancy. Overexpression of HDAC8 is correlated with various cancers. Design of selective HDAC8 inhibitors is always a challenging task to the chemistry audiences. In this communication, a diverse set comprising large number of compounds are subjected to recursive partitioning (RP) analysis to develop decision trees to discriminate compounds into HDAC8 inhibitors (active) and non-inhibitors (inactive). Acquiring knowledge about the essential structural and physicochemical parameters can be useful in designing potential and selective HDAC8 inhibitors. Moreover, this work validates our previous results observed in Bayesian modelling study of this dataset. This comparative learning will surely enrich drug discovery aspects related to HDAC8 inhibitors. Communicated by Ramaswamy H. Sarma

Automated summary

In this study, a diverse set of compounds were analyzed using recursive partitioning (RP) analysis to develop decision trees for discriminating HDAC8 inhibitors from non-inhibitors. Understanding essential structural and physicochemical parameters is crucial for designing potential and selective HDAC8 inhibitors, and the results validate previous findings from Bayesian modeling. This comparative learning will enhance drug discovery efforts related to HDAC8 inhibitors.