Ligand-based designing, in silico screening, and biological evaluation of new potent fructose-1,6-bisphosphatase (FBPase) inhibitors.

Fructose-1,6-bisphosphatase - hereafter abbreviated as FBPase has been recently implicated in diabetes prompting several attempts to discover and optimize new FBPase inhibitors. Toward this end we explored the pharmacophoric space of 136 FBPase inhibitors using three diverse sets of inhibitors. This identified 520 pharmacophores that were subsequently clustered into 104 groups. Cluster centers were evaluated by receiver operating characteristic (ROC) curves analysis and correlation with bioactivities of collected compounds. Pharmacophore model Hypo1/7 illustrated the best combination of classification power (ROC-AUC) and correlation with bioactivity. Two other pharmacophores (Hypo2/1 and Hypo2/6) were found to be mergeable and their combined model (Hypo2-1/2-6) illustrated excellent ROC performance. We employed Hypo1/7 and Hypo2-1/2-6 models to screen the National Cancer Institute (NCI) list of compounds. In silico mining identified 18 FBPase inhibitors out of which six were of sub-micromolar IC(50) values.