Discovery of new β-D-galactosidase inhibitors via pharmacophore modeling and QSAR analysis followed by in silico screening

Glycosidases, including β‐D‐galactosidase, are involved in a variety of metabolic disorders, such as diabetes, viral or bacterial infections, and cancer. Accordingly, we were prompted to find new β‐D‐galactosidase inhibitors. Towards this end, we scanned the pharmacophoric space of this enzyme using a set of 41 known inhibitors. Genetic algorithm and multiple linear regression analyses were used to select an optimal combination of pharmacophoric models and physicochemical descriptors to yield self‐consistent and predictive quantitative structure‐activity relationship (QSAR). Five pharmacophores emerged in the QSAR equations suggesting the existence of more than one binding mode accessible to ligands within β‐D‐galactosidase pocket. The successful pharmacophores were complemented with strict shape constraints in an attempt to optimize their receiver‐operating characteristic curve profiles. The validity of the QSAR equations and the associated pharmacophoric models were experimentally established by the identification of several β‐D‐galactosidase inhibitors retrieved via in silico search of two structural databases: the National Cancer Institute list of compounds and our in house built structural database of established drugs and agrochemicals. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2011