Abstract
The human cytochrome P450 19A1 (CYP19A1, aromatase) is a heme-containing protein catalyzing the final steps of the biosynthesis of the steroid hormone 17β-estradiol. It is a key target for the treatment of sex-hormone-related disorders due to its role in mediating the conversion of androgens to estrogens. Here, we report the development of a virtual screening workflow incorporating machine learning and structure-based modeling that has led to the discovery of new CYP19A1 inhibitors. The machine learning models were built on comprehensive CYP19A1 data sets extracted from the ChEMBL and PubChem Bioassay databases and subjected to thorough validation routines. Ten promising hits that resulted from the virtual screening campaign were selected for experimental testing in an enzymatic assay based on heterologous expression of human CYP19A1 in yeast. Among the seven structurally diverse compounds identified as new CYP19A1 inhibitors, compound 9, a novel, noncovalent inhibitor based on coumarin and imidazole substructures, stood out by its high potency, with an IC50 value of 271 ± 51 nM.