Integrated Virtual Screening Approach Identifies New CYP19A1 Inhibitors

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 <b>9</b>, a novel, noncovalent inhibitor based on coumarin and imidazole substructures, stood out by its high potency, with an IC<sub>50</sub> value of 271 ± 51 nM.

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2025-03-19
DOI
https://doi.org/10.1021/acs.jcim.5c00204
Citations
2
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
3.01

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article

Integrated Virtual Screening Approach Identifies New CYP19A1 Inhibitors

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2 citations
Journal of Chemical Information and Modeling
Computational Drug Discovery Methods
3.01
article

Integrated Virtual Screening Approach Identifies New CYP19A1 Inhibitors

Mario Andrea Marchisio, Matthias Bureik, Jie Wu, Ya Chen, Clemens Alexander Wolf, Johannes Kirchmair, Gerhard Wolber, Sijie Liu
article en
2 citations

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 <b>9</b>, a novel, noncovalent inhibitor based on coumarin and imidazole substructures, stood out by its high potency, with an IC<sub>50</sub> value of 271 ± 51 nM.

Journal of Chemical Information and ModelingVol. 65(7)
University of Vienna (AT), Tianjin University (CN), Freie Universität Berlin (DE)
China Scholarship Council
Openalex Percentile: Top 10%
Computational Drug Discovery Methods
3.01
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