ArticleBioinformatics and biology insights2026
In Silico Identification of Natural-Product Aromatase (CYP19A1) Inhibitors via Integrated Molecular Docking and QSAR Modelling.
Article in Bioinformatics and biology insights, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 authors.
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Abstract
Aromatase (CYP19A1) is the rate-limiting enzyme in estrogen biosynthesis and the principal therapeutic target for estrogen receptor-positive (ER+) breast cancer. Despite the clinical success of third-generation aromatase inhibitors (AIs) such as letrozole and anastrozole, acquired resistance and systemic adverse effects, including musculoskeletal pain, bone density loss, and cardiovascular complications, which necessitate the identification of novel scaffolds. This study employs an integrated computational pipeline combining ligand-based quantitative structure-activity relationship (QSAR) modelling and structure-based molecular docking to screen the AfroDB and EANPDB natural product libraries for putative aromatase inhibitors. A curated dataset of 3,068 unique compounds derived from ChEMBL (v33) was used to train Random Forest models, with the binary classification model achieving an area under the receiver operating characteristic curve (AUC) of 0.95 on the independent holdout set, an accuracy of 0.90, and a Matthews correlation coefficient (MCC) of 0.76. The regression model for pIC
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