Artificial intelligence-guided discovery of fungal ergosterol derivatives as selective LXRβ agonists targeting the cholesterol dependency of glioblastoma.

Authors

Qi Li, Chunxue Zhang, Tiantian Hu, Zhenzhen Zhang, Haigang Wu

Published in

Molecular diversity. Sep 12, 2026. Epub Sep 12, 2026.

Abstract

Glioblastoma (GBM) is the most lethal primary brain malignancy, for which no new systemic therapy has achieved regulatory approval in over past decade. A molecular vulnerability of GBM is its comprehensive co-dependency on exogenous cholesterol to sustain oncogenic signaling and membrane biogenesis, rendering the liver X receptor beta (LXRβ)-the principal transcriptional regulator of cholesterol efflux in the central nervous system-an attractive therapeutic target. However, the development of LXRβ-selective, blood-brain barrier (BBB)-penetrant agonists that avoid the hepatotoxicity associated with LXRα co-activation has remained an unresolved challenge. Here, we developed an integrated artificial intelligence-based virtual screening pipeline. The pipeline integrates ML-QSAR modelling, D-MPNN, and deep learning-based DTI prediction. It was applied to systematically interrogate a curated library of ~ 1.2 million natural products. Sequential filtering by predicted LXRβ affinity, LXRβ/LXRα selectivity, and integrated pharmacokinetic scoring, refined by molecular docking and MM-GBSA binding free energy calculations, identified four high-confidence candidates. Experimental validation by CCK-8 cytotoxicity assays across five GBM cell lines and six normal cell models revealed that 5,6-Epoxyergosterol is a potent anti-glioma agent. Furthermore, a 200 ns molecular dynamics simulation of the LXRβ-5,6-Epoxyergosterol complex demonstrated stable binding, with dominant hydrogen-bonding contacts at His435 and Trp443 and a free energy landscape consistent with a single dominant agonist-bound conformation. These findings establish fungal ergosterol derivatives as a novel class of LXRβ-targeted therapeutics and provide a validated AI-driven framework for accelerating natural product drug discovery in neuro-oncology.

PMID:
42732035
Bibliographic data and abstract were imported from PubMed on 13 Sep 2026.

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