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    Home » News » Machine learning identifies natural compounds to treat lung diseases
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    Machine learning identifies natural compounds to treat lung diseases

    healthadminBy healthadminJune 16, 2026No Comments3 Mins Read
    Machine learning identifies natural compounds to treat lung diseases
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    New research published in engineering combined machine learning (ML) and experimental validation to identify the natural flavonoid dihydromyricetin (DHM) as a potent inhibitor of the TGF-β/ALK5 signaling cascade with therapeutic potential in idiopathic pulmonary fibrosis (IPF). IPF is a chronic, progressive, life-threatening lung disease characterized by diffuse alveolitis, structural destruction, and excessive extracellular matrix deposition, with a median survival of only 3 to 5 years. Current drug treatments, including pirfenidone and nintedanib, only slightly slow disease progression and cannot halt or reverse fibrosis, creating an urgent need for new therapeutic approaches.

    In this study, researchers built a fine-tuned ML classification model from the Uni-Mol framework to predict inhibitors of the canonical TGF-β/SMAD pathway from natural products. The model was trained and validated using affinity data from BindingDB and externally tested using ChEMBL compounds, achieving strong performance with an area under the precision recall curve (AUPRC) of 0.936 and an area under the receiver operating characteristic curve (AUROC) of 0.902. The research team screened a library of over 16,700 herbal natural products and predicted 408 potential inhibitors, from which they selected 20 for experimental confirmation. Using a dual-luciferase reporter assay, DHM emerged as the most potent candidate to suppress the TGF-β/SMAD signaling cascade.

    in vitro Experiments showed that DHM suppressed TGF-β1-induced epithelial-to-mesenchymal transition (EMT) in A549 cells and fibroblast transdifferentiation in MRC-5 cells, while also decreasing cell migration induced by TGF-β1. In a bleomycin-induced pulmonary fibrosis mouse model, DHM attenuated fibrotic lesions and inflammatory responses, improved respiratory function indicators, and reduced collagen accumulation and hydroxyproline levels in lung tissue. Mechanistic studies have revealed that DHM targets the type I TGF-β receptor, also known as ALK5, directly binds to the receptor’s kinase domain, reduces its membrane expression, suppresses its kinase activity, and causes downregulation of both SMAD-dependent and non-canonical TGF-β signaling pathways.

    This study is the first to report DHM as a TGF-β/SMAD pathway inhibitor identified by ML and with demonstrated efficacy against IPF. The antifibrotic activity of DHM is mediated by ALK5 blockade, which suppresses downstream signaling, EMT, and fibroblast activation. Compared with currently available drugs, DHM exhibits a favorable safety profile and good water solubility, supporting its promise as a candidate for anti-IPF drug development. These findings also highlight the value of integrating computational and experimental methods in discovering natural product-based treatments for complex lung diseases.

    sauce:

    Reference magazines:

    Dong, L. others. (2025). Machine learning-powered insights: A novel role for dihydromyricetin in inhibiting the TGF-β/ALK5 signaling cascade in the treatment of pulmonary fibrosis. engineering. DOI: 10.1016/j.eng.2025.10.017. https://www.sciencedirect.com/science/article/pii/S2095809925006496?via%3Dihub



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