ArticleMolecular diversity2026
Machine learning-driven prioritization and experimental validation of traditional Chinese medicine-derived STING-inhibitory candidates.
Article in Molecular diversity, 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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8 authors.
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Abstract
The stimulator of interferon genes (STING) is a key signalling adaptor in the cGAS-STING pathway of the innate immune system and plays a significant role in autoimmune diseases, viral infections, and cancer, thus representing a promising target for small-molecule inhibitor therapies. This study presents an integrated multidimensional computer-aided drug design (CADD) approach that utilises machine learning (ML), molecular docking, molecular dynamics (MD) simulations, and ADMET prediction to efficiently prioritize new STING expression suppressor candidates from natural products. We developed a precise ML-based STING classification model with 90.2% accuracy and a robust STING inhibitor activity regression model demonstrating strong predictive capabilities, as evidenced by an R
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