Application of machine learning in predicting quorum sensing inhibition: integration of QSAR modeling, virtual screening, and lead compound development
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Từ khóa:
QS inhibition
Pseudomonas aeruginosa
Machine Learning
QSAR
Fingerprints
Adaboost
Docking
NAOMInext
Tóm tắt
Antibiotic resistance is a critical global health challenge, with Pseudomonas aeruginosa emerging as a formidable pathogen due to its reliance on quorum sensing (QS) for virulence and biofilm formation. This study presents an integrated computational pipeline combining machine learning (ML), molecular docking, and de novo drug design to identify novel QS inhibitors. A dataset of 1,983 compounds was used to train ML models, with the Transformer-CNN-Inception architecture achieving the highest predictive accuracy (90%, AUC = 0.96). Virtual screening against LasR, LuxR, and LuxI revealed 82 high-affinity ligands (binding energy ≤ -10 kcal/mol), outperforming erythromycin (-8.6 kcal/mol). Using NAOMInext, 105 derivatives were generated, with one lead compound exhibiting strong binding (-13.4 kcal/mol) and 92% predicted QS inhibition. ADMET profiling confirmed favorable drug-like properties, including low hepatotoxicity and high gastrointestinal absorption. This work demonstrates a robust framework for accelerating the discovery of anti-virulence agents, offering a promising strategy to combat antibiotic resistance.
Tài liệu tham khảo
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Oct 31, 2025
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Ly, T. N., và N. T. Huyen. “Application of Machine Learning in Predicting Quorum Sensing Inhibition: Integration of QSAR Modeling, Virtual Screening, and Lead Compound Development”. Tạp Chí Khoa học Và Công nghệ Đại học Đà Nẵng, vol 23, số p.h 10B, Tháng Mười 2025, tr 31-36, doi:10.31130/ud-jst.2025.23(10B).632E.
