Current Computer-Aided Drug DesignCurrent Computer-Aided Drug DesignCurrent Computer-Aided Drug Design1573-40991875-6697Bentham Science64392110.2174/1573409919666230417080832ChemistryResearch ArticleScreening of Inhibitors against Idiopathic Pulmonary Fibrosis: Few-shot Machine Learning and Molecule Docking based Drug RepurposingChangJuninfo@benthamscience.netZouShaoqinginfo@benthamscience.netXuSuboinfo@benthamscience.netXiaoYiweninfo@benthamscience.netZhuDuinfo@benthamscience.netCollege of Life Science, Jiangxi Science & Technology Normal University0102202420213414407012025Copyright ©; 2024, Bentham Science Publishers2024Bentham Science Publishershttps://journals.eco-vector.com/1573-4099/article/view/643921

Introduction:Idiopathic pulmonary fibrosis is a chronic progressive disorder and is diagnosed as post-COVID fibrosis. Idiopathic pulmonary fibrosis has no effective treatment because of the low therapeutic effects and side effects of currently available drugs.

Aim:The aim is to screen new inhibitors against idiopathic pulmonary fibrosis from traditional Chinese medicines.

Methods:Few-shot-based machine learning and molecule docking were used to predict the potential activities of candidates and calculate the ligand-receptor interactions. In vitro A549 cell model was taken to verify the effects of the selected leads on idiopathic pulmonary fibrosis.

Results:A logistic regression classifier model with an accuracy of 0.82 was built and, combined with molecule docking, used to predict the activities of candidates. 6 leads were finally screened out and 5 of them were in vitro experimentally verified as effective inhibitors against idiopathic pulmonary fibrosis.

Conclusion:Herbacetin, morusin, swertiamarin, vicenin-2, and vitexin were active inhibitors against idiopathic pulmonary fibrosis. Swertiamarin exhibited the highest anti-idiopathic pulmonary fibrosis effect and should be further in vivo investigated for its activity.

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