Screening of Inhibitors Against Idiopathic Pulmonary Fibrosis: Few-Shot Machine Learning and Molecule Docking Based Drug Repurposing

• Published in: Current computer-aided drug design
• Main Authors: Chang, Jun, Zou, Shaoqing, Xu, Subo, Xiao, Yiwen, Zhu, Du
• Format: Journal Article
• Language: English
• Published: United Arab Emirates 01.01.2024
• Subjects: virtual screen; Few-shot machine learning; drug repurposing; idiopathic pulmonary fibrosis; molecule docking; traditional Chinese medicines
• ISSN: 1875-6697
• DOI: 10.2174/1573409919666230417080832

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