Structural similarity-based prediction of the potential active ingredients and mechanism of action of traditional Chinese medicine formulations used to anti-aging

摘要

Objective:To predict the potential active ingredients (PAIs) and mechanism of action of traditional Chinese medicine formulations used to delay aging.Methods:We incorporated the use of quantum-chemistry calculations and machine learning to predict the active ingredients of some Chinese herbal medicines used to delay aging.Then,a network-pharmacology approach was used to uncover how these PAls delayed aging.Results:Twelve PAls with anti-aging effects were discovered:androsterone,MHP,cortisone,propyl methyl trisulfide,retinol,retinal,cortisol,11-cis-Retinol (2R,3R)-3-hydroxyproline,4,5alpha-Dihydrocortisone (2S)-2-ammonio-6-ureidohexanoate and17alpha,21-Dihydroxy-5beta-pregnane-3,11,20-trione.Enrichment analyses indicated that a putative compound target and aging target were significantly associated with:regulation of the immune system;insulin receptor signaling pathway;regulation of the mitotic cell cycle;response to nutrient levels;response to oxidative stress;release of cytochrome c from mitochondria;learning or memory;inflammatory response.Conclusions:A novel method was proposed to predict the PAls of anti-aging herbal medicines by incorporating quantum-chemistry calculations and machine learning.Then,a network-pharmacology approach was used to uncover how these PAls delay aging.The information provided by our study on PAls may aid the discovery of anti-aging drugs.