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

Abstract

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 PAIs delayed aging. Results: Twelve PAIs 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 PAIs of anti-aging herbal medicines by incorporating quantum-chemistry calculations and machine learning. Then, a network-pharmacology approach was used to uncover how these PAIs delay aging. The information provided by our study on PAIs may aid the discovery of anti-aging drugs. Keywords: Quantum chemistry calculations, Machine learning, Active ingredients, Mechanism, Aging