Artificial Intelligence and Network Pharmacology Reveal the Medication Rules of Professor Wang Yu-Ying in the Treatment of Climacteric Syndrome

Abstract

Abstract Objective: The objective of this study was to explore the medication rules and academic ideas of Professor Wang Yu-Ying in the treatment of climacteric syndrome (CLS) and to predict new prescriptions. Materials and Methods: The characteristics of frequency, clustering, four properties, and five flavors were analyzed, and new prescriptions were predicted through an artificial intelligence (AI)-based method. The potential pathways of new prescriptions were explored through network pharmacology-based analysis. Results: The top 16 medicinals used by Professor Wang Yu-Ying in the treatment of CLS included Danggui, Longgu, Muli, Fuling, Chuanxiong, Gancao, Xiangfu, and Tusizi. The AI method was applied to predict the basic prescription for treating CLS: Danggui 15 g, Duanlonggu 30 g, Duanmuli 30 g, Fuling 28 g, Chuanxiong 10 g, Gancao 6 g, Xiangfu 12 g, Tusizi 14 g, etc., Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses showed that the pathogenesis of CLS might be related to the estrogen pathway, involving typical steroid responses. Conclusions: This study summarized Professor Wang’s medication experience in the treatment of CLS based on the data mining of clinical diagnoses and treatment cases. The AI method was used to predict the new prescription of CLS treatment, which was found to be reasonable by network pharmacology studies on its multi-target and multi-pathway mechanisms.