AUTHOR=Qin Dan , Zhang He , Du Bin , Wang Hui , Liu Ligang , Wang Yun 

TITLE=Understanding the ancient classic and famous prescriptions via the property of Chinese materia medica

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1551531

DOI=10.3389/fphar.2025.1551531

ISSN=1663-9812

ABSTRACT=BackgroundAncient classic and famous prescriptions (ACFPs), derived from traditional Chinese medicine (TCM) classics, are widely utilized due to their precise therapeutic effects and distinctive clinical advantages. Existing research predominantly focuses on individual prescriptions, and there is lack of systematic exploration of medication patterns within the official ACFPs catalog. The property of Chinese materia medica (PCMM), a multidimensional representation of medicinal properties, offers a novel perspective for systematically analyzing TCM formulas.ObjectiveIn this study, we aim to investigate the implicit medication patterns of ACFPs from the PCMM perspective, establish a feature extraction model based on the property combination of Chinese materia medica (PCCMM), and evaluate its effectiveness in representing and reconstructing ACFPs.MethodsBased on the Chinese Pharmacopoeia (ChP), we constructed a CMM–PCCMM network as the forward feature extraction process. We formulated the backward process as a constrained combinatorial optimization problem to rebuild ACFPs from their PCCMMs. We evaluated the performance of PCCMM in reconstructing ACFPs using the Jaccard similarity coefficient. Furthermore, we tested the capability of PCCMM to distinguish ACFPs from random pseudo-formulas and classify ACFPs according to deficiency syndromes. Finally, we conducted frequency analysis, association rule analysis, distance analysis, and correlation analysis to explore the implicit medication patterns of ACFPs based on PCCMM.ResultsNumerical experiments showed that PCCMM effectively represented and reconstructed ACFPs, achieving an average Jaccard similarity coefficient above 0.8. PCCMM outperformed the nomenclature of CMM in distinguishing ACFPs from random pseudo-formulas and classifying deficiency syndromes. Frequency analysis revealed that high-frequency CMMs were mainly tonic medicines, whereas high-frequency PCCMMs predominantly mapped to the even–sweet–spleen meridian. The association rule analysis based on PCCMM yielded significantly more implicit compatibility rules than CMM alone. Distance and correlation analyses identified synergistic CMM pairs and PCCMM pairs, such as Jujubae Fructus (Dazao) and Zingiberis Rhizoma Recens (Shengjiang), which is consistent with clinical experience.ConclusionThe PCCMM-based feature extraction model provides a quasi-equivalent representation of TCM formulas, effectively capturing implicit medication patterns within ACFPs. PCCMM outperforms traditional CMM methods in formula reconstruction, classification, and medication pattern mining. This study offers novel insights and methodologies for systematically understanding TCM formulas, guiding clinical application, and facilitating the design and optimization of new TCM formulas.