Network Analysis of Predicted Therapeutic Symptoms in National Health Insurance Herbal Prescriptions

Background: Predicting the molecular mechanisms and therapeutic effects of National Health Insurance herbal Prescriptions (NHPs) is challenging because of their multi-compound nature. We aimed to predict the therapeutic mechanisms of 56 NHPs using a Traditional Chinese Medicine (TCM) database and bioinformatic tools. Methods: We used a TCM database and bioinformatic techniques to construct networks of 56 NHPs. The network’s predicted potential therapeutic symptoms were compared with clinical study results and known indications to evaluate their validity. Finally, we identified the potential diseases and predicted the molecular mechanisms of 13 selected NHPs. Results: Of the initial 56 NHPs, 13 were selected for a detailed analysis. A five-layer network was constructed, which linked an average of 1,359 potential diseases per prescription. The concordance rate between network predictions and clinical papers was 45.6%, whereas that between network predictions and previously known major indications was 37.8%. Molecular mechanism analysis revealed that NHPs affect multiple pathways, including those related to metabolic diseases and nerve regulation. Conclusion: Our findings suggest that combining bioinformatics and clinical data can provide insights into the therapeutic effects of NHPs. This approach can guide future research by predicting potential therapeutic mechanisms and applications, providing insights into the therapeutic effects of NHPs.