Traditional Chinese Medicine prescription recommendation for Alzheimer’s disease based on network propagation and reinforcement learning

As the global population ages, the prevalence of Alzheimer’s disease (AD) has been steadily increasing. Traditional Chinese medicine, with abundant ingredients and multi-targeting, has shown promising anti-AD effects. However, the complex mechanism of herbal actions makes it challenging to discover effective herbal prescriptions for AD. In this study, we propose an herbal prescription recommendation approach for AD based on network propagation and reinforcement learning. A target-ingredient-herb network is constructed, and network propagation is used to obtain the network score (Nscore) of an herb. The empirical score (Escore) of the herb pair is calculated based on known prescriptions. The herbal prescription score (HPscore) is then calculated based on Nscore and Escore. Finally, reinforcement learning is combined with HPscore to infer effective prescriptions. By validating the predicted prescriptions, we identify the targets of the herbs and AD-related proteins from the database, refining these results through cross-analysis. Core targets are determined using protein-protein interaction network analysis. GO and KEGG analyses explore biological roles of core targets. Molecular docking confirms interactions between actives and targets. Results show optimal herbal formulations act effectively on targets. These may offer strategies to optimize prescriptions, therapies, and drug development.