Abstract Fangjiomics is a promising paradigm that enhances research on multi-omics-based pharmacological mechanisms of Fangji from holistic and systematic perspective. We reviewed recent advances in Fangjiomics, focusing on database and analysis platform development, methodological innovations, and translational applications. Through the integration of Fangji and multi-omics data, multi-level system analysis approaches were developed, encompassing single-target analysis, signaling pathways, multi-targeted network and modules. Fangjiomics has emerged as a key strategy in various areas of Fangji research. To support the high quality development of Fangjiomics, we propose principles and perspectives from the integrated, macro-level, and practical viewpoints.

ObjectiveTo support intelligent clinical decision-making in traditional Chinese medicine（TCM）， this study utilized ontology and knowledge graph construction techniques to achieve the IT application of clinical practice guidelines. MethodBased on the principles of findability， accessibility， interoperability， and reusability （FAIR principles）， this study employed ontology techniques to construct an ontology for TCM clinical practice guidelines and built a knowledge graph using coronary heart disease as an example. Based on the Checklist for Reporting Practice Guidelines in Traditional Chinese Medicine and Recommendation Grading in TCM Clinical Guidelines/Consensus （T/CAS 530—2021），the ontology of TCM clinical practice guidelines was constructed using the seven-step ontology construction method. On this basis，the TCM diagnosis and treatment data from the Guidelines for the Diagnosis and Treatment of Stable Angina Pectoris in Coronary Heart Disease were stored in Neo4j in the form of triples through knowledge extraction，integration，and storage. ResultThe information in the clinical practice guidelines was divided into three categories： onset and prevention information， diagnosis information， and treatment information， and the TCM clinical practice guideline ontology was constructed. A total of 27 concepts related to TCM clinical diagnosis and treatment and 14 data attributes were obtained， and 12 conceptual relationships including hierarchical relationships and object attributes were established. By taking coronary heart disease as an example and the TCM clinical practice guideline ontology as the model layer， the knowledge map of TCM diagnosis and treatment guidelines for stable angina pectoris in coronary heart disease with 276 nodes and 336 relationships was constructed， realizing the visual display and query of the guideline content. ConclusionThe ontology of TCM clinical practice guidelines and the knowledge graph of stable angina pectoris in coronary heart disease constructed by combining the seven-step ontology construction method and Neo4j graph database technology are efficient and flexible，providing an intelligent TCM diagnosis and treatment scheme and promoting the standardization and objectification of TCM diagnosis and treatment.

ObjectiveTo explore the efficacy-driving mechanism of Danhong injection （DHI） in the treatment of stable angina pectoris （SAP） based on the composition-activity relationship of target modules and clarify the pharmacological effects of DHI. MethodAccording to the angina frequency （AF） in the Seattle Angina Questionnaire （SAQ） that was obtained in the previous clinical trial， the patients before and after DHI treatment were grouped based on efficacy. The transcriptomic data of the patients before treatment and in the best efficacy group 30 days post-treatment were selected as the data source， and then weighted gene co-expression network analysis （WGCNA） was employed to construct the co-expression network. Relevant modules in the network were identified and associated with clinical features. In addition， the On-modules （Z value below 0） were identified by Zsummary. The topological indicators such as density， centrality， and clustering coefficient were adopted to explore the dynamics of DHI efficacy at the network level and module level， respectively. In addition， the driver genes were screened by the personalized network control （PNC） algorithm. Finally， rat H9C2 cells were used to establish the model of hypoxia/reoxygenation （H/R）， which was used to confirm the potential therapeutic target of DHI for SAP and provide a scientific basis for revealing the therapeutic mechanism of DHI. ResultWe identified 19 modules in the best efficacy group of DHI for SAP， and the comparison between day 0 and day 30 revealed 12 On-modules. The changes of network topological indicators at the network and module levels confirmed the correlation between the best efficacy of DHI treatment and topological dynamics. Finally， the driver genes， Klotho and fibroblast growth factor 22 （FGF22）， in DHI treatment of SAP were verified by the H9C2 cell model of H/R. ConclusionBased on clinical transcriptome data， this study determined the composition-activity relationship of target modules of DHI for SAP， which provided a scientific basis for deciphering the efficacy-driven mechanism of DHI for SAP.

ObjectiveThis study aims to develop a prediction model for the compatibility of Chinese medicinal pairs based on Graph Convolutional Networks （GCN）， named HC-GCN. The model integrates the properties of herbs with modern pharmacological mechanisms to predict pairs with specific therapeutic effects. It serves as a demonstration by applying the model to predict and validate the efficacy of blood-activating herb pairs. MethodsThe training dataset for herb pair prediction was constructed by systematically collecting commonly used herb pairs along with their characteristic data， including Qi， flavor， meridian tropism， and target genes. Integrating traditional characteristics of herb with modern bioinformatics， we developed an efficacy-oriented herb pair compatibility prediction model （HC-GCN） using graph convolutional networks （GCN）. This model leverages machine learning to capture the complex relationships in herb pair compatibility， weighted by efficacy features. The performance of the HC-GCN model was evaluated using accuracy （ACC）， recall， precision， F1 score （F1）， and area under the ROC curve （AUC）. Its predictive effectiveness was then compared to five other machine learning models： eXtreme Gradient Boosting （XGBoost）， logistic regression （LR）， Naive Bayes， K-nearest neighbor （KNN）， and support vector machine （SVM）. ResultsUsing herb pairs with blood-activating effects as a demonstration， a prediction model was constructed based on a foundational dataset of 46 blood-activating herb pairs， incorporating their Qi， flavor， meridian tropism， and target gene characteristics. The HC-GCN model outperforms other commonly used machine learning models in key performance metrics， including ACC， recall， precision， F1 score， and AUC. Through the predictive analysis of the HC-GCN model， 60 herb pairs with blood-activating effects were successfully identified. Among of these potential herb pairs， 44 include at least one herb with blood-activating effects. ConclusionIn this study， we established an efficacy-oriented compatibility prediction model for herb pairs based on GCN by integrating the unique characteristics of traditional herbs with modern pharmacological mechanisms. This model demonstrated high predictive performance， offering a novel approach for the intelligent screening and optimization of traditional Chinese medicine prescriptions， as well as their clinical applications.