These modules are designed as independent AI research workbenches. Each entry reserves a future connection point for the production agent, data pipeline, or external analysis service.
TCM Research Design Agent
Builds hypothesis trees, decomposes research tasks, and generates executable SOPs with study design, statistical methods, figure placeholders, and risk assessment.
Compound Formula Synergy and Optimization
Models multi-ingredient perturbation, synergy, antagonism, dose-time phenotype trajectories, and TCM efficacy proxy indicators for formula analysis.
Generative Formula Optimization with PBPK Simulation
Generates candidate formulas under compatibility constraints and simulates exposure, efficacy, dose adjustment, and PK interaction across virtual populations.
Synthetic Biology Simulation for Active Compounds
Designs microbial chassis engineering paths for scarce active natural products through biosynthetic retrosynthesis, enzyme mining, and flux balance analysis.
Multimodal TCM Quality Analysis
Fuses spectrum, image, HPLC fingerprint, processing records, and origin metadata to evaluate batch consistency and processing-related quality shifts.
Medicinal and Edible Product Development Agent
Develops functional food concepts for medicinal-edible materials, including evidence chains, population targeting, sensory prediction, process optimization, and compliance checks.
TCM Biological Function Enrichment Analysis
Integrates GO, KEGG, Reactome, WikiPathways, and cross-omics evidence for compound-target and formula-level functional enrichment with TCM-specific background correction.
Real-World TCM Efficacy Mining Agent
Extracts and standardizes EMR, HIS, insurance, and follow-up data, then applies causal inference to quantify real-world efficacy and patient benefit heterogeneity.
Herb-Drug Interaction and Pharmacovigilance Agent
Combines mechanism prediction and real-world adverse event mining to evaluate herb-drug interactions, clinical safety guidance, and pharmacovigilance signals.
TCM Drug Repositioning Agent
Discovers new indications through knowledge graph multi-hop reasoning, literature evidence synthesis, causal chain generation, and real-world evidence validation.
