Knowledge graph reasoning
Map relationships between materia medica, bioactive compounds, protein targets, pathways, and disease phenotypes.
Systems pharmacology
Project / 01
AI in TCM Formula Discovery
Connect traditional formulation knowledge with modern molecular evidence to reveal testable therapeutic hypotheses.
Systems pharmacology
EPM / 01
01 Purpose
Make centuries of formulation knowledge computationally legible without flattening its systems-level complexity.
A research environment for exploring Traditional Chinese Medicine formulas across herbs, compounds, targets, pathways, and phenotypes. The system helps teams move from complex historical knowledge to ranked, evidence-linked candidates for experimental validation.
Prioritize formulas and constituent combinations for validation
Surface multi-target mechanisms that single-compound screens may miss
Unify traditional knowledge, literature, and omics evidence
02 Platform capabilities
Designed around
the decision.
Purpose-built intelligence for the workflow, evidence, and constraints of this research domain.
Formula comparison
Compare candidate formulas by predicted mechanisms, overlap, novelty, and supporting evidence.
Evidence traceability
Follow every generated hypothesis back to its source data and reasoning path.
03 Research workflow
1Ingest evidence
2Build graph
3Reason across systems
4Rank hypotheses
04 Applications
Where the platform
creates leverage.
Indication expansion
Identify new disease contexts for known formulas through pathway-level similarity.
Mechanism exploration
Generate interpretable models of synergistic and complementary activity.
Candidate refinement
Rank formula variants before committing resources to wet-lab studies.
05 For research groups and teams
Running this across a lab, a department, or a pipeline?
Anyone can create an account and start using the tool today. Talk to us about team seats, bulk credits, private datasets, or integrating the platform with your own infrastructure.