100K+
Formula resources
Connect formulation experience with herbal composition to compare combinations and guide formula simplification and optimization.
VMED-OS / INTELLIGENCE MEETS EXPERIMENTAI insights.
AI insights.
Put to the clinical test.
VMED-OS is Veritas Medicine’s proprietary TCM AI model, supporting natural-origin original molecules, global botanical drugs and innovative TCM drugs. Knowledge reasoning connects candidate design with pharmacology, so experimental results inform each new research decision.
VMED-OS / TCM INTELLIGENCE AT SCALEMillion-scale molecules and evidence.
Million-scale molecules and evidence.
A data foundation for TCM AI at scale.
VMED-OS brings together 100K+ formula resources, 100K+ botanical resources, 1M+ natural product molecules and 1M+ clinical evidence items, alongside approximately 100M cumulative herb–disease associations. This breadth supports candidate search and combination research across multiple constituents, targets and herbal formulations, informing mechanistic analysis, formula optimization and experimental validation.
100K+
Botanical resources
Connect botanical origins, medicinal parts and constituent information to inform material selection and research into bioactive constituents.
1M+
Natural product molecules
Link natural constituents with biological targets to investigate active ingredients and mechanisms involving multiple constituents.
1M+
Clinical evidence items
Bring together clinical literature and trial registrations to assess human evidence and inform study populations and endpoints.
~100M
Herb–disease relationships
Connect botanicals and mechanistic signals around diseases to prioritize candidates and propose testable formulation hypotheses.
One AI platform. Three research directions.
01 Natural-origin original molecules
Investigate natural scaffolds and bioactivity with target matching, structural novelty and developability to guide screening and optimization.
Outputs: Molecular candidates, mechanistic hypotheses and hit-validation plans.
02 Global botanical drugs
Start with defined modern medical indications and connect targets, compounds and botanical materials to predict new candidates and prioritize pharmacology, safety and quality studies.
Outputs: Botanical candidates, prediction rationale and experimental screening plans.
03 Innovative TCM drugs
Support Class 1.1 formula design and optimization, Class 1.2 extract research and Class 1.3 new-material assessment, connecting TCM theory, human experience and experimental validation.
Outputs: Candidate strategies, research rationale and staged validation plans.
Computation and experiments.
One learning cycle.
VMED-OS, the proprietary TCM AI model developed by Veritas Medicine, connects knowledge graphs, semantic alignment and mechanistic reasoning with experiments. Predictions guide pharmacology studies; results update models and program decisions. For programs advancing into clinical research, human evidence further refines the strategy.
Connect knowledge
Align formulas, herbs, compounds, targets and indications with sources, directions of effect and evidence levels.
Reason with AI
Use knowledge graphs and mechanistic networks to propose candidates, active fractions and quality-marker hypotheses.
Test in the lab
Challenge hypotheses with chemical analysis, disease models and pharmacology endpoints, including counterevidence.
Learn from results
Feed dose, exposure, activity and quality findings back into candidate priorities and the next study plan.
Eight research areas
Supportive Care in Cancer
Cardiovascular & Cerebrovascular Diseases
Musculoskeletal Diseases
Central Nervous System Disorders
Kidney & Urologic Diseases
Digestive Diseases
Endocrine & Metabolic Diseases
Skin Diseases & Wound Repair
Ten platforms. One connected development pathway.
Clinical experience translation
Integrate TCM clinical experience, hospital preparations and human-use evidence to define indications, formulation rationale and endpoints for testable translational research.
AI-assisted screening
Use VMED-OS to connect botanicals, compounds, targets and indications, comparing mechanistic rationale and evidence strength to prioritize candidates for experimental screening.
Pharmacology validation
Define models and endpoints for target indications, investigate activity, dose response and mechanisms, and use experimental results to guide candidate selection and refinement.
Active constituent discovery
Combine pharmacological signals with chemical analysis to isolate, identify and investigate active constituents, informing candidate discovery and structural optimization through evidence on bioactive components.
Quality standards research
Characterize botanicals, extracts and formulations, develop analytical methods and investigate quality markers to support consistent quality assessment throughout research and manufacturing.
Integrated materials and formulation
Coordinate raw material selection, extraction and dosage-form design, studying formulation performance, process scale-up and batch consistency to connect laboratory findings with product development.
Transdermal delivery screening
Compare formulations, carriers and permeation strategies for selected constituents and applications, assessing skin permeation, local retention and compatibility to support transdermal formulation screening.
Sustained and controlled release
Design delivery matrices and formulation processes around drug properties and dosing needs, studying release profiles, stability and in vitro–in vivo relationships to inform development.
Zhangbang traditional processing
Connect Zhangbang processing knowledge with modern analysis of process parameters, compositional changes and quality attributes to document traditional techniques and support process refinement.
Rapid non-destructive botanical testing
Combine non-destructive testing with data modeling to investigate botanical identity, quality differences and atypical features, supporting rapid assessment during material acceptance, grading and monitoring.
BUILT FOR PARTNER DECISIONS
Fast response. On-time delivery.
Connect evidence maps, candidate comparisons, pharmacology experiments and milestone decisions around your indication and research challenge. Clear data packages and validation paths support joint evaluation, due diligence and development planning.
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