Summary
Validation on 100 pairs across nutrients, minerals, lipids, polymers, and a few common drugs. Goal: confirm that the ΨΛ V4 Grid correctly flags three interaction types: synergy, antagonism, neutral. No formulas. Just the logic, outcomes, and limits.
Why map affinities
Simple combinations change effectiveness:
- Vitamin C increases iron absorption.
- Vitamin D3 supports calcium uptake.
- Calcium inhibits iron absorption.
- Zinc and copper compete.
Knowledge exists but is scattered. ΨΛ V4 puts it on one common basis to compare, prioritize, and automate.
The logic, without formulas
Each entity (atom, molecule, material) gets a logical signature. Two signatures are compared point by point. Strong “recognition” → higher affinity. Weak recognition → antagonism. No clear signal → neutral.
Process:
- extract each item’s signature
- match relevant motifs between the two items
- aggregate comparisons into one affinity score
Then apply calibrated thresholds to label synergy, antagonism, or neutral. Thresholds come from the observed distribution on the 100 pairs and from published examples.
What the 100 pairs show
Synergies (examples):
- Vitamin C ↔ Iron: matches increased absorption.
- Vitamin D3 ↔ Calcium: matches calcium regulation.
- Vitamin E ↔ Lipids: matches membrane protection.
- Vitamin C ↔ Collagen: matches collagen synthesis.
Antagonisms (examples):
- Calcium ↔ Iron: matches reduced iron absorption.
- Zinc ↔ Copper: matches competition in absorption.
- Tetracyclines ↔ Calcium: matches chelation effects.
Neutrals (examples):
- Vitamin B12 ↔ Calcium: no net effect expected.
- Vitamin D ↔ Selenium: no marked interaction.
- Aspirin ↔ Zinc: neutral in this framework.
Bottom line: one consistent rule separates most known cases. Results are stable, reproducible, and aligned with references.
Uses
- Formulation & R&D: filter promising combos, avoid obvious conflicts, propose variants.
- Nutrition & health: structure basic guidance with synergies and conflicts.
- Education: visualize the interaction landscape with a shared language.
- Automation: connect the grid to ingredient libraries for real-time alerts and suggestions.
Limits
- Not medical advice. Does not replace diagnosis or treatment.
- Dose, chemical form, and biological context matter and can flip trends.
- Drugs require higher vigilance. Always check clinical guidance.
What this validation proves
- One unified logic explains interactions across domains without switching tools between nutrition, materials, and biology.
- The grid generalizes: it does not memorize fixed pairs; it recognizes motifs. Useful for exploring new combinations.
- The framework is falsifiable: add pairs, measure precision, draw ROC curves. Performance can be tested, improved, and compared.
Next steps
- Expand beyond 100 pairs for stronger statistics.
- Publish ROC curves and precision/recall by class.
- Integrate pharmaceutical libraries for finer screens.
- Open an internal API for third-party tests and industrial use cases.
FAQ
Does the grid invent interactions?
No. It classifies from signatures. When the literature is clear, the grid follows it. When ambiguous, the grid returns an intermediate score.
Can it apply elsewhere?
Yes. The same mechanism works for DNA, materials, and complex systems, with context-appropriate constraints.
How to contribute?
Propose additional pairs with sources. We integrate, measure, and publish results.
Learn more
- PDF: “Validation Affinities 100”.
- Canonical bio-mineral tables.
- ΨΛ V4 methodological guide.