Agentic Causal Graph Learning for Drug Target Discovery
Causal graph methods for mapping pathway relationships — applied to nutrition pathway analysis.
Our work begins with a defined scientific question. We then assess fit, scope the report, review relevant literature, and apply computational or biomolecular methods where they add value.
Define the question
Assess fit and scope
Run literature and computational analysis where relevant
Deliver a written report or dossier
Not every project requires every method. The method depends on the question. The deliverable is always a written scientific asset.
The computational approaches we may use have been validated through peer-reviewed publications at AAAI, ICLR, NeurIPS, and other top AI and bio venues.
Causal graph methods for mapping pathway relationships — applied to nutrition pathway analysis.
ADMET screening methods adapted for ingredient safety and bioavailability assessment.
Validated causal hypothesis testing methods applied to bioactive compound analysis.
Multi-scale safety screening of compounds — the same approach for ingredient-level analysis.