Three programs, one open pipeline.

AI-Guided Target Discovery

Using machine learning over public immunology datasets to prioritize targets and candidate molecules for allergic and inflammatory disease, before a single wet-lab dollar is spent.

1. 1.1

   Build a reproducible, open pipeline over public datasets (GEO, Open Targets, ChEMBL) for allergic and atopic disease.

2. 1.2

   Rank targets by durability of effect and manufacturability, not only potency.

3. 1.3

   Publish the pipeline and ranked lists openly so any lab can build on them.

Durable, Affordable Therapeutics

Partnering with academic labs to move the strongest candidates toward long-acting, low-cost formulations designed for global access.

1. 2.1

   Select candidates where a long-acting or preventive formulation is plausible.

2. 2.2

   Design for low cost of goods and cold-chain independence from the first experiment.

3. 2.3

   Structure collaborations so results stay accessible, not locked behind exclusive licenses.

Community Health Data

Short, consented surveys and volunteer cohorts that capture how allergies and eczema actually show up in daily life, feeding real-world signal back into discovery.

1. 3.1

   Mobile-first surveys built with and for families managing allergic disease.

2. 3.2

   Consent-first, de-identified data with clear participant rights.

3. 3.3

   Share aggregate findings back with the community that produced them.