How the method works.

Responder Atlas is our platform, built on public pharmacogenomic data. For every drug in the DepMap/PRISM training set, we know which cell lines responded and which didn't, and what those cell lines look like at the molecular level. That gives us a map from molecular structure of a cancer cell to how strongly a compound hits it.

When you submit a new molecule, we compute where it sits in that map by chemical structure and read the response signal off its neighbourhood — a ranked score across seventeen cancer tissue types plus a confidence tier that reflects how similar your molecule is to what the model has actually seen.

The output is a directional triage signal. Every prediction is accompanied by its provenance: whether the compound was measured in DepMap or inferred from structure, which training-set drug it's most similar to, and how tight that similarity is. Nothing is hidden behind a scalar.

Cross-tissue selectivity — separating the indication where a drug works from the ones where it doesn't — is the hardest axis, and sits at the structural limit of chemistry-only methods. We say so plainly: per-indication scaffold-LDO performance lands around r ≈ 0.39. Useful for triage, not for committing to a single tissue on structure alone.

The signal is grounded in cell-line response, not patient outcomes. It's a hypothesis-generating triage layer, not a clinical or efficacy prediction. Confidence tiers are the honest handle: elevated when your molecule is close to training, exploratory when it isn't, and we surface both.