Question 1

What is GeroQubit?

Accepted Answer

GeroQubit is a GPU-free, multi-objective de-novo design platform for geroscience-native small molecules. It designs candidates on a target x tissue x hallmark model, builds them reaction-first so every molecule has a verified synthesis route, and reports every score with a measured baseline and a calibrated confidence interval — the full search runs on CPU in seconds, with no GPU and no overnight training.

Question 2

How is GeroQubit different from other AI molecule generators?

Accepted Answer

It is geroscience-native (aging is the design objective, not a retrofit from oncology), it runs GPU-free on CPU in seconds, and it is honesty-first: every efficacy prediction ships a conformal confidence interval and the published validation accuracy, including where the signal is weak (leave-one-out rho ~ 0.16) and where it fails (AUC 0.62 on genuinely novel chemotypes).

Question 3

How accurate is GeroQubit?

Accepted Answer

On a held-out retrospective recovery of ~1,940 ChEMBL binders against decoys, the binding-plausibility score reaches ROC-AUC 0.945 with ~20x enrichment at 1% (BEDROC 0.91) and survives a scaffold-disjoint split. The structure-to-lifespan efficacy prior is a deliberately weak, disclosed signal with 90.3% measured conformal-interval coverage.

Question 4

Does GeroQubit produce synthesizable molecules?

Accepted Answer

Yes. Molecules are assembled reaction-first through verified reaction steps rather than emitted as raw strings, so every candidate has a synthesizable 1-2 step route by construction, with atom-level synthon provenance.

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