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GeroQubit
De-novo longevity drug design you can actually synthesize
De-novo longevity drug design — GeroQubit builds geroscience-native, synthesizable small molecules on a target × tissue × hallmark model, reaction-first and GPU-free, with a calibrated confidence interval on every score.
Hi PH 👋 I'm Dinesh, one of two people building this.
Every AI drug-discovery tool I looked at had the same two problems: it would hand you a molecule no chemist could actually make, and it would report a benchmark score without telling you when that score stops working.
So we built GeroQubit backwards. Instead of generating a structure and reverse-engineering a synthesis, the engine evolves recipes — scaffold + building blocks + named reactions. The molecule is the output of a route that already exists. Nothing gets proposed that can't be built.
The part I'd rather show you than hide: our binding score hits ROC-AUC 0.945 on known chemistry — and collapses to 0.62 on genuinely novel molecules. That collapse is universal in this field; almost nobody publishes it. We put it on our benchmark page in the same size type as the good number, because a score you can't trust is worse than no score.
Other things that are real: matches the strongest unconstrained generators on the PMO benchmark (0.938, QED oracle) while every candidate stays makeable; the whole search runs on a CPU in seconds; and each efficacy prediction carries a calibrated confidence interval (~90% coverage).
It's research-use only — a hypothesis engine for medicinal chemists, not a clinical claim. We have no wet-lab data yet, and I'd rather say that up front.
Happy to answer anything, especially the sceptical questions. 🧬
About GeroQubit on Product Hunt
“De-novo longevity drug design you can actually synthesize”
GeroQubit was submitted on Product Hunt and earned 2 upvotes and 3 comments, placing #118 on the daily leaderboard. De-novo longevity drug design — GeroQubit builds geroscience-native, synthesizable small molecules on a target × tissue × hallmark model, reaction-first and GPU-free, with a calibrated confidence interval on every score.
On the analytics side, GeroQubit competes within Health & Fitness, Artificial Intelligence and Science — topics that collectively have 560.3k followers on Product Hunt. The dashboard above tracks how GeroQubit performed against the three products that launched closest to it on the same day.
Who hunted GeroQubit?
GeroQubit was hunted by Dinesh K. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of GeroQubit including community comment highlights and product details, visit the product overview.
Dinesh K