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Research notes

What we measured, including what did not work.

Longer write-ups on the parts of de novo longevity drug design that are hard to get right. Every number here comes from a run on our own engine, and the results that went against us are in the same posts as the ones that did not.
Method9 min

Knowing when to trust a prediction: the applicability domain in AI drug discovery

We ship a calibrated confidence figure on every molecule, because we measured exactly where our own predictions stop being reliable. Most tools report the score and not the caveat.

New here

Our own leave-one-out and generated-molecule measurements: known binders sit 0.41-0.53 from each other, our output sits 0.14-0.17 from them, and recovery AUC falls 0.945 to 0.62 on that slice. Also the negative result that 3D physics lost to 2D fingerprints in this regime.

Method10 min

Synthesizability by construction: design the route, not the molecule

If your generator emits SMILES, it cannot tell you how the molecule was made, because it never knew. Evolving the recipe instead of the structure changes what the model can and cannot say.

New here

A measured reaction-search result (+22.9% products per reaction evaluation, 95% CI [+16.2%, +33.1%], 24/24 paired runs) and a worked failure: an azole coupling template that could never fire, invisible because a dead template produces no wrong molecules, only missing ones.

Biology11 min

Lifespan targets are not rejuvenation targets, and the evidence separates cleanly

Every gene on our lifespan list appears in the longevity-gene database. None of the rejuvenation-evidence genes do. That separation is measurable, and it changes which question a molecule can answer.

New here

A measured, non-circular separation between the two target sets (1 of 11 versus 6 of 6 carry aging-perturbation data) plus two pre-registered no-effect results published with their confidence intervals, which is the part almost nobody publishes.

Method12 min

Nine quantum-inspired operators that make molecular search measurably faster

Twenty-four paired runs, every one a win: 22.9% more products per reaction evaluation, with feasibility prediction at 0.946 AUC against 0.637 for the rule table it replaced.

New here

A company named after qubits publishing the proof that its own quantum kernel is a classical RBF (max absolute difference 1.44e-15 against scikit-learn), plus three operators killed by pre-declared controls, including one whose apparent signal was entirely a sparsity artifact.

Biology12 min

CXCR2 inhibitor design: three new chemotypes for a target with validated senescence biology

Three candidates, each with named reagents, a genotoxicity screen run over the route rather than the product, and a written protocol at 10 mg scale.

New here

A measured chemical-distance comparison between the four clinically failed CXCR2 compounds and our three designed candidates, showing the failed drugs sit closer to each other than ours sit to any of them, with the exact Tanimoto ranges published.

Biology13 min

The 12 hallmarks of aging, and how each one becomes a drug target

Genomic instability, telomere attrition, epigenetic drift and nine more. Here is what each hallmark means for drug design, which have real chemistry behind them, and how a candidate gets scored against every one.

New here

A per-hallmark druggability read from our own target work: which of the twelve have usable small-molecule chemistry, which are covered by our 21 runnable targets, and the measured finding that 11 hallmarks carry roughly 2.9 independent directions rather than 12.

Why these read the way they do

Publishing where we fail is the point. Two of our own validation studies showed no effect results and both are written up here with their confidence intervals, because a number quoted without its interval is a different claim from the one the data supports. If a figure on this site looks weaker than a competitor's, that is usually because it was measured on the hard case.