De novo longevity chemistry,with the route that makes it.
Quantum methods for the biology of aging.
GeroQubit combines proprietary quantum-derived methods with a multidimensional search architecture to explore aging biology, tissue context, biological interactions and chemical feasibility, while designing the synthesis route alongside the molecule. Two programmes: extend lifespan, or push cells younger.
Send us a target. We design a candidate set against it and send back the molecules with their full synthesis routes, reagents and costed steps. Free, and with no claim on the IP. Every score is computational and carries its interval.
2D structureCOC1=C/C(=C\C(=O)C(=O)Nc2nc(O)c3cc(C)ccc3n2)OC(O)=C1Not another molecule generator.A different way to search.
Most design systems score a molecule against a list of objectives and pick the winner. GeroQubit treats discovery as one search through biological and chemical state at the same time, and moves compute toward the directions that hold up.
The reactions are part of the genome, so the route exists before the molecule does. A generator that draws a structure first has to guess the route afterwards, and sometimes there is not one.
Nine quantum operators, one pass
These are not nine scores to average. They are nine coordinated search operators reading the same aging state, so tissue, biological relationships and molecular constraints stay visible together instead of collapsing aging into one number.
Behind the tissue axis
Genes with a measured aging direction, per tissue. Every tissue in the state is covered in the thousands, including thymus, which one of our four sources does not carry at all.
Behind the hallmark axis
The twelve hallmark gene sets are not the same size, and we do not draw them as though they were. Chronic inflammation has 2,972 genes and cellular senescence has 99, so the two are not observed equally well.
Aging is a state, not a score.
GERO is geroscience. QUBIT is how we hold it: aging carried as a quantum-derived state vector, ten tissue components superposed with twelve hallmark components, and never collapsed to a scalar until a human asks for one. Below is a real candidate from a real run, read out both ways.
The molecule assembled in the recipe above, carried through the search as a state rather than a score.
Its own hallmark mean. Everything to the right disappears into it.
Five tissues respond. Five do not. Brain is one of the five that do not.
Runs 0.95 to 0.00. Four hallmarks are untouched, and a mean cannot say which four.
Twelve hallmarks, ten tissues, 15,705 genes with a measured aging direction, and 21 targets across two programmes.
A state vector, not a number. Twenty-two components held in superposition, read by quantum-derived operators, and measured at the end rather than averaged at the start.
A candidate that helps liver and does nothing for brain stays legible as exactly that, all the way through the search.
The structure runs classically on ordinary CPUs. What is quantum here is the formalism the operators are derived from, not the hardware, and we say so on every surface.
A quantum operator that reads chemistry before it tries it.
QRC is the reaction-feasibility operator in the QX layer, and the only one of the nine we let steer the engine. It projects a fragment onto the reactions its functional groups can support, so the search stops spending itself on chemistry that was never going to work. Below is why that matters, measured on our own corpus.
Head of the curve: mitsunobu 98% · amide coupling 66% · buchwald–hartwig 63%. Tail: 27 templates that have never once produced a product on our chemistry.
What QRC does
It reads the functional groups on the fragment in hand and puts the reactions most likely to work on it first. Nothing is skipped and nothing new becomes reachable. Only the order changes, so a workable route turns up earlier and fewer attempts land in that flat tail.
16.1% of all reaction attempts used to go to templates that never once produced a product.
How well it predicts
Predicting whether a reaction will fire, on 9,173 reactant pairs never seen in training. Scale starts at 0.50, which is chance.
24 paired runs, 8 targets, 3 seeds. 24 won, 0 lost.
A search-efficiency result. It says nothing about whether a molecule works in an animal.
Assembled from real chemistry, so the route exists before the molecule does.
The engine evolves recipes, not pictures of molecules. Nine quantum operators read the aging state you chose and steer that search; the chemistry underneath is ordinary named reactions with the reagents each bond needs.
Most generators emit a structure and guess the synthesis afterwards. We build from the reaction, so the route is not a prediction.
The proof, in three numbers.
Backed by a methods preprint, including the number where we fail.
Recovers known binders from decoys on a scaffold-disjoint held-out split.
Level with the strongest unconstrained generators (0.94–0.95), and ours stay makeable.
The lifespan prior is weak but real (ρ 0.158), always shipped with its interval.
We write the test down before we run it.
Tested twice against compounds already known to fail 19 in mice, then 133 in worms. Neither test showed we can rank them. Published in full, both times.
- Protocol fixed
- Analysis run
- Result published
Nine quantum-derived operators, rebuilt for chemistry.
Quantum methods are written for qubits, not for molecules. Every operator we use had to be rebuilt before it worked on a search over real synthesis routes. We do not publish the rebuilds. We publish what they score.
The first bar is the industry’s own generation benchmark, and we reach it while every molecule is constrained to a real route, a restriction the methods in that range do not carry. The third is not a score but a property: a route exists by construction, so it is 100%, and a structure-first generator cannot report it at all.
We audit our own quantum claims
A similarity model at the centre of our stack turned out to be reproducible by classical means to within 1.44e–15. We found it, published it, and withdrew the claim rather than sell it.
What survived that audit
One operator captures structure that no conventional formulation can express. It moves a ranking metric by +0.0112, 95% interval [+0.0083, +0.0145], excluding zero. Small, real, and the interval travels with it.
Genotoxicity screened at design time
ICH M7 alerts are run over the route reagents, not only the final molecule, catching a DNA-reactive handle that is consumed during synthesis and invisible to product-only screening. 12/12 known positives flagged, 6/6 benign clean.
Quantum-inspired, classically computed. Benchmark range from Gao et al., NeurIPS 2022. Where a result is a tie or a loss we publish it as one. The full ledger, including what did not work, is on the benchmark page.
Three steps. Answers in seconds.
Pick a target
Eleven, ranked by human genetic evidence.
→Set the scope
A single organ, or pan-aging across all ten.
→Get candidates
A route and a stated confidence on each.
Built for teams hunting real longevity chemistry.
Longevity biotechs
Novel, makeable scaffolds for hit-to-lead, before you commit synthesis budget.
Academic geroscience labs
Tool compounds to probe a target you have already validated biologically.
Medicinal chemistry teams
A ranked shortlist with routes, ADMET and an explicit confidence on each.
We fix the biology first, not the molecule.
- Genomic instability
- Telomere attrition
- Epigenetic alterations
- Loss of proteostasis
- Disabled autophagy
- Deregulated nutrient sensing
- Mitochondrial dysfunction
- Cellular senescence
- Stem-cell exhaustion
- Altered intercellular comm.
- Chronic inflammation
- Dysbiosis
- CNS / brain
- Heart
- Liver
- Skeletal muscle
- Immune (PBMC)
- Adipose
- Vascular
- Hypothalamus
- Gut
- Thymus
Hallmarks follow López-Otín et al., Cell 2023. The readout is rule-based and reported as a prior, not a measurement.
A target the clinic already knows, in a disease nobody pointed it at.
CXCR2 is the receptor that reinforces cellular senescence. Four antagonists have been through human trials against it, all for lung disease or cancer, all dosed through the whole body. None was taken into osteoarthritis, where the drug can be put into the joint instead. We designed three candidates for that gap.
N-arylation → amide coupling
N-arylation → amide coupling
N-arylation → amide coupling
Nothing here has been tested. One of the three can already be ordered from a catalogue, which puts a real experiment three weeks away. Full study, scores and routes on the methods page.
Built for aging biology, not retrofitted from oncology.
The whole stack is built around one idea: hold the aging state whole. Quantum formalism is what makes that practical, because a state vector is the natural object for something with twenty-two components that interact.
Built for aging
Twelve hallmarks and ten tissues, carried as one state. Not an oncology retrofit.
Honest uncertainty
Efficacy calls ship a calibrated interval, and we publish the tests we fail.
Answer in seconds
A full design run finishes while you are still on the call.
Two programmes
11 lifespan targets, 10 rejuvenation targets, 10 tissues, nine quantum operators.
The pathways longevity actually runs on.
Design against one mechanism, or switch to whole-body mode and optimise across all ten tissues at once.Each tag is the direction that program is designed toward, its reference compounds all act that way. Confirming it takes an assay.
NAD⁺ → SIRT1 / PGC-1α metabolic axis
Chromatin guardian; NF-κB co-repression
Rate-limiting enzyme of the NAD⁺ salvage pathway
Senolytic clearance of senescent cells
Autophagy gate; canonical longevity axis
Cellular energy sensor; ADaM-site activation
Suppress the SASP / inflammaging
Silence the senescence secretory phenotype
RAS–MEK–ERK inflammaging axis
Master antioxidant-response regulator
IGF-1 / insulin (daf-2) longevity axis
A different question: can it push cells younger?
Lifespan and rejuvenation are not the same programme, and we measured it rather than assumed it. Every one of our eleven lifespan targets is a known longevity gene; almost none carries direct aging-perturbation data. The targets that do carry it are epigenetic enzymes, and none of them was on the lifespan list.This programme is research. It designs candidates today; nothing in it has been synthesised or assayed.
Epitranscriptomic ageing; highest-scoring admitted target
The Zhang Sci Transl Med 2021 rejuvenation target
Drives the senescent state (Sen, Mol Cell 2019)
Master regulator of the SASP (Freund, EMBO J 2011)
The SASP receptor (Acosta, Cell 2008)
Raises HIF-1α; hypoxic adaptation
Mitochondrial protein acetylation with age
2-HG and the metabolic arm of ageing
Nucleic-acid methylation marks
Redox balance
A library screen needs known ligands to begin from. SOD1 has 18 measured binders in ChEMBL and ALKBH1 has 45, enough to recognise the chemistry, nowhere near enough to screen against. Building a molecule needs no starting library, which is exactly why these stay on our list rather than falling off it.
We don't ship a score we can't measure. Where the signal is strong, we say so. Where it's weak, we show the interval, and let your chemists decide.
A field burned by overpromising. We measure, or we don't ship.
Pick a programme. We'll bring the chemistry.
Eleven aging programmes, ten tissues, and a synthesis route on every candidate.