A Tenth of a Cent per Guess, and No Qubits in the Loop
A tenth of a cent per ranked pair
On August 19, 2026, SandboxAQ made a virtual screening model called AQPotency generally available. The company's release says it scores how strongly a candidate molecule is likely to act on a disease target, ranks molecule and target pairs in seconds, runs on ordinary computing hardware, and costs as little as one dollar per thousand comparisons. At the advertised minimum, a million pairwise rankings come to about a thousand dollars. Screening at that price stops being a budgeted event a team plans around and becomes something a chemist can run while thinking.
Screening without a solved protein structure
The claim that will interest a hospital pharmacologist sits one line further down. AQPotency is described as working without a solved protein structure, which makes it structure-free potency prediction: the model does not need a crystallographic or cryo-electron map of the target before it will score anything. A great many disease targets have no such map, and programs aimed at them have historically stopped at that wall. Cheap ranking only helps if the ranking is right, and the release publishes no accuracy figures. What it offers instead is eight customer programs with what the company calls experimentally validated impact, plus named academic collaborations. No independent evaluation, no prospective blinded benchmark and no head to head against an established free-energy method appears in the launch material.
The Parkinson's campaign has its own preprint
The launch quotes Gary Miller of Columbia University's Mailman School of Public Health on selective binders for SV2C, a synaptic vesicle protein implicated in Parkinson's disease. That campaign was posted to bioRxiv the same day, with a SandboxAQ corresponding author and Miller as final author. Its methods matter: because no full-length SV2C structure existed, the team built a homology model from SV2A cryo-electron microscopy templates, ran molecular dynamics, and applied a convolutional neural network scoring function inside a funnel that narrowed 5.96 million commercial compounds to 3.19 million before docking. Of 94 prioritized candidates, 71 were profiled and 22 were active. It is a genuine result on a target with no selective probes, and it is neither an AQPotency run nor a structure-free one.
The pillar field reads not applicable
Nothing in this announcement runs on a quantum computer. The most operationally ready development in today's quantum medicine pool comes from a company the quantum trade press covers closely, and the tool is classical software on conventional machines. One day later, a separate group posted a preprint on loading a molecule's wavefunction onto qubits at all, which is the step before any quantum chemistry happens. Both belong in the pool, and they sit at opposite ends of the readiness ladder.