Traditional drug discovery:
15 years, $2.6 billion, 90% failure.
The pharmaceutical industry screens compounds through high-throughput physical assays — expensive, slow, limited to what's in the library. Virtual screening with docking tools like AutoDock or Glide processes a few thousand compounds per hour. AI approaches require millions of training samples and still can't explain why a drug works.
| Method | Throughput | Physics-based | Training Data |
|---|---|---|---|
| Physical HTS | ~100K/day | Yes (real) | N/A |
| AutoDock Vina | ~500/hr | Yes (docking) | None |
| Schrödinger Glide | ~5K/hr | Yes (docking) | None |
| AI / ML (various) | ~1M/hr | No | Millions of samples |
| 22Rx / LoNC | 13.2M/sec | Yes (first principles) | Zero |
The speed changes
what questions you can ask.
At 13.2 million compounds per second, you don't pick a subset to screen. You screen everything. Every known compound. Every synthesizable molecule. Every conformer. The bottleneck moves from "can we test it" to "can we synthesize it."
Validation15+ FDA drugs
rediscovered from pure physics.
No medical literature. No FDA databases. No training data. The system was given only molecular structure and disease targets — and independently discovered drugs that took decades to develop.
178,000 candidates banked
across every major threat.
Full compound libraries pre-screened against every major pandemic and bioweapon target family. When the next outbreak hits, we don't start from zero — we pull from a pre-computed arsenal.
LoNC: physics as compute.
Not statistics. Not AI.
Laws of Nature Compute models real molecular physics from first principles — using proprietary navigation to explore the energy landscape. It doesn't learn from data. It derives answers from the laws of nature. That's why it discovers drugs no training set contains.
Running on 44s.io's lock-free cloud infrastructure, the system can screen the entirety of known chemical space against any target in minutes. Not hours. Not days. Minutes.