From target to synthesis-ready candidates. We design, score, screen, and select — our wet-lab partners build and validate.
In short: we are a complete computational drug discovery platform. Bring us a target and we generate a candidate library, predict structure, screen for safety and patent problems, and rank what's left into a short, defensible build list. Wet-lab partners synthesize and validate; their results feed back into the engine.
This is a schematic of one step in the discovery pipeline — candidates in, screening, a ranked build list out. It illustrates the process; it is not prediction output for specific molecules.
Eight stages, one auditable thread. The first six run inside the platform; the last two happen with your wet-lab partner, and what they learn comes back in.
You provide the biological target.
We generate a candidate library against it.
Boltz-2 — an open, third-party model we license and run, not one we built — predicts each candidate's 3D structure and its complex with the target, with interface confidence.
Our selection engine ranks every candidate that's left.
Biosafety (could it be misused as a biothreat) and freedom-to-operate (does it infringe existing patents) screening — fail-closed, meaning any candidate we can't clear is blocked, not waved through.
Ranked, auditable, sealed to a tamper-evident record.
Our synthesis and validation partners build it.
What comes back from the bench.
Closing the loop: wet-lab outcomes feed back into the design and scoring engine, sharpening every candidate library that follows — the platform gets better informed with every round, not just faster.
Schematic of the discovery pipeline. Stages 1–6 run inside the platform; stages 7–8 happen with a wet-lab partner and are not LillixBio outputs.
AI can now design more drug candidates than any company can afford to synthesize and test. Generating ideas is no longer the bottleneck — knowing which few are worth a wet lab's time is. LillixBio is the computational engine that makes that call, end to end, from a named target to a synthesis-ready build list.
Same discovery engine, three ways to use it — sized to how each customer actually works.
The discovery engine at the center of the pipeline: designs or ingests candidates, predicts structure, screens for safety and patent problems, and ranks what's left into a short build list — with the reasoning for each pick attached.
See DECIDE ↓The clearance step every candidate passes before it reaches a build list, available standalone. Every candidate gets a clear yes-or-no, plus a report you can hand to a regulator.
See CLEAR ↓The operational layer of discovery: tracks what happens after a build list ships to the wet lab — tied to the same paper trail DECIDE already created.
See OPERATE ↓Each engagement is scoped around something specific you want built, and measured against whatever you're already using today.