Platform
One platform. Target structure in. Ranked candidate dossier out.
Our computational pipeline runs binding affinity, ADMET, and off-target screening as a single integrated workflow — not three separate tools stitched by a consultant.
Workflow
Seven steps. One integrated pipeline.
Differentiators
Three decisions we made that most tools haven't.
Scale of training data matters.
Most published GNN docking models train on fewer than 1M complexes and generalize poorly outside their training distribution. Our training corpus spans every major target class, increasing coverage of novel chemotypes your team is likely working on.
Tox surprises should not come from Phase I.
The FDA's most common Phase I safety discontinuation causes — reactive metabolites, time-dependent CYP inhibition, mitochondrial toxicity — are not covered by standard ADMET tools. We built predictors for each of them.
A biochemical HTS campaign takes 4–8 weeks from assay development to data.
We deliver a ranked hit list in 48–72 hours — not as a replacement for wet-lab confirmation, but as a filter that reduces the synthesis queue from hundreds to a focused set of 10–20 candidates worth making. Your bench time goes to the ones the model has already cleared on binding, ADMET, and selectivity grounds.
Deliverables