Three operational layers — computational, experimental, and translational — held together by one biological data graph. Programs move forward continuously, not in monthly handovers.
A foundation model trained on 1.2B annotated biological sequences predicts protein–compound interactions in silico — surfacing leads weeks ahead of physical assay.
Unlike vendor screening libraries, our graph is built from first‑principles biology: structural data, longitudinal patient cohorts, organoid readouts, and over a decade of curated literature. It updates nightly with every wet‑lab result that returns from the floor.
A 32,000 sq.ft robotic facility synthesises and tests thousands of candidates per week against patient‑derived organoid models — the highest‑fidelity in vitro assay system commercially available.
Every program runs on disease tissue, not transformed cell lines. Phenotypic readouts feed back into the computational graph within 36 hours of plate completion. Failure modes that would only emerge in late preclinical surface here, weeks earlier.
A biomarker‑led clinical team carries promising candidates through IND‑enabling studies and Phase 1 in partnership with academic medical centers across three continents.
Translational biomarkers are designed in alongside discovery, not bolted on at IND. Patient stratification hypotheses are pressure-tested against prospective cohorts before the first dose. Partners get a ready-to-run Phase 1 protocol, not a candidate molecule on a shelf.
A typical engagement runs from target nomination through Phase 1 readout in 26–32 months — with continuous data sharing, no quarterly reveals.
Patient multi-omics analysis to identify and validate druggable targets.
Generative chemistry, virtual screen, in-silico ranking.
Robotic synthesis and patient organoid assays.
DMPK, tox, CMC, GLP packages and biomarker design.
Investigator network, biomarker-led patient stratification.