Generative antibody design and wet-lab validation running as one loop, under one roof. This page is the technical deep-dive — written for CSOs, lab heads and therapeutic area leads.
Most platforms hand off between a computational team and an outsourced lab. Every handoff costs weeks and loses information. Antiverse closes the loop.
Antibody libraries generated de novo from target structure alone. No requirement for existing ligands, binders, or prior campaign data — which is what allows programs to start where others cannot.
Candidates clustered by predicted epitope and functional profile, so a partner receives a diverse panel rather than fifty variants of the same binder. Diversity is the hedge against downstream developability failure.
Engineered lines expressing GPCRs at 20× the density of standard lines. Receptors are presented in their native membrane environment, not as a purified proxy — which is why functional, not just binding, candidates come out.
Panning and functional assay against the disease-relevant receptor. Results feed directly back into the generative model, compounding the data advantage with every program run.
Generalist protein models are trained on general biological data. Antiverse's models have been trained exclusively on GPCRs since 2017 — the structural nuance is the whole point.
GPCR-specific training data, accumulated across partner and internal programs.
Proprietary engineered cell lines. Years to build, not purchasable.
Higher GPCR expression than standard lines, enabling functional screening.
For the scientific evaluator, peer-reviewed work is the trust signal. Every paper, preprint and poster in one place.
Author list · journal · volume. Abstract summary, two lines.
Author list · bioRxiv. Abstract summary, two lines.
Conference name · session. One-line summary.
Ben Holland and the Antiverse scientific team. Field landscape and Antiverse's position within it.
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