Pillar 1 · Gene therapy

Capsid Intelligence.

A 240-agent platform that turns published variant libraries, surface-receptor biology, and protein-language-model embeddings into ranked, defensible AAV capsid candidates. U.S. patent 63/986,270 pending.

15 capsids modeled
240 active agents
30 perturbation studies ingested
7 Kd records · 5 PDB structures
L1–L8 predictive framework

Four engines. One pipeline.

Each module is independent (run alone or stack), but designed to share data: fitness scores feed phase-3 prediction; BBB scores filter the fitness output for CNS programs; predictive-levels frames where each module sits in the evidence hierarchy.

15 capsids modeled.

Natural and engineered. BBB transcytosis score, dominant receptor, tissue preference, and seroprevalence (corrected per Boutin 2010 where the literature was wrong).

CapsidClassBBB scorePrimary receptorTissue tropismSeroprev.
AAV9natural0.647Galactose / AAVR2broad (CNS · heart · muscle)~47%
PHP.eBengineered (AAV9-derived)0.710LY6ACNS (murine)—
AAV-Spark100engineered (AAV8-lineage)0.412LamRliver (Beqvez)~38%
AAVrh74natural (rhesus)0.483HSPG · LamRmuscle · CNS~18%
Anc80L65ancestral reconstructed0.534HSPGliver · retinalow
AAV5natural0.218PDGFR · sialic acidairway · CNS3.2% (Boutin 2010)
4D-R100engineered (745aa)0.427retinal-targetedretina—
4D-C102engineered (739aa)0.292cardiac-targetedcardiomyocytes—
… 7 more (AAV1, 2, 3B, 6, 7, 8, DJ — see full inventory under briefing)

Real data, not synthetic.

The platform replaces mock features at every level with peer-reviewed published data — and where the literature was wrong, we corrected it (AAV5 seroprevalence 30–40% → 3.2%; Beqvez capsid mis-assigned as AAVrh74 → corrected to AAV-Spark100/AAV8 lineage).

30 perturbation experiments

From 8 published papers. Used to calibrate Level 2 scaffolding (receptor → tissue → fitness coupling).

7 Kd records

4 SPR + 3 BLI measurements for capsid-receptor binding affinity. Used to anchor the binding-affinity module.

5 PDB structures

With binding residue maps. Used for surface curvature, pocket detection, and contact-map generation.

U.S. Provisional 63/986,270.

Filed 2026-02-19. Covers the predictive levels framework, the BBB transcytosis model, the surfaceome calibration approach, and the contrastive-learning pipeline for capsid fitness.

Modeling a capsid program?

Send us a candidate list (sequences, target tissue, or program name). We come back within 5 business days with a ranked report against the 15-capsid baseline.

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