0.81
Leave-program-out AUC
AINN-P1 on VHH affinity maturation, vs 0.68 for finetuned ESM2, parity-plus at 3.9× fewer parameters.
AINN-P1, our protein foundation model, designs picomolar antibodies that work in the wet lab. Validated across 40 targets. Licensable today.
Validated targets with sub-nM leads
Single-digit picomolar campaigns
Molecules experimentally tested in wet lab
Parameter protein foundation model (AINN-P1)
Most AI drug-discovery platforms stop at a demo. We go further: our models are trained on industry-grade proprietary data and validated at every stage by wet-lab experiments, so what we deliver to pharma partners is already de-risked.
The result is a different kind of handoff, not a screening score, but a program: patentable, validated, licensable, and ready for your development pipeline.
Built on AINN-P1, SentinusAI combines sequence modeling, affinity prediction, and generative design into a single pipeline. Trained on proprietary assay data and continuously benchmarked against wet-lab outcomes.
IgG, Fab, scFv, VHH, bispecifics. Picomolar leads on IL-13, IL-25, IL-36R, TSLP.
De novo small molecules, PROTAC design, scaffold hopping. CRBN neosubstrate selectivity panels.
GLP-1 analogs, cyclic peptides, stapled therapeutics. 3.2M-peptide screens with 90%+ accuracy.
CAR construct optimization, activation prediction, persistence modeling.
AINN-P1 outperforms finetuned ESM2 on leave-program-out AUC for VHH binders (0.81 vs 0.68) at a fraction of the parameter count. Our methods are peer-reviewed, our benchmarks are external, our failures are published alongside our successes.
AINN-P1 on VHH affinity maturation, vs 0.68 for finetuned ESM2, parity-plus at 3.9× fewer parameters.
Methods, benchmarks, and validation studies published across antibody, protein-language-model, and chemistry venues.
Founders bring twenty-plus years of structure-free affinity modeling and protein engineering to the company founded in 2021, not a ChatGPT-era pivot.
A selection of in-house antibody programs with wet-lab-confirmed picomolar binding. Full data room available under CDA.
| Target | Modality | Best Kd | Stage |
|---|---|---|---|
| IL-13 · atopic dermatitis, asthma | VHH / scFv | ~1 pM | Lead optimization |
| IL-25 · atopic & T2 inflammation | IgG | ~1 pM | Lead optimization |
| IL-36R · generalized pustular psoriasis | IgG | ~1 pM | Lead optimization |
| TSLP · severe asthma | IgG / Fab | ~1 pM | Lead optimization |
| IL-15 × IL-21 bispecific · oncology | Bispecific IgG | single-digit pM | Affinity maturation |
| TNF-α × OX40L bispecific · autoimmune | Bispecific IgG | single-digit pM | Affinity maturation |
| TROP2 · oncology ADC payload target | IgG / Fab | sub-nM | Wet-lab validated |
| B7-H3 · solid tumors | IgG | sub-nM | Wet-lab validated |
All affinities measured by SPR or BLI from AINN-P1-designed campaigns. Figures from Antibody Model Experimental Validation Statistics (Sep 2026), 40-target dataset, available under CDA.
We work with partners whose capabilities complete what a sequence-first model cannot do alone: human biological validation on one side, clinical-outcome prediction on the other. These are the two active, publicly announced collaborations today.
Partnership to combine Ainnocence AI-driven discovery with Obatala's human adipose and metabolic tissue platforms, so that de-novo molecules can be tested in physiologically relevant human biology before any animal study.
Read announcement →Industry-first collaboration integrating Ainnocence's discovery platform with Phase Advance's preclinical and clinical trial modeling, so that drug performance can be predicted years earlier than traditional development allows.
Read announcement →Our data room is open under CDA. Pick a target, we'll share wet-lab data and freedom-to-operate analysis within one week.
Request the data room →