Predict Protein Formulation Viscosity Before You Have Enough Material
From 4 µL to Smarter Formulations
visQ.AI™ pairs nanovisQ® microliter-scale viscosity data with machine learning to predict viscosity and injectability across hundreds of untested formulations, using only a few measurements.
Each model starts with a shared, validated dataset and adapts locally to the user’s protein and excipient space; no cloud connection or data sharing required.
🔬 How It Works
1. Measure
Collect viscosity profiles from just 4 µL using nanovisQ® (100 – 15,000,000 s⁻¹ shear-rate range).
2. Learn
visQ.AI trains on those anchor measurements to capture your formulation’s behavior.
3. Predict
Instantly map viscosity and injection force across concentration, temperature, and excipient space.
4. Refine
Add new nanovisQ® data; the model evolves and becomes protein-specific.
📊 Validation Snapshot
Built on hundreds of viscosity datasets from monoclonal, polyclonal, and Fc-Fusion antibodies measured across different buffers with many excipients.
Unlike other models, visQ.AI uniquely couples measured shear-rate data with predictive learning, achieving R² > 0.85 and ≤ 20 % error across unseen formulations.
🌍 Early Access
visQ.AI is now available for pilot collaborations with formulation teams seeking to accelerate SC biologic development.