DeepViscosity
Compare predicted antibody viscosity.
Results
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Method & evidence
Classify paired heavy/light candidates with DeepViscosity and compare their predicted high-concentration viscosity risk.
Scope & limitations
- DeepViscosity requires paired heavy/light chains and does not infer missing partners.
Interpreting results
- Use the classification to prioritize review within a consistently prepared candidate set.
- The probability is model evidence, not a measured viscosity or a universal formulation threshold.
- Compare predictions with concentration, buffer, temperature, and experimental viscosity data.
References
Input requirements & outputs
Input checks
- Paired heavy and light protein sequences per row
- One frozen Selection or Dataset Version
- Up to 100 rows and 500 residues per sequence
Expected results
- Candidate classification — Compare predicted viscosity class across the selected antibody set.
- Ensemble probability — Review mean probability and variation across the model ensemble.
- Model input features — Inspect the physicochemical feature values used for each candidate.
