Predicting the Dynamic Viscosity of High-Concentration Antibody Solutions with a Chemically Specific Coarse-Grained Model
Bibliographic record
Abstract
Abstract The viscosity of high-concentration protein solutions is a critical parameter in bio-pharmaceutical formulation development. Conventionally, the viscosity is measured and optimized in labor-intensive experimental workflows that require a lot of material. While predicting the viscosity with atomistic molecular dynamics (MD) simulations is feasible, they are computationally prohibitively expensive due to the large system sizes and the long simulation times involved. Coarse-grained MD (CG-MD) simulations significantly reduce computational demands, but evaluating their accuracy and predictive power requires rigorous validation. Here, we assess the capability of the Martini 3 CG force field to predict the viscosity of high-concentration antibody solutions. We show that a refined Martini 3 force field, with optimized protein–protein interactions, can predict the elevated viscosities observed in concentrated solutions of F(ab’) 2 fragments of the therapeutic monoclonal antibody (mAb) omalizumab. Furthermore, we show that our previously developed Martini 3-exc model for arginine excipients successfully captures the trend of lowering viscosity, as observed in our rheology experiments. These findings open the way to physics-based computational prediction of the properties of dense biopharmaceutical solutions via large-scale MD simulations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".