Testing High Concentrations of Membrane Active Antibiotic Chlorhexidine Via Computational Titration and Calorimetry
Bibliographic record
Abstract
Coarse-grained strategies for membrane simulations are designed to increase efficiency for larger and more complex molecular dynamics simulations. For membrane active antibiotics, the concentration dependence of their action presents a tremendous challenge in simulation scale. In this study, we examine the effects of concentration for the popular membrane active antibacterial drug chlorhexidine. It presents an interesting biophysical modeling test, where from experimental experience we know that model membranes of 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) can absorb very high quantities of the drug without disruption. We construct a coarse-grained model of chlorhexidine in three different charged states and compare to previous all-atom simulations and new experiments. Using large, long-time, and unbiased simulations of chlorhexidine inserting into the lipid bilayer, we find little changes to the functional structure of a DMPC membrane up through concentrations of 15:100 drug:lipid, where the slowing rate of continued insertion tests the capabilities of even this coarse-grained approach. We validate our simulations with computational calorimetry measurements, and show that they agree with new experimental data from differential scanning calorimetry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".