The Role of Atomic Polarization in the Thermodynamics of Chloroform Partitioning to Lipid Bilayers
Bibliographic record
Abstract
In spite of extensive research and use in medical practice, the precise molecular mechanism of volatile anesthetic action remains unknown. The distribution of anesthetics within lipid bilayers and potential targeting to membrane proteins is thought to be central to therapeutic function. Therefore, obtaining a molecular level understanding of volatile anesthetic partitioning into lipid bilayers is of vital importance to modern pharmacology. In this study we investigate the partitioning of the prototypical anesthetic, chloroform, into lipid bilayers and different organic solvents using molecular dynamics simulations with potential models ranging from simplified coarse-grained MARTINI to additive and polarizable CHARMM all-atom force fields. Many volatile anesthetics display significant inducible dipole moments, which correlate with their potency, yet the exact role of molecular polarizability in their stabilization within lipid bilayers remains unknown. We observe that explicit treatment of atomic polarizability makes it possible to accurately reproduce solvation free energies in solvents with different polarities, allowing for quantitative studies in heterogeneous molecular distributions, such as lipid bilayers. We calculate the free energy profiles for chloroform crossing lipid bilayers to reveal a role of polarizability in modulating chloroform partitioning thermodynamics via the chloroform-induced dipole moment and highlight competitive binding to the membrane core and toward the glycerol backbone that may have significant implications for understanding anesthetic action.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".