Explicitly Solvated Ligand Contribution to Continuum Solvation Models for Binding Free Energies: Selectivity of Theophylline Binding to an RNA Aptamer
Bibliographic record
Abstract
To provide more accurate computational estimates of binding free energies in solution from molecular dynamics (MD) simulations, a separate solvation contribution for the binding ligand is determined from a linear response treatment. We use explicit water coordinates for this term and combine with MM-PBSA (molecular mechanics, Poisson-Boltzmann, and surface area contributions) in a new approach (MM-PB/LRA-SA). To assess this method, application to the binding between theophylline and its derivatives to an RNA aptamer was performed and compared with experimental binding affinities. Explicitly solvated MD trajectories were generated with the same parameter set used in the previous work by Gouda et al., who compared the relative binding of these molecules by both the MM-PBSA and thermodynamic integration methods. Substituting the linear response term for the ligand in the MM-PB/LRA-SA approach led to an improvement upon MM-PBSA when compared with experimental and thermodynamic integration results at approximately twice the computational cost. The balance between accuracy and computational expense achieved using this method suggests potential advantages in applying it in the virtual drug-screening process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".