Comprehensive Evaluation of End-Point Free Energy Methods in DNA–Ligand Interaction Predictions
Bibliographic record
Abstract
Deoxyribonucleic acid (DNA) serves as a repository of genetic information in cells and is a critical molecular target for various antibiotics and anticancer drugs. A profound understanding of small molecule interaction with DNA is crucial for the rational design of DNA-targeted therapies. While the molecular mechanics/Poisson–Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) approaches have been well established for predicting protein–ligand binding, their application to DNA–ligand interactions has been less explored. In this study, we systematically investigated the binding of 13 diverse small molecules to DNA, evaluating the accuracy of DNA–ligand interaction predictions across different solvation approaches, interior dielectric constants (ε in ), and molecular force fields. Our results demonstrate that MM/PBSA, using energy-minimized structures (the bsc1 force field and ε in = 20), provides the best correlation ( R p = −0.742) with experimental binding affinities, surpassing the performance of rDock scoring functions (best R p = −0.481). Notably, the interior dielectric constant was found to significantly impact DNA–ligand binding free energy predictions, especially for MM/PBSA. Moreover, both MM/PBSA and MM/GBSA predictions (ε in = 16 or 20) exhibited superior performance in distinguishing native-like binding modes within the top-10 poses from decoys, compared to the molecular docking tools used in this study. However, the popular docking software PLANTS demonstrates notable efficacy in predicting the top-1 binding pose. Given the considerably higher computational cost of MM/PBSA, MM/GBSA rescoring with higher ε in = 16 or 20 is more efficient for recognizing the native-like binding poses for DNA–ligand systems. This study presents the first detailed exploration of end-point free energy calculations in the context of DNA–ligand interactions and offers valuable insights for the application of the MM/PB(GB)SA methods in this domain.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".