Numerical Errors in Minimization Based Binding Energy Calculations
Bibliographic record
Abstract
This work examines the effect of small input perturbations on binding energies computed from differences between energy minimized structures, such as the Prime MM-GBSA and MOE MM-GB/VI methods. The applied perturbations include translations of the cognate ligand in the binding site by a maximum of 0.1 Å along each coordinate or the permutation of the order of atoms of the cognate ligand without any changes to the atom coordinates. These seemingly inconsequential input changes can lead to as much as 17 kcal/mol differences in the computed binding energy. The calculated binding energies cluster around discrete values, which correspond to specific ligand poses. It appears that the largest variations are observed for target-ligand systems in which there is a possibility for multiple poses with strong hydrogen bonds. The barriers between different poses can appear fractal-like, making it difficult to predict which solution will be produced from a given input. Including protein flexibility in MM-GBSA calculations further increases numerical instability, and the protein strain terms seem to be the major factor contributing to this sensitivity. In such calculations it appears unwise to extend the flexible region beyond 6 Å.
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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 | 0.000 | 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 | 0.000 | 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".