Dimension reduction in conformational analysis: a two-rotor mathematical model of amino acid diamide conformational potential energy surface
Bibliographic record
Abstract
The conformational potential energy surface (PES) of a molecule provides insights into the relative stability of the possible foldamers. However, the time and space complexity of electronic structure calculations, commonly used to generate PES, increases exponentially with an increasing number of atoms. The use of mathematical functions to model the topology of conformational PES is an alternative to more computer-intensive quantum chemical calculations, but the choice and complexity of functions used are crucial in achieving more accurate results. This paper presents a method to illustrate the topology of amino acid diamide PESs through a linear combination of a Fourier series and a mixture of Gaussian functions. Results yield a significantly small error, with an average RMSE of 4.9946 kJ mol−1 for all fits, which suggest that these functions may be used to represent the topology of the PESs, with around twofold order of magnitude decrease in computational time, with respect to DFT electronic structure calculations. This study ultimately aims to provide a foundation for a framework on building polypeptide PES from individual amino acid PESs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".