Sign Gradient Descent Algorithms for Accelerated Kinetostatic Protein Folding in Nanorobotics Design
Bibliographic record
Abstract
Numerical simulations of protein folding enable the design of protein-based nanomachines and nanorobots by predicting folded three-dimensional protein structures with high accuracy and revealing the protein conformation transitions during folding and unfolding. In the kinetostatic compliance method (KCM) for folding simulations, protein molecules are represented as ensembles of rigid nano-linkages connected by chemical bonds, and the folding process is driven by the kinetostatic influence of nonlinear interatomic force fields until the system converges to a free-energy minimum of the protein. Despite its strengths, the conventional KCM framework demands an excessive number of iterations to reach folded protein conformations, with each iteration requiring costly computations of interatomic force fields. To address these limitations, this work introduces a family of sign gradient descent (SGD) algorithms for predicting folded protein structures. Unlike the heuristic-based iterations of the conventional KCM framework, the proposed SGD algorithms rely on the sign of the free-energy gradient to guide the kinetostatic folding process. Owing to their faster and more robust convergence, the proposed SGD-based algorithms reduce the computational burden of interatomic force field evaluations required to reach folded conformations. Their effectiveness is demonstrated through numerical simulations of KCM-based folding of protein backbone chains.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".