Bio-inspired optimization & sampling technique for side-chain packing in MCCE
Bibliographic record
Abstract
The prediction of proteins' conformation helps to understand their exhibited functions, \nallows for modeling and allows for the possible synthesis of the studied protein. Our \nresearch is focused on a sub-problem of protein folding known as side-chain packing. Its \ncomputational complexity has been proven to be NP-Hard. The motivation behind our \nstudy is to offer the scientific community a means to obtain faster conformation \napproximations for small to large proteins over currently available methods. As the size \nof proteins increases, current techniques become unusable due to the exponential nature \nof the problem. We investigated the capabilities of a hybrid genetic algorithm / simulated \nannealing technique to predict the low-energy conformational states of various sized \nproteins and to generate statistical distributions of the studied proteins' molecular \nensemble for pKa predictions. Our algorithm produced errors to experimental results \nwithin .acceptable margins and offered considerable speed up depending on the protein \nand on the rotameric states' resolution used.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".