Molecular ‘time‐machines’ to unravel key biological events for drug design
Why this work is in the frame
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Bibliographic record
Abstract
Molecular dynamics ( MD ) has become a routine tool in structural biology and structure‐based drug design ( SBDD ). MD offers extraordinary insights into the structures and dynamics of biological systems. With the current capabilities of high‐performance supercomputers, it is now possible to perform MD simulations of systems as large as millions of atoms and for several nanoseconds timescale. Nevertheless, many complicated molecular mechanisms, including ligand binding/unbinding and protein folding, usually take place on timescales of several microseconds to milliseconds, which are beyond the practical limits of standard MD simulations. Such issues with traditional MD approaches can be effectively tackled with new generation MD methods, such as enhanced sampling MD approaches and coarse‐grained MD ( CG‐MD ) scheme. The former employ a bias to steer the simulations and reveal biological events that are usually very slow, while the latter groups atoms as interaction beads, thereby reducing the system size and facilitating longer MD simulations that can witness large conformational changes in biological systems. In this review, we outline many of such advanced MD methods, and discuss how their applications are providing significant insights into important biological processes, particularly those relevant to drug design and discovery. WIREs Comput Mol Sci 2017, 7:e1306. doi: 10.1002/wcms.1306 This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Free Energy Methods Software > Simulation Methods
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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 it