ConfBuster: Open-Source Tools for Macrocycle Conformational Search and Analysis
Bibliographic record
Abstract
Macrocycles are cyclic macromolecules that have gained an increased interest in drug development. To our knowledge, the current bioinformatics tools that are available to investigate and predict macrocycles 3D conformations are limited in their availability. In this paper, we introduce ConfBuster, a suite of tools written in Python with the goal of sampling the lower energy conformations of macrocycles. The suite also includes tools for the analysis and visualisation of the conformational search results. Coordinate sets of single molecules in MOL2 or PDB format are required as input, and a set of lower energy conformation coordinates is returned as output, as well as PyMOL script and graphics for results analysis. In addition to Python and the optional R programming languages with freely available packages, the tools require Open Babel and PyMOL to work properly. For several examples, ConfBuster found macrocycle conformations that are within few tenths of Å of the experimental structures in minutes. To our knowledge, this is the only open-source tools for macrocycle conformational search available to the scientific community
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.016 |
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".