Assessing and improving the performance of consensus docking strategies using the DockBox package
Bibliographic record
Abstract
Molecular docking is a well‐established computational method to predict how a ligand binds to a specific protein target as well as to assess the strength of the binding. Although docking programs are used worldwide for drug discovery, it is not always simple to identify which program or combination of programs provides the best results for a target of interest. Here we present DockBox, a computational package designed to facilitate the use and comparison of multiple docking and scoring programs at a time, and to combine them using different consensus strategies. A new consensus docking method called scoring‐based consensus docking (SBCD) is introduced, which significantly improves success rates in pose prediction as well as virtual‐screening (VS) hit rates compared to standard consensus docking. Several test cases are presented to illustrate the success of the SBCD method in docking and VS scenarios. SBCD requires the same computational cost as standard consensus methods in terms of number of required programs, computational power and time, making it a promising new approach for the screening of large chemical libraries. Presentation of the DockBox package Figure 1
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".