Pose Ensemble Graph Neural Networks to Improve Docking Performances.
Bibliographic record
Abstract
The prediction of the geometry and strength governing small molecule-protein interactions remains a paramount challenge in drug discovery due to their complex and dynamic nature. A number of machine learning (ML) methods have been proposed to complement and improve on physics-based tools such as molecular docking, usually by mapping three dimensional features of individual poses to their closeness to experimental structures and/or to binding affinities. Here, we introduce Dockbox2 (DBX2), a novel approach that encodes ensembles of computational poses within a graph neural network architecture via simple energy-based features derived from molecular docking. The model was jointly trained to predict binding pose likelihood as a node-level task and binding affinity as a graph-level task using the PDBbind dataset and demonstrated significant performance in comprehensive, retrospective docking and virtual screening experiments. Our results encourage further exploration of ML models based on conformational ensembles to provide more accurate estimates of small molecule-protein interactions and thermodynamics. The DBX2 code is available at https://github.com/jp43/DockBox2.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".