AI-augmented physics-based docking for antibody-antigen complex prediction
Bibliographic record
Abstract
MOTIVATION: Predicting the structure of antibody-antigen complexes is a challenging task with significant implications for the design of better antibody therapeutics. However, the levels of success have remained dauntingly low, particularly when high standards for model quality are required, a necessity for efficient antibody design. Artificial intelligence (AI) has significantly impacted the landscape of structure prediction for antibodies, both alone and in complex with their antigens. METHODS: We utilized AI-guided antibody modeling tools to generate ensembles displaying diversity in the complementarity-determining region (CDR) and integrated those into our previously published AlphaFold2-rescored docking pipeline, a strategy called AI-augmented physics-based docking. In this study, we also compare docking performance with AlphaFold and Boltz-1, the new state-of-the-art. We distinguish between two types of success tailored to specific downstream applications: (i) criteria sufficient for epitope mapping, where gross quality is adequate and can complement experimental techniques, and (ii) criteria for producing higher-quality models suitable for engineering purposes. RESULTS: We highlight that the quality of the ensemble is crucial for docking performance, that including too many models can be detrimental, and that prioritization of models is essential for achieving good performance. In a scenario analogous to docking using a crystallized antigen, our results robustly demonstrate the advantages of AI-augmented docking over AlphaFold2, further accentuated when higher standards in quality are imposed. Docking also shows improvements over Boltz-1, but those are less pronounced. Docking performance is still noticeably lower than AlphaFold3 in both epitope mapping and antibody design use cases. We observe a strong dependence on CDR-H3 loop length for physics-based tools on their ability to successfully predict. This helps define an applicability range where physics-based docking can be competitive to the newer generation of AI tools. AVAILABILITY AND IMPLEMENTATION: The AF2 rescoring scripts are available at github.com/gaudreaultfnrc/AF2-Rescoring.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".