PocketX: Preference Alignment for Protein Pockets Design through Group Relative Policy Optimization
Bibliographic record
Abstract
Abstract Designing protein pockets that target specific ligands is crucial for drug discovery and enzyme engineering. Although deep generative models show promise in proposing high-quality pockets, they are usually trained purely to match the data distribution and therefore overlook key biophysical properties, such as binding affinity, expression, and solubility, that ultimately determine developability and success. We introduce PocketX, an online reinforcement learning framework that explicitly aligns a generative model with desired biophysical properties. The framework first trains a base model that co-designs pocket structures and sequences conditioned on a target ligand, and then fine-tunes this model with Group Relative Policy Optimization (GRPO) to reward the desired attributes. Because GRPO employs group-relative rewards, it produces lower-variance policy updates, resulting in more stable and efficient learning than competing alignment strategies. Evaluated on the CrossDocked2020 benchmark, PocketX surpasses existing methods in metrics such as binding energy and evolutionary plausibility. Ablation studies further show that GRPO outperforms alternative alignment strategies, including Direct Preference Optimization (DPO), confirming GRPO’s effectiveness for biophysical property alignment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".