FragDockRL: A Reinforcement Learning Method for Fragment-Based Ligand Design via Building Block Assembly and Tethered Docking
Bibliographic record
Abstract
Abstract Efficient exploration of combinatorial chemical space under synthetic constraints remains a central challenge in computational molecular design. Here, we present FragDock, a molecular design framework that combines building block (BB)-based virtual synthesis with tethered docking guided by a predefined core structure. FragDock defines a structured search space by assembling molecules from synthetically accessible BBs through known chemical reactions and evaluating candidates using tethered docking with a restrained core binding pose. Within this framework, we introduce FragDockRL, a reinforcement learning-based search method that uses docking-score-based rewards and a modified Deep Q-Network (DQN) to guide stepwise molecular growth. We evaluated FragDockRL on three protein targets, CSF1R, FA10, and VEGFR2, using training-cycle analysis and benchmark comparisons with One-Step Reaction, Random Search, Beam Search, and Monte Carlo Tree Search. FragDockRL progressively enriched molecules with favorable docking scores during learning and generated more cutoff-passing unique molecules than Random Search across all three targets, supporting the benefit of learning-guided prioritization. However, the best-performing search strategy was target-dependent: One-Step Reaction, FragDockRL, and Beam Search each showed advantages in different cases. Representative molecular case studies showed that selected compounds retained reference-like binding poses while introducing structural variation in peripheral regions. The reaction schemes used commercially available BBs and well-established medicinal chemistry transformations, supporting the synthetic plausibility of the selected compounds. Overall, FragDock provides a flexible framework for synthetically constrained, structure-guided molecular exploration, and FragDockRL offers a learning-guided search mode for productive candidate prioritization under limited generation budgets.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".