FragDockRL: A Reinforcement Learning Method for Fragment-Based Ligand Design via Building Block Assembly and Tethered Docking

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