FragDockRL grows synthesizable ligands from a docked core with reinforcement learning
- FragDock builds molecules from synthetically accessible building blocks via known reactions and scores them by tethered docking that restrains a predefined core pose.
- FragDockRL adds reinforcement learning: rewards from rDock tethered docking followed by SMINA scoring drive a modified Deep Q-Network that guides stepwise molecular growth.
- The method was evaluated on three therapeutically important protein targets, including a receptor tyrosine kinase.
💡 Points
Growing from a fixed core under synthesis constraints mirrors how fragment elaboration actually happens on a project, which beats free generative models that hand you unmakeable structures. Docking-based rewards remain the weak link because they reward size, so I'd judge it by whether the compounds are truly makeable and hold up against a prospective test.