Coupling Synthesis and 3D Geometry for Pocket-Conditioned Molecular Generation

Structure-based molecular generation can propose ligands for a target protein, but connecting their synthetic construction with their placement in the binding pocket remains challenging. We present SynTrace, a supervised model that couples synthesis decisions to the evolving three-dimensional state of a molecule in its pocket. The central idea is to learn reaction choices and coordinate evolution from a shared geometric representation, so that each synthesis decision can use the spatial state produced by earlier generation steps. We train this model by aligning synthesis routes with docked product poses, which provide paired action and coordinate targets without reward-based policy optimization. On 100 CrossDocked2020 test pockets, SynTrace generates 10,000 molecules with a median redocking score of −8.45 kcal/mol, a 45.49% AiZynthFinder solve rate using Enamine stock, and diversity of 0.889. Interventions show that synthesis decisions respond to relative ligand–pocket geometry and identify spatial interactions as the main source of pocket sensitivity on reference decision states.