# RetroReasoner

Retrosynthesis prediction shifts from emitting reactants directly to a four-stage reasoning trace that mirrors a chemist's disconnection strategy: product analysis, key-substructure identification, strategic bond disconnection, and synthetic-equivalent mapping. The model is fine-tuned on structured rationales from SyntheticRetro, then trained with reinforcement learning on a round-trip reward that passes predicted reactants through a forward synthesis model and rewards reconstructions of the original product. On the 500-instance ORDerly main test set, RetroReasoner (RL) reaches Exact@1 of 0.526 and Round-trip@1 of 0.826; on the rare atom/token subset it reaches a feasible ratio of 0.557 and template diversity of 2.97, above the same-architecture Prediction-Only (RL) at 0.478 and 1.97.

Retrosynthesis prediction shifts from emitting reactants directly to a four-stage reasoning trace that mirrors a chemist's disconnection strategy: product analysis, key-substructure identification, strategic bond disconnection, and synthetic-equivalent mapping. The model is fine-tuned on structured rationales from SyntheticRetro, then trained with reinforcement learning on a round-trip reward that passes predicted reactants through a forward synthesis model and rewards reconstructions of the original product. On the 500-instance ORDerly main test set, RetroReasoner (RL) reaches Exact@1 of 0.526 and Round-trip@1 of 0.826; on the rare atom/token subset it reaches a feasible ratio of 0.557 and template diversity of 2.97, above the same-architecture Prediction-Only (RL) at 0.478 and 1.97.