# Retrosynthesis Prediction

The study introduces "trap molecules," an adversarial evaluation idea in which an AI agent uses reaction SMARTS templates and USPTO reaction records to deliberately design molecules carrying sensitive groups, near-duplicate reactive sites or bulky groups that lure retrosynthesis algorithms into characteristic errors. On 100 chemoselectivity, 100 regioselectivity and 27 steric trap molecules, three algorithms built on different principles, UAlign, SimpRetro and RetroChimera, all showed clear weaknesses in their top-20 predictions, with overall problem rates of 66%–77%, 85%–93% and 92.6%–100% for the three problem types.

The study introduces "trap molecules," an adversarial evaluation idea in which an AI agent uses reaction SMARTS templates and USPTO reaction records to deliberately design molecules carrying sensitive groups, near-duplicate reactive sites or bulky groups that lure retrosynthesis algorithms into characteristic errors. On 100 chemoselectivity, 100 regioselectivity and 27 steric trap molecules, three algorithms built on different principles, UAlign, SimpRetro and RetroChimera, all showed clear weaknesses in their top-20 predictions, with overall problem rates of 66%–77%, 85%–93% and 92.6%–100% for the three problem types.

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.