# Trap Molecules

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.