MolReAct uses an LLM agent to dynamically constrain RL action spaces to validated reaction templates, achieving the highest average Top-10 score of 0.571 across 14 drug optimization tasks while providing explicit synthetic pathways.
Pepthink-r1: LLM for interpretable cyclic peptide optimization with cot SFT and reinforcement learning
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Reinforcement Learning with LLM-Guided Action Spaces for Synthesizable Lead Optimization
MolReAct uses an LLM agent to dynamically constrain RL action spaces to validated reaction templates, achieving the highest average Top-10 score of 0.571 across 14 drug optimization tasks while providing explicit synthetic pathways.