Molecular optimization stops at one turn: MFG trains an LLM on multi-turn feedback and reaches 81.50% average similarity-constrained success across ten tasks
Multi-objective molecular optimization asks a single generation to improve several properties at once while keeping the scaffold similar, yet earlier LLM-based methods either injected previous-turn feedback into the prompt at inference without training for it, or trained only on non-iterative single-turn generation. MFG recasts the task as a multi-turn problem with feedback and uses reinforcement learning to train a model that exploits each turn's property changes and similarity to the source molecule; built on GeLLM3O, GeLLM3O-MFG reaches an average similarity-constrained success rate of 81.50% across ten MuMOInstruct tasks.