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MFG (Multi-turn Feedback Guided Molecular Optimization) is a new framework that formulates multi-turn feedback-guided molecular optimization as a reinforcement learning problem.
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10/14/2026
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UnverifiedGeLLM³O is an advanced LLM for multi-objective molecular optimization; it already possesses single-turn multi-objective optimization capability, but lacks an explicit training mechanism for utilizing historical feedback in multi-turn iteration.69% similarUnverifiedThe MFG framework contains two key designs: the Feedback Vector and the Feedback-aligned Reward function.65% similarUnverifiedThe core task of retrosynthesis analysis is, given a target molecule, to work backward to feasible synthetic routes; PostEra's approach is to model reaction prediction as a sequence-to-sequence or graph-to-graph problem, training models on large-scale reaction datasets (such as USPTO patent reaction data).62% similarUnverifiedMost existing LLM molecular optimization methods focus on single-turn generation, ignoring the fact that real medicinal chemistry optimization is usually multi-turn iterative.61% similarUnverifiedLLM的模式坍缩主要由自回归生成机制与RLHF优化的组合效应驱动，可从解码策略、训练目标和提示设计三个维度入手61% similar
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