MolLingo introduces a multi-agent framework with BFE molecular representation and docking-grounded reasoning to outperform frontier LLMs on molecular design benchmarks including fourfold docking score gains.
Reference- guided policy optimization for molecular optimization via llm reasoning.arXiv preprint arXiv:2603.05900, 2026
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Active-GRPO reaches 0.1773 average SRxSim on TOMG-Bench MOLOPT by adaptively switching between imitation and self-reinforcement while upgrading references, outperforming GRPO and RePO.
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MolLingo: Molecule-Native Representations for LLM-Powered Scientific Agents
MolLingo introduces a multi-agent framework with BFE molecular representation and docking-grounded reasoning to outperform frontier LLMs on molecular design benchmarks including fourfold docking score gains.
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Active-GRPO: Adaptive Imitation and Self-Improving Reasoning for Molecular Optimization
Active-GRPO reaches 0.1773 average SRxSim on TOMG-Bench MOLOPT by adaptively switching between imitation and self-reinforcement while upgrading references, outperforming GRPO and RePO.