ReMedI: Inference-Time Refinement and Self-Distillation for Controllable Molecular Optimization

ReMedI introduces an inference‑time refinement framework that combines a pre‑trained molecular language model with a Metropolis‑Hastings search to perform multi‑objective optimization of drug leads. The method proposes edits conditioned on the current molecule and refines them to improve properties, preserve lead similarity, and maintain scaffold integrity. Experiments on C‑MuMOInstruct and SMDD benchmarks show that ReMedI outperforms RL‑based baselines, achieving higher success rates and requiring far fewer training leads.

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• llms
• reinforcement-learning
• fine-tuning
• efficiency
• benchmarks
• safety