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LLMs enhanced with reinforcement learning for controllable molecular optimization

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Researchers have introduced C-MORAL, a new reinforcement learning framework designed to enhance the capabilities of large language models in molecular optimization. This framework addresses the challenge of aligning LLMs with complex and competing drug design constraints by employing group-based relative optimization and continuous reward aggregation. Experiments on the C-MuMOInstruct benchmark demonstrated that C-MORAL significantly outperforms existing models, achieving a Success Optimized Rate of 48.9% on in-domain tasks and 39.5% on out-of-domain tasks while maintaining scaffold similarity. AI

IMPACT Introduces a novel RL post-training method to improve LLM performance on complex molecular design tasks.

RANK_REASON This is a research paper detailing a new framework for LLMs in molecular optimization.

Read on arXiv cs.LG →

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LLMs enhanced with reinforcement learning for controllable molecular optimization

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This is a research paper detailing a new framework for LLMs in molecular optimization.
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Rui Gao, Youngseung Jeon, Swastik Roy, Morteza Ziyadi, Xiang 'Anthony' Chen ·

    C-MORAL: Controllable Multi-Objective Molecular Optimization with Reinforcement Alignment for LLMs

    arXiv:2604.23061v1 Announce Type: new Abstract: Large language models (LLMs) show promise for molecular optimization, but aligning them with selective and competing drug-design constraints remains challenging. We propose C-Moral, a reinforcement learning post-training framework f…

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