C-MORAL: Controllable Multi-Objective Molecular Optimization with Reinforcement Alignment for LLMs
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910262858088448 |
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| author | Gao, Rui Jeon, Youngseung Roy, Swastik Ziyadi, Morteza Chen, Xiang 'Anthony' |
| author_facet | Gao, Rui Jeon, Youngseung Roy, Swastik Ziyadi, Morteza Chen, Xiang 'Anthony' |
| contents | 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 for controllable multi-objective molecular optimization. C-Moral combines group-based relative optimization, property score alignment for heterogeneous objectives, and bottleneck-sensitive non-linear reward aggregation to improve stability across competing molecular properties. Experiments on C-MuMOInstruct and S$^2$-Bench MolOpt show that C-Moral achieves the best performance among compared methods on both benchmarks. On C-MuMOInstruct, C-Moral achieves the best Success Optimized Rate (SOR) of 48.9\% on in-domain tasks and 39.5\% on out-of-domain tasks while preserving scaffold similarity. On S$^2$-Bench MolOpt, it also achieves the strongest results across LogP, MR, and QED optimization tasks. These results suggest that C-Moral is an effective way to align molecular LLMs with continuous and constrained molecular design objectives. Our code and models are publicly available at https://github.com/Rwigie/C-MORAL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_23061 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | C-MORAL: Controllable Multi-Objective Molecular Optimization with Reinforcement Alignment for LLMs Gao, Rui Jeon, Youngseung Roy, Swastik Ziyadi, Morteza Chen, Xiang 'Anthony' Machine Learning Artificial Intelligence 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 for controllable multi-objective molecular optimization. C-Moral combines group-based relative optimization, property score alignment for heterogeneous objectives, and bottleneck-sensitive non-linear reward aggregation to improve stability across competing molecular properties. Experiments on C-MuMOInstruct and S$^2$-Bench MolOpt show that C-Moral achieves the best performance among compared methods on both benchmarks. On C-MuMOInstruct, C-Moral achieves the best Success Optimized Rate (SOR) of 48.9\% on in-domain tasks and 39.5\% on out-of-domain tasks while preserving scaffold similarity. On S$^2$-Bench MolOpt, it also achieves the strongest results across LogP, MR, and QED optimization tasks. These results suggest that C-Moral is an effective way to align molecular LLMs with continuous and constrained molecular design objectives. Our code and models are publicly available at https://github.com/Rwigie/C-MORAL. |
| title | C-MORAL: Controllable Multi-Objective Molecular Optimization with Reinforcement Alignment for LLMs |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2604.23061 |