C-MORAL: Controllable Multi-Objective Molecular Optimization with Reinforcement Alignment for LLMs
arXiv:2604.23061v2 Announce Type: replace 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 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
The proliferation of large language models (LLMs) is prompting research into their application for complex, multi-objective optimization problems, particularly in drug discovery.
Improving molecular optimization through AI holds significant promise for accelerating drug discovery, materials science, and synthetic biology, potentially collapsing development timelines and costs.
The ability to align LLMs with complex, competing drug-design constraints could lead to a new paradigm in molecular engineering, making the design process more efficient and controllable.
- · Pharmaceutical companies
- · Biotech firms
- · AI researchers in chemistry
- · Synthetic biology sector
More efficient discovery of novel drug candidates and materials.
Reduced R&D costs in fields reliant on molecular design, accelerating innovation cycles.
The democratization of advanced molecular design capabilities, potentially leading to a boom in personalized medicine and bespoke materials.
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