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

📰 ArXiv cs.AI

Learn how C-MORAL enables controllable multi-objective molecular optimization with reinforcement alignment for LLMs, improving drug design efficiency

advanced Published 28 Apr 2026

Action Steps

Apply reinforcement learning to fine-tune LLMs for molecular optimization
Use group-based relative optimization to handle heterogeneous objectives
Configure property score alignment for selective constraints
Implement continuous non-linear reward aggregation for improved optimization
Test C-MORAL on molecular optimization tasks to evaluate its performance

Who Needs to Know This

Pharmaceutical researchers and AI engineers can benefit from C-MORAL to optimize molecular structures for drug design, aligning with competing constraints

Key Insight

💡 C-MORAL combines reinforcement learning, group-based relative optimization, and property score alignment to improve molecular optimization for LLMs

Key Takeaways

Learn how C-MORAL enables controllable multi-objective molecular optimization with reinforcement alignment for LLMs, improving drug design efficiency

Full Article

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

Abstract:
arXiv:2604.23061v1 Announce Type: cross 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 continuous non-linear reward aggregation to improve

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