MoMUInstruct leverages Large Language Models to overcome limitations in traditional molecule optimization approaches, offering unprecedented scalability and generalizability.

• Creates the first high-quality instruction-tuning dataset for molecular optimization
• Enables multi-property optimization where traditional methods typically handle only 1-2 properties
• Demonstrates superior generalizability to novel optimization tasks without retraining
• Achieves better performance while requiring less computational resources

This breakthrough has significant implications for pharmaceutical development, potentially accelerating drug discovery by efficiently designing molecules with multiple desired therapeutic properties simultaneously.

Generalizing Large Language Models for Multi-property Molecule Optimization