This research addresses a critical gap in using Large Language Models for molecular generation: the lack of structural diversity in generated molecules, which is essential for effective drug discovery.

• LLMs show impressive performance in generating molecules but tend to produce similar structures
• The authors propose novel methods to align LLMs with structural diversity requirements
• Diverse molecular generation significantly improves odds of finding viable drug candidates
• The approach provides pharmaceutical researchers with alternative molecules that can succeed where others fail

This advancement matters for medical applications by potentially accelerating drug discovery timelines and improving success rates through diverse candidate generation, ultimately reducing costs and bringing treatments to patients faster.

Can LLMs Generate Diverse Molecules? Towards Alignment with Structural Diversity