This research introduces a collaborative approach between humans and large language models to enhance Structure-Based Drug Design (SBDD), addressing key limitations in current computational methods.

• Combines 3D structure-based models with LLMs' chemistry knowledge to create more viable drug candidates
• Demonstrates how LLMs can help prioritize drug-likeness alongside molecular fit
• Shows significant improvements in producing candidates that meet medicinal chemistry standards
• Establishes a framework for human-AI collaboration in drug discovery workflows

This approach could accelerate pharmaceutical development by reducing failed candidates and streamlining the discovery process, ultimately bringing effective treatments to patients faster.

Pushing the boundaries of Structure-Based Drug Design through Collaboration with Large Language Models