This research introduces a novel approach for teaching LLMs to effectively access and utilize structured scientific databases, overcoming a significant limitation in current AI systems.

• Combines reinforcement learning with database feedback to enhance LLMs' ability to leverage structured scientific data
• Demonstrates superior performance in molecular property prediction compared to conventional methods
• Creates a more scientifically-informed AI capable of reasoning with specialized knowledge
• Establishes a framework for incorporating centuries of accumulated scientific expertise into AI systems

For medicine and pharmaceutical research, this breakthrough enables more accurate drug discovery processes, better molecular property predictions, and helps bridge the gap between AI capabilities and specialized scientific knowledge domains.

RLDBF: Enhancing LLMs Via Reinforcement Learning With DataBase FeedBack