This research introduces a multi-agent LLM system that improves drug-target interaction (DTI) prediction through collaborative reasoning among specialized AI agents.

• Leverages multiple AI perspectives to overcome single-LLM limitations in complex biological contexts
• Implements a structured framework where specialized agents contribute domain expertise
• Demonstrates improved consistency and reliability in predicting how drugs interact with biological targets
• Provides a novel approach to accelerate early-stage drug discovery and reduce development costs

This innovation addresses critical challenges in pharmaceutical research by enabling more accurate prediction of drug interactions with biological systems, potentially streamlining the identification of promising therapeutic candidates and reducing failed clinical trials.

DrugAgent: Multi-Agent Large Language Model-Based Reasoning for Drug-Target Interaction Prediction