DrugPilot: LLM-based Parameterized Reasoning Agent for Drug Discovery

TL;DR

DrugPilot is an innovative LLM-based agent system designed for drug discovery, integrating structured tools and a parameterized memory pool to enhance multi-stage scientific workflows and decision-making.

Contribution

It introduces a parameterized reasoning architecture with a memory pool for heterogeneous data, improving automation and data handling in drug discovery workflows.

Findings

Achieves high task completion rates of 98.0%, 93.5%, and 64.0% in different scenarios.

Outperforms state-of-the-art agents like ReAct and LoT.

Supports efficient multi-turn dialogue and complex scientific reasoning.

Abstract

Large language models (LLMs) integrated with autonomous agents hold significant potential for advancing scientific discovery through automated reasoning and task execution. However, applying LLM agents to drug discovery is still constrained by challenges such as large-scale multimodal data processing, limited task automation, and poor support for domain-specific tools. To overcome these limitations, we introduce DrugPilot, a LLM-based agent system with a parameterized reasoning architecture designed for end-to-end scientific workflows in drug discovery. DrugPilot enables multi-stage research processes by integrating structured tool use with a novel parameterized memory pool. The memory pool converts heterogeneous data from both public sources and user-defined inputs into standardized representations. This design supports efficient multi-turn dialogue, reduces information loss during data exchange, and enhances complex scientific decision-making. To support training and benchmarking, we construct a drug instruction dataset covering eight core drug discovery tasks. Under the Berkeley function-calling benchmark, DrugPilot significantly outperforms state-of-the-art agents such as ReAct and LoT, achieving task completion rates of 98.0%, 93.5%, and 64.0% for simple, multi-tool, and multi-turn scenarios, respectively. These results highlight DrugPilot's potential as a versatile agent framework for computational science domains requiring automated, interactive, and data-integrated reasoning.