DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaboration

• URL: http://arxiv.org/abs/2411.15692v1
• Date: Sun, 24 Nov 2024 03:06:59 GMT
• Title: DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaboration
• Authors: Sizhe Liu, Yizhou Lu, Siyu Chen, Xiyang Hu, Jieyu Zhao, Tianfan Fu, Yue Zhao,
• Abstract summary: We introduce DrugAgent, a multi-agent framework aimed at automating machine learning (ML) programming in drug discovery. DrugAgent incorporates domain expertise by identifying specific requirements and building domain-specific tools, while systematically exploring different ideas to find effective solutions. For example, DrugAgent is able to complete the ML programming pipeline end-to-end, from data acquisition to performance evaluation for the ADMET prediction task, and finally select the best model.
• Score: 31.892593155710625
• License:
• Abstract: Recent advancements in Large Language Models (LLMs) have opened new avenues for accelerating drug discovery processes. Despite their potential, several critical challenges remain unsolved, particularly in translating theoretical ideas into practical applications within the highly specialized field of pharmaceutical research, limiting practitioners from leveraging the latest AI development in drug discovery. To this end, we introduce DrugAgent, a multi-agent framework aimed at automating machine learning (ML) programming in drug discovery. DrugAgent incorporates domain expertise by identifying specific requirements and building domain-specific tools, while systematically exploring different ideas to find effective solutions. A preliminary case study demonstrates DrugAgent's potential to overcome key limitations LLMs face in drug discovery, moving toward AI-driven innovation. For example, DrugAgent is able to complete the ML programming pipeline end-to-end, from data acquisition to performance evaluation for the ADMET prediction task, and finally select the best model, where the random forest model achieves an F1 score of 0.92 when predicting absorption using the PAMPA dataset.

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