The rise of agentic AI in drug discovery from static tools to autonomous virtual scientists

Driven by large language models, AI agents are transforming drug discovery, enabling AI to break free from task-specific tools and advance toward the vision of autonomous virtual scientists. This perspective reviews applications across the R&D lifecycle. To evaluate system maturity, we introduce a 6-level “Spectrum of Agentic Autonomy.” We advocate for novel evaluation paradigms centered on full-chain traceability and outline adoption barriers, positioning AI agents as pragmatic scientist-in-the-loop co-pilots.

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Publication Details

Journal
npj Drug Discovery.
Published
2026-10-12
DOI
https://doi.org/10.1038/s44386-026-00075-0
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.00
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article

The rise of agentic AI in drug discovery from static tools to autonomous virtual scientists

Yuchen Zhu, Tingjun Hou, Hongxia Xu, Meijing Fang et al.
npj Drug Discovery.
Computational Drug Discovery Methods
article

The rise of agentic AI in drug discovery from static tools to autonomous virtual scientists

Yuchen Zhu, Tingjun Hou, Hongxia Xu, Meijing Fang, Lanqing Li, Lexiao Mao, Donghai Zhao, Hui Shi, Qun Su, Chang-Yu Hsieh
article en

Abstract

Driven by large language models, AI agents are transforming drug discovery, enabling AI to break free from task-specific tools and advance toward the vision of autonomous virtual scientists. This perspective reviews applications across the R&D lifecycle. To evaluate system maturity, we introduce a 6-level “Spectrum of Agentic Autonomy.” We advocate for novel evaluation paradigms centered on full-chain traceability and outline adoption barriers, positioning AI agents as pragmatic scientist-in-the-loop co-pilots.

npj Drug Discovery.Vol. 3(1)
Chinese University of Hong Kong (HK), Zhejiang Lab (CN), Shanghai Innovation Institute (CN), Zhejiang University (CN)
Openalex Percentile: Top 13%
Computational Drug Discovery Methods
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