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.
Authors
- Yuchen Zhu
- Tingjun Hou (ORCID: https://orcid.org/0000-0001-7227-2580)
- Hongxia Xu (ORCID: https://orcid.org/0009-0003-9933-3998)
- Meijing Fang (ORCID: https://orcid.org/0009-0009-4509-9212)
- Lanqing Li
- Lexiao Mao
- Donghai Zhao
- Hui Shi
- Qun Su
- Chang-Yu Hsieh
Institutions
- Chinese University of Hong Kong (HK)
- Zhejiang Lab (CN)
- Shanghai Innovation Institute (CN)
- Zhejiang University (CN)
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