PharmAgents2: Towards Autonomous End-to-End Drug Discovery

Authors: Haichuan Tan , Bowen Gao , Tao Huang , Jiaxin Tan , Xin Hong , Baoguo Yao , Yifei Wang , Zhichao Deng , Hanji Du , Dapeng Jiang , Yiheng Li , Weiwei Ma , Xin Ma , Yuqi Shi , Zixiang Wang , Kelin Wu , Tianxiang Wu , Wenxuan Xie , Jie Zang , Huiyu Zhang , Mojia Zhang , Yuying Zhang , Ya-Qin Zhang , Wei-Ying Ma , Yanyan Lan

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Abstract: Drug discovery requires continuous scientific decision-making across a vast research space. We introduce PharmAgents2, an autonomous system for end-to-end drug discovery that combines knowledge-enhanced decision-making with organized large-scale exploration. Paper2Skill extracts transferable scientific decision knowledge from public sources and, together with expert-developed skills, supports research decisions. Self-Scaling Discovery expands exploration through parallel agents inheriting research context and decision-making capabilities across targets, scientific hypotheses, and discovery strategies. We introduce PharmAgents-Bench v0.1, comprising 121 questions across nine decision scenarios and three end-to-end tasks assessed with expert-defined process rubrics. PharmAgents2 outperforms evaluated baselines in most decision categories and achieves the highest process scores on all three end-to-end tasks. In three real-world studies, autonomous investigations generated findings subsequently supported by human-executed experiments. For LpxH, PharmAgents2 hypothesized a previously unreported allosteric site and screened compounds against it, identifying a novel inhibitor scaffold with experimentally confirmed inhibitory activity and binding. For JAK1, it prioritized a covalent inhibition strategy and nominated compounds with experimentally demonstrated activity. Starting from a known bioactive molecule, Self-Scaling Discovery explored candidate targets across the proteome and identified a new binding target, with the interaction subsequently confirmed experimentally. PharmAgents2 represents a prototype of a virtual pharmaceutical company, marking a step toward autonomous end-to-end drug discovery.

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Artificial Intelligence (cs.AI) ; Quantitative Methods (q-bio.QM)

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arXiv:2611.07653 [cs.AI]

(or

arXiv:2611.07653v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.07653

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arXiv-issued DOI via DataCite (pending registration)

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From: Haichuan Tan [ view email ]

[v1]

Thu, 5 Nov 2026 08:39:19 UTC (19,231 KB)

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