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PharmAgents2: Towards Autonomous End-to-End Drug Discovery — arXiv:2611.07653

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.07653
License (as archived): CC0-1.0
Archived: 2026-11-06T14:41:41.996Z
SHA-256 of the archived text: 31a45f74a50e16b03484fd62f638e489c7a22237e772a077974f449c18e609c9
Stable URL of this page: https://metraipolis.com/documento/dc_edec54089ba2

How to cite

PharmAgents2: Towards Autonomous End-to-End Drug Discovery — arXiv:2611.07653 — arXiv (en), https://arxiv.org/abs/2611.07653 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_edec54089ba2 (Metraipolis archive dc_edec54089ba2, sha256 31a45f74a50e16b03484fd62f638e489c7a22237e772a077974f449c18e609c9)

BibTeX

@misc{dcedec54089ba2,
  title = {PharmAgents2: Towards Autonomous End-to-End Drug Discovery — arXiv:2611.07653},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.07653},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_edec54089ba2; sha256 31a45f74a50e16b03484fd62f638e489c7a22237e772a077974f449c18e609c9},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.07653. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Quantitative Methods (q-bio.QM).
Pagina del lavoro: https://arxiv.org/abs/2611.07653 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-06T14:18:42.643Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).