Redefining druggable targets with artificial intelligence

Karen Akinsanya; Mohammed AlQuraishi; Ann Boija; John Chodera; Anna Cichońska; Marzyeh Ghassemi; Martha Head; Wengong Jin; Warren A. Kibbe; Nevan Krogan; Michael V. LeVine; John Moult; Lynda Stuart; Georgia Tourassi; James Zou; Regina Barzilay; Olivier Elemento

doi:10.1038/s41587-025-02770-1

Redefining druggable targets with artificial intelligence

Karen Akinsanya, Mohammed AlQuraishi, Ann Boija, John Chodera, Anna Cichońska, Marzyeh Ghassemi, Martha Head, Wengong Jin, Warren A. Kibbe, Nevan Krogan, Michael V. LeVine, John Moult, Lynda Stuart, Georgia Tourassi, James Zou, Regina Barzilay, Olivier Elemento

Computing & Computational Sciences Dir

Research output: Contribution to journal › Comment/debate

Abstract

A vast landscape of ‘undruggable’ cancer targets remains beyond the reach of conventional therapeutic agents. Recent advances in artificial intelligence (AI), however, are challenging this paradigm. Synthesizing insights from a Cancer Moonshot workshop, we argue that systemically addressing the undruggable target space with AI requires a new conceptual framework. We highlight the failure of current target taxonomies and the need for benchmarking datasets, and re-evaluate clinical validation for novel AI-driven modalities.

Original language	English
Pages (from-to)	1416-1418
Number of pages	3
Journal	Nature Biotechnology
Volume	43
Issue number	9
DOIs	https://doi.org/10.1038/s41587-025-02770-1
State	Published - Sep 2025

Funding

The workshop whose insights are summarized here was organized as part of the Cancer Moonshot initiative through joint efforts of the National Cancer Institute (NCI), Department of Energy (DOE), Advanced Research Projects Agency for Health (ARPA-H), and Food and Drug Administration (FDA). We thank the workshop organizing committee (J. Couch, S. Hanlon, A. Kilianski, J. Klemm, R. Philip and A. Predith) and all participants for their valuable contributions to the discussions and insights that shaped this Comment. Special thanks to the staff at the Hubert Humphrey Building for hosting the workshop and providing logistical support. This work was partially supported by the Cancer Moonshot initiative. The views expressed in this Comment are those of the authors and do not necessarily reflect the official policy or position of any government agency or institution.

Access to Document

10.1038/s41587-025-02770-1

Cite this

@article{8169ffb1549b4c38b6b6cef65d2a4ea0,

title = "Redefining druggable targets with artificial intelligence",

abstract = "A vast landscape of {\textquoteleft}undruggable{\textquoteright} cancer targets remains beyond the reach of conventional therapeutic agents. Recent advances in artificial intelligence (AI), however, are challenging this paradigm. Synthesizing insights from a Cancer Moonshot workshop, we argue that systemically addressing the undruggable target space with AI requires a new conceptual framework. We highlight the failure of current target taxonomies and the need for benchmarking datasets, and re-evaluate clinical validation for novel AI-driven modalities.",

author = "Karen Akinsanya and Mohammed AlQuraishi and Ann Boija and John Chodera and Anna Cicho{\'n}ska and Marzyeh Ghassemi and Martha Head and Wengong Jin and Kibbe, \{Warren A.\} and Nevan Krogan and LeVine, \{Michael V.\} and John Moult and Lynda Stuart and Georgia Tourassi and James Zou and Regina Barzilay and Olivier Elemento",

note = "Publisher Copyright: {\textcopyright} Springer Nature America, Inc. 2025.",

year = "2025",

month = sep,

doi = "10.1038/s41587-025-02770-1",

language = "English",

volume = "43",

pages = "1416--1418",

journal = "Nature Biotechnology",

issn = "1087-0156",

publisher = "Nature Research",

number = "9",

}

TY - JOUR

T1 - Redefining druggable targets with artificial intelligence

AU - Akinsanya, Karen

AU - AlQuraishi, Mohammed

AU - Boija, Ann

AU - Chodera, John

AU - Cichońska, Anna

AU - Ghassemi, Marzyeh

AU - Head, Martha

AU - Jin, Wengong

AU - Kibbe, Warren A.

AU - Krogan, Nevan

AU - LeVine, Michael V.

AU - Moult, John

AU - Stuart, Lynda

AU - Tourassi, Georgia

AU - Zou, James

AU - Barzilay, Regina

AU - Elemento, Olivier

PY - 2025/9

Y1 - 2025/9

N2 - A vast landscape of ‘undruggable’ cancer targets remains beyond the reach of conventional therapeutic agents. Recent advances in artificial intelligence (AI), however, are challenging this paradigm. Synthesizing insights from a Cancer Moonshot workshop, we argue that systemically addressing the undruggable target space with AI requires a new conceptual framework. We highlight the failure of current target taxonomies and the need for benchmarking datasets, and re-evaluate clinical validation for novel AI-driven modalities.

AB - A vast landscape of ‘undruggable’ cancer targets remains beyond the reach of conventional therapeutic agents. Recent advances in artificial intelligence (AI), however, are challenging this paradigm. Synthesizing insights from a Cancer Moonshot workshop, we argue that systemically addressing the undruggable target space with AI requires a new conceptual framework. We highlight the failure of current target taxonomies and the need for benchmarking datasets, and re-evaluate clinical validation for novel AI-driven modalities.

UR - https://www.scopus.com/pages/publications/105013800469

U2 - 10.1038/s41587-025-02770-1

DO - 10.1038/s41587-025-02770-1

M3 - Comment/debate

C2 - 40830262

AN - SCOPUS:105013800469

SN - 1087-0156

VL - 43

SP - 1416

EP - 1418

JO - Nature Biotechnology

JF - Nature Biotechnology

IS - 9

ER -

Redefining druggable targets with artificial intelligence

Abstract

Funding

Access to Document

Other files and links

Fingerprint

Cite this