Artificial intelligence foundation for therapeutic science

被引：72

作者：

Huang, Kexin ^{[1
,6
]}

Fu, Tianfan ^{[2
]}

Gao, Wenhao ^{[3
]}

Zhao, Yue ^{[4
]}

Roohani, Yusuf ^{[5
]}

Leskovec, Jure ^{[6
]}

Coley, Connor W. ^{[3
]}

Xiao, Cao ^{[7
]}

Sun, Jimeng ^{[8
]}

Zitnik, Marinka ^{[9
,10
,11
]}

机构：

[1] Harvard Univ, Hlth Data Sci Program, Boston, MA 02115 USA

[2] Georgia Inst Technol, Coll Comp, Atlanta, GA 30332 USA

[3] MIT, Dept Chem Engn, Cambridge, MA 02139 USA

[4] Carnegie Mellon Univ, Heinz Coll, Pittsburgh, PA 15213 USA

[5] Stanford Univ, Sch Med, Stanford, CA USA

[6] Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA

[7] IQVIA, Analyt Ctr Excellence, Cambridge, MA USA

[8] Univ Illinois, Dept Comp Sci, Champaign, IL USA

[9] Harvard Univ, Harvard Med Sch, Dept Biomed Informat, Boston, MA 02115 USA

[10] Broad Inst MIT & Harvard, Cambridge, MA 02142 USA

[11] Harvard Data Sci Initiat, Cambridge, MA 02138 USA

来源：

NATURE CHEMICAL BIOLOGY | 2022年 / 18卷 / 10期

基金：

美国国家科学基金会; 美国国家卫生研究院;

关键词：

D O I：

10.1038/s41589-022-01131-2

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

Artificial intelligence (AI) is poised to transform therapeutic science. Therapeutics Data Commons is an initiative to access and evaluate AI capability across therapeutic modalities and stages of discovery, establishing a foundation for understanding which AI methods are most suitable and why.

引用

页码：1033 / 1036

页数：4

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