Artificial intelligence foundation for therapeutic science

Kexin Huang; Tianfan Fu; Wenhao Gao; Yue Zhao; Yusuf Roohani; Jure Leskovec; Connor W Coley; Cao Xiao; Jimeng Sun; Marinka Zitnik

doi:10.1038/s41589-022-01131-2

Artificial intelligence foundation for therapeutic science

Nat Chem Biol. 2022 Oct;18(10):1033-1036. doi: 10.1038/s41589-022-01131-2.

Authors

Kexin Huang^#^{1

2}, Tianfan Fu^#³, Wenhao Gao^#⁴, Yue Zhao⁵, Yusuf Roohani⁶, Jure Leskovec², Connor W Coley⁴, Cao Xiao⁷, Jimeng Sun⁸, Marinka Zitnik^{9

10

11}

Affiliations

¹ Health Data Science Program, Harvard University, Boston, MA, USA.
² Department of Computer Science, Stanford University, Stanford, CA, USA.
³ College of Computing, Georgia Institute of Technology, Atlanta, GA, USA.
⁴ Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁵ Heinz College, Carnegie Mellon University, Pittsburgh, PA, USA.
⁶ School of Medicine, Stanford University, Stanford, CA, USA.
⁷ Analytics Center of Excellence, IQVIA, Cambridge, MA, USA.
⁸ Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
⁹ Department of Biomedical Informatics, Harvard Medical School, Harvard University, Boston, MA, USA. marinka@hms.harvard.edu.
¹⁰ Broad Institute of MIT and Harvard, Cambridge, MA, USA. marinka@hms.harvard.edu.
¹¹ Harvard Data Science Initiative, Cambridge, MA, USA. marinka@hms.harvard.edu.

^# Contributed equally.

Abstract

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

Grants and funding

R01 NS107291/NS/NINDS NIH HHS/United States