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Artificial intelligence foundation for therapeutic science
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“Initiatives like the DREAM Challenges,284 MoleculeNet,110 and Therapeutic Data Commons285 offer benchmark data sets across a wide spectrum of prediction tasks for pharmacology.”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [285]
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Metadata
- Authors
- Huang, K., Fu, T., Gao, W., Zhao, Y., Roohani, Y., Leskovec, J., Coley, C. W., Xiao, C., Sun, J., & Zitnik, M.
- Affiliations
- Harvard University (US)
- Georgia Institute of Technology (US)
- Massachusetts Institute of Technology (US)
- Carnegie Mellon University (US)
- Stanford Medicine (US)
- Stanford University (US)
- IQVIA (United States) (US)
- University of Illinois Urbana-Champaign (US)
- ORCID
- Venue
- Nature Chemical Biology
- Published
- 2022-09-21
- Volume
- 18
- Issue
- 10
- Pages
- 1033-1036
- ISSN
- 1552-4450 (ISSN-L)
- 1552-4469
- PMC
- PMC9529840
- OpenAlex
- W4296551289
- Cited by
- 207
- References
- 23
- Citations by year
- 2026: 51 · 2025: 58 · 2024: 44 · 2023: 51 · 2022: 3
- FWCI
- 20.36
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Chemical Biology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Human Genome Research Institute (U54HG010426)
- National Institute of Neurological Disorders and Stroke (R01NS107291)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence, drug discovery, therapeutic modalities
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