The Drug Repurposing Hub: a next-generation drug library and information resource

By S. M. Corsello, Joshua A. Bittker, Zihan Liu, Joshua Gould, P. McCarren, J. Hirschman, Stephen E. Johnston, Anita Vrcic, Bang Wong, Mariya Khan and 5 more. Nature Medicine, 2017

904 citations recorded by the source corpus.

is enriched in drugs targeting kinases, GPCRs, and ion channels.

Subfields: Computational Theory and Mathematics, Oncology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/b6bbfe6096b57d05b8c4bbd8e2099925ebffdf37

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Tianfan Fu
2. Jimeng Sun
3. Kexin Huang
4. Cao Xiao
5. Marinka Zitnik
6. Lucas Glass
7. Emre Kıcıman
8. Kit-Kay Mak

In Oncology

1. Nathanael S. Gray
2. J. Lamb

In Pharmacology

1. Cao Xiao
2. Jimeng Sun
3. Marinka Zitnik
4. Tianfan Fu
5. Kexin Huang

Citations in the corpus

• Discovery of a structural class of antibiotics with explainable deep learning (2023, background): “…that it identifies potential antibiotics from large chemical libraries, resulting in the discovery of halicin 1 and abaucin 14 from the Drug Repurposing Hub 17 (comprising around 6,000 molecules) and other antibacterial compounds from approximately 107 million molecules in the ZINC15 library 18 .”
• G Protein-Coupled Receptors as Targets for Approved Drugs: How Many Targets and How Many Drugs? (2018, methodology): “As additional studies such as the Broad repurposing initiative (Corsello et al., 2017) characterize GPCR-drug interactions in more detail, we anticipate a growth in this number, as secondary interactions between GPCRs and drugs are defined (Allen and Roth, 2011).”
• Empowering Biomedical Discovery with AI Agents (2024, neutral): “Another challenge is the lack of standardization in biomedical discovery workflows, including data generation protocols that vary based on factors like disease cell lines, dosage levels, and time points [190].”
• Artificial intelligence in drug development: present status and future prospects. (2019, background): “The concept of applying an existing therapeutic to a new disease is advantageous because the new drug is qualified go directly to Phase II trials for a different indication without having to pass through Phase I clinical trials and toxicology testing again [58,59].”
• A Next Generation Connectivity Map: L1000 platform and the first 1,000,000 profiles (2017, positive): “This led to 58,820 expected relationships that could plausibly be recovered in the CMap-L1000v1 compendium (see Methods and screening library publication for details of annotations) (Corsello et al., 2017).”
• A Social Media Study on the Effects of Psychiatric Medication Use (2019, background): “Drug repurposing— finding new clinical applications for currently approved medications, offering the potential of low cost and quicker to market treatments (Corsello et al. 2017).”
• DeepPurpose: a deep learning library for drug–target interaction prediction (2020, background): “, 2019], and a drug repurposing library [Corsello et al., 2017].”