Loading...
Loading...
Browse all stories on DeepNewz
VisitWhich institution will primarily collaborate with Kempner Institute on TxGNN by end of 2024?
Harvard Medical School • 25%
MIT • 25%
Stanford University • 25%
Other • 25%
Official press releases or announcements from the Kempner Institute or collaborating institutions
Open-Access AI Model TxGNN Repurposes Drugs for Rare Diseases Affecting 300 Million
Sep 25, 2024, 01:26 PM
Artificial intelligence (AI) is making significant strides in the field of drug discovery, particularly in repurposing existing drugs for rare diseases. A new, open-access AI model, TxGNN, developed by researchers at the Kempner Institute and Harvard Medical School, has been highlighted in a study published in Nature Medicine. This model is designed to identify potential therapies from existing medicines for thousands of diseases, including those with no or limited treatment options. TxGNN has been trained on data from 17,080 diseases and 7,957 drugs, enabling it to predict drug candidates for conditions that currently lack FDA-approved treatments. The model's ability to repurpose drugs is seen as a breakthrough, potentially benefiting the 300 million people worldwide affected by rare diseases.
View original story
Jameel Clinic • 25%
Harvard University • 25%
MIT • 25%
Other • 25%
MIT • 25%
Stanford • 25%
Harvard • 25%
Other • 25%
Stanford University • 25%
Harvard University • 25%
University of California, Berkeley • 25%
Carnegie Mellon University • 25%
Pfizer • 25%
Merck • 25%
Johnson & Johnson • 25%
Other • 25%
European Space Agency • 25%
Japanese Aerospace Exploration Agency • 25%
Russian Space Agency • 25%
Indian Space Research Organisation • 25%
Stanford University • 25%
MIT • 25%
UC Berkeley • 25%
Other • 25%
MIT • 25%
Harvard Medical School • 25%
Massachusetts General Hospital • 25%
Other • 25%
Yes, with one institution • 25%
Yes, with two institutions • 25%
Yes, with three or more institutions • 25%
No • 25%
MIT • 25%
Stanford University • 25%
Max Planck Institute • 25%
Other • 25%
MIT • 25%
Harvard • 25%
Stanford • 25%
Other • 25%
Yes • 50%
No • 50%
Yes • 50%
No • 50%
Yes • 50%
No • 50%
Drug A • 25%
Other • 25%
Drug C • 25%
Drug B • 25%