Friday, December 8, 2023 NIAID-funded Study Uses Machine Learning + High-throughput Screening to Find Immune Modulators A 3D model of the Nuclear Factor Kappa B1 (NF-κB1) homodimer. NF-κB1 is a member of the NF-κB family, important immune modulators that are targeted by the small molecules developed in the study. (Credit: NIAID) Machine learning + high-throughput screening was used to find small molecules with big impacts on the innate immune system when used as vaccine adjuvants, reports a new article in Chemical Science. In the NIAID-funded study, researchers at the University of Chicago developed a screening pipeline aided by a novel artificial intelligence tool for the rapid discovery of immune modulators and built a library of small molecules that can tune innate immune pathways. The authors say this work could be used to rapidly reveal novel ways to improve vaccination and immune treatments. National Institute of Allergy and Infectious Diseases  |  National Institutes of Health Update your subscriptions, modify your password or e-mail address, or stop subscriptions at any time on your Subscriber Preferences Page. You will need to use your e-mail address to log in. If you have questions or problems with the subscription service, please contact Subscriber Help. This service is provided to you at no charge by National Institute of Allergy and Infectious Diseases (NIAID).                             This email was sent to myhcistech.healthnews360@blogger.com using GovDelivery Communications Cloud on behalf of: National Institute of Allergy and Infectious Diseases · 5601 Fishers Lane · Bethesda, MD 20892 · 1-866-284-4107