{"id":130783,"date":"2025-08-09T04:03:20","date_gmt":"2025-08-09T04:03:20","guid":{"rendered":"https:\/\/www.europesays.com\/us\/130783\/"},"modified":"2025-08-09T04:03:20","modified_gmt":"2025-08-09T04:03:20","slug":"3-1-million-nih-grant-backs-uta-teams-ai-push-in-antibody-drug-discovery-dallas-innovates","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/us\/130783\/","title":{"rendered":"$3.1 Million NIH Grant Backs UTA Team’s AI Push in Antibody Drug Discovery \u00bb Dallas Innovates"},"content":{"rendered":"

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[Image: Alena Butusava\/istockphoto]<\/p>\n

A University of Texas at Arlington computer science professor has secured $3.1 million in federal funding to tackle one of medicine\u2019s most time-consuming challenges: figuring out which antibodies will successfully bind to disease-causing targets.<\/strong><\/p>\n

Junzhou Huang, the Jenkins Garrett Endowed Professor in the Department of Computer Science and Engineering, will use the National Institutes of Health R01 award to accelerate the use of artificial intelligence in antibody drug discovery\u2014research the university says could strengthen responses to future pandemics.<\/p>\n

The grant supports Huang\u2019s work applying machine learning to predict how antibodies bind to viruses and other antigens, a critical first step in developing treatments for infectious and autoimmune diseases.<\/p>\n

Traditionally, it can take more than a decade and billions of dollars to bring a drug to market, UTA said.<\/p>\n

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Junzhou Huang<\/p>\n

\u201cThis project is about using AI to automate and improve the early stages of drug development, particularly antibody design,\u201d Huang said. \u201cIf we can predict the right binding interactions computationally, it could dramatically speed up the pipeline and lower the risks and costs of drug development.\u201d<\/p>\n

Huang\u2019s research builds on his team\u2019s high-ranking finish in an international AI protein structure prediction challenge, where they placed sixth overall and ranked first in the protein contact map prediction track, competing against institutions such as Google DeepMind and the University of Washington.<\/p>\n

The recognition led to collaborations with Tao Wang at UT Southwestern Medical Center and Jun Wang at New York University. Together, the team coauthored a paper published in Nature Cancer<\/a> and is working to bridge the gap between academic research and pharmaceutical applications.<\/p>\n

In addition to the NIH grant, Huang\u2019s lab recently received a $200,000 award from Johnson & Johnson to explore AI-based toxicology prediction, another early-stage step in drug development that can identify potential safety issues before clinical trials.<\/p>\n

\u201cThe goal is to shorten the response time to react to emerging diseases by enabling faster, AI-driven antibody development,\u201d Huang said. \u201cThis could make a huge difference the next time we face a public health crisis.\u201d<\/p>\n

UTA said the new funding is part of its expanding research portfolio in artificial intelligence and health science innovation.<\/p>\n

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