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Advancing Antimicrobial Resistance Prediction Through Artificial Intelligence

Award Number: R01AI198596
ORGANIZATION: NATIONAL INSTITUTE OF ALLERGY & INFECTIOUS DISEASES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 08/20/2026
PERIOD OF PERFORMANCE END DATE: 07/31/2031

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Advancing Antimicrobial Resistance Prediction Through Artificial Intelligence - Antibiotic resistance (AR) is a critical global public health threat causing significant mortality and economic burden. In the U.S., AR infections lead to numerous deaths and billions in costs annually. Current AR detection methods are often slow or incomplete, delaying effective treatment. This project aims to advance AR prediction by integrating cutting-edge whole-genome sequencing (WGS) and artificial intelligence (Al). Our central goal is to develop rapid, comprehensive, and accurate AR prediction methods. We will employ Oxford Nanopore long-read sequencing to capture a wide array of genetic and epigenetic features from bacterial isolates, including SNPs, copy number variations (CNVs), structural variations (SVs), and DNA methylation. A customized bioinformatics pipeline will identify these features. The project has three aims: 1. Derive comprehensive molecular features for AR prediction: we will utilize nanopore reads to analyze a wide range of bacterial molecular features. These features will be used to create a new supervised contrastive learning algorithm that can predict AR phenotypes more accurately, even with smaller datasets. 2. Develop protein structure-guided AR prediction models: to address emerging resistance, we will create Graph-BAR, a framework using in silica predicted 3D protein structures (e.g., from AlphaFold) to assess the functional impact of bacterial genetic variants. This approach converts genetic features into structure-based features to predict AR, even for unobserved mutations. 3. Create open-source software and cloud infrastructure: we will develop user-friendly, open-source software and a scalable, cost-effective cloud-based platform for disseminating our bioinformatics pipelines and AR prediction models to researchers. Key innovations include the comprehensive use of Nanopore sequencing data, the novel protein structure-guided Al models, and an accessible cloud-based dissemination strategy. Successful completion will significantly enhance rapid and accurate AR prediction, empowering both researchers and clinicians. This work will provide critical tools and novel approaches to combat the urgent public health crisis of antibiotic resistance, and ultimately improve patient outcomes through rapid diagnosis leading to better informed antibiotic decision making.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $826,690 )
2026	2026	THE UNIVERSITY OF TEXAS SOUTHWESTERN MEDICAL CENTER	5323 HARRY HINES BLVD	DALLAS	TX	75390	DALLAS	USA	Allergy and Infectious Diseases Research	000	1	8/19/2026	NEW	$826,690
														Subtotal = $826,690

Grand Total All Awards = $826,690

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Advancing Antimicrobial Resistance Prediction Through Artificial Intelligence

Award Number: R01AI198596

ORGANIZATION: NATIONAL INSTITUTE OF ALLERGY & INFECTIOUS DISEASES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 08/20/2026

PERIOD OF PERFORMANCE END DATE: 07/31/2031

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