# Accelerating discovery of narrow-spectrum antibiotics for Lyme disease

> **NIH AI R01** · TUFTS UNIVERSITY BOSTON · 2026 · $859,278

## Abstract

PROJECT SUMMARY
The development of narrow-spectrum antibiotics against the Lyme disease-causing bacterium, Borrelia
burgdorferi has the potential to significantly alter current approaches to the treatment and prevention of Lyme
disease. Narrow-spectrum agents that affect only the target bacteria avoid issues of propagation of resistance
in off-target bacteria, alterations in microbiome, and overgrowth of pathogenic bacteria. However, physical
screening of compounds for activity against multiple bacteria such as through traditional high-throughput screens
is highly inefficient due to its very low “hit” rate for activity against B. burgdorferi (<0.3%). Even when “hits” are
found downstream testing for toxicity, studies of pharmacokinetics, and mechanisms of action are time-
consuming and costly. Advances in machine learning can accelerate narrow-spectrum antibiotic development
by more efficiently and comprehensively searching the vast potential space of small molecule candidates to
identify the optimal balance of inhibitory properties, bio-availability, and toxicity. In this project, we propose to
develop two modeling-based platforms to accelerate drug development efforts for Lyme disease using
combinations of computational and experimental approaches. The first platform will use machine learning to
design compounds with predicted activity against B. burgdorferi but not other bacteria using high throughput
screening data from B. burgdorferi, E. coli and S. aureus. This framework will be biologically and chemically
informed in an automated way using medical literature agents, predicted proteome binding, and machine learning
models of bioavailability and toxicity. The second platform will focus on speeding identification of mechanisms
of action of novel agents using a multi-omic profiling model across dimensions of morphology and transcriptional
response to known agents to generate predictive models. In the process of developing these tools, our work will
also produce d

## Key facts

- **NIH application ID:** 11319336
- **Project number:** 1R01AI197351-01
- **Recipient organization:** TUFTS UNIVERSITY BOSTON
- **Principal Investigator:** Bree Beardsley Aldridge; Maha  Farhat; Linden T Hu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** AI
- **Fiscal year:** 2026
- **Award amount:** $859,278
- **Award type:** 1
- **Project period:** 2026-02-19T00:00:00 → 2031-01-31T00:00:00

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11319336

## Citation

> US National Institutes of Health, RePORTER application 11319336, Accelerating discovery of narrow-spectrum antibiotics for Lyme disease (1R01AI197351-01). Retrieved via AI Analytics 2026-07-21 from https://api.ai-analytics.org/grant/nih/11319336. Licensed CC0.

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