# SBIR 136 - PREDICTIVE: Knowledge Graphs for Infectious Diseases

> **NIH NIH N43** · PREDICTIVE, LLC · 2024 · $298,262

## Abstract

Addressing this critical challenge of drug discovery for infectious disease caused by known of emerging pathogens can be catalyzed by developing structured knowledge about both biological mechanisms underlying the emergence and progression of ID and known drugs with established or hypothesized mechanism of action. This structured knowledge base can support the discovery of novel treatments. Using modern knowledge mining technologies, all available ID-relevant, including biomedical, chemogenomic, and clinical, data can be integrated and organized into a knowledge graph (KG)(1), which can be mined to reveal functional biological pathways involving ID-relevant targets as well as map relevant chemical space of bioactive compounds that can modulate ID targets. In this pilot project. using modern knowledge mining technologies, we will integrate and organize these diverse data into an Infectious Disease KG (IDKG), which can be mined to reveal functional biological pathways underlying the emergence and progression of ID.

## Key facts

- **NIH application ID:** 11214917
- **Project number:** 75N93024C00038-0-9999-1
- **Recipient organization:** PREDICTIVE, LLC
- **Principal Investigator:** ALEX TROPSHA
- **Activity code:** N43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $298,262
- **Award type:** —
- **Project period:** 2024-09-05 → 2025-09-04

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11214917

## Citation

> US National Institutes of Health, RePORTER application 11214917, SBIR 136 - PREDICTIVE: Knowledge Graphs for Infectious Diseases (75N93024C00038-0-9999-1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/11214917. Licensed CC0.

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