RepBPPL: An Integrative Network and Deep Learning Approach for Identification and Prioritization of Drug Repurposing Candidates against Bacterial Priority Pathogens

This repository contains data, code, and analysis from our study on An Integrative Network and Deep Learning Approach for Identification and Prioritization of Drug Repurposing Candidates against Bacterial Priority Pathogens. We apply our framework across 17 WHO Priority Pathogens from the 2024 BPPL list.

🧭 Project Overview

• Integrate GO-based functional annotations for biological context on raw drug target interaction data.
• Predict novel drug-target interactions (DTIs) for prioritized bacterial targets using NBI.
• Selecting candidate targets and drugs using multiple filtering criteria.
• Perform drug and target-space expansion for prioritised drugs and targets

🧬 Methodology Overview

🗂 Repository Structure

• data/: All input datasets including GO annotations, BLAST results,(except raw drugbank data) and their result
• scripts/: Core scripts for NBI scoring, GO filtering, BLAST parsing,etc
• assets/ : Figures and supporting information.