Two IEEE ICEBE 2025 papers released on explainable AI for drug repurposing
We are excited to announce that Mohammed Ali, our CTO, and his collaborators at University of Technology Sydney (UTS) have had their groundbreaking research published in IEEE Xplore. The papers, presented at the IEEE International Conference on E-Business Engineering (ICEBE 2025), are now available for public access.
The first paper is titled "The Role of Explainable AI in Knowledge Graph-Based Drug Repurposing: Bridging Trust and Discovery" and the second, "Explainable AI for Knowledge Graph-Based Drug Repurposing: Methods, Challenges, and Interpretability Frameworks." These papers explore how Explainable AI (XAI) and Knowledge Graphs can be combined to accelerate drug repurposing by transforming complex biomedical data into explainable and reliable predictions.
Why This Matters for Biotech R&D
At Interstellar Consultation Services, we don't just build software—we build platforms grounded in rigorous, peer-reviewed science. These publications validate the core methodology that powers our approach: leveraging AI to accelerate R&D and create actionable insights from biomedical data. The research highlights how our platform uses Knowledge Graphs and XAI to help clients make data-driven decisions, turning fragmented data into actionable, interpretable, and trustworthy insights—an essential capability for biotech R&D.
We would like to extend a huge thank you to our collaborators at UTS, particularly Dr. Marwa Mustafa and Dr. Mohamed Awadallah, for their exceptional contributions to this work. Their dedication and expertise were pivotal to the success of these papers.
Bridging Academic Rigor with Production-Grade Software
The findings presented in these papers were also shared by Mohammed Ali at the IEEE ICEBE 2025 conference. The research showcases how we use Knowledge Graphs and XAI to accelerate drug repurposing, highlighting the same methodology we're currently implementing in our pilot projects.
At Interstellar, we are proud to bridge the gap between academic rigor and production-grade software, ensuring that the technologies we develop are not just theoretical but practical, ready for real-world applications.