Abstract
Finding new uses for existing drugs, known as drug repurposing, is a widely adopted drug development strategy in the pharmaceutical industry. Computational drug repurposing leverages vast biomedical data to prioritize repurposing candidates. Once these candidates are prioritized, domain experts face the burden of evaluating their true potential. In this work, we propose a visualization-based approach to address this challenge for a multimodal class of computational drug repurposing, where heterogeneous evidence modalities are integrated. We conducted a design study in close collaboration with domain experts, from which we derived a domain abstraction of the expert assessment process. Grounded in this abstraction, we developed an interactive visualization approach that explicitly models the expert reasoning process. We applied the proposed approach to create a prototype implementation, molIEreVIS, in the context of an operational drug repurposing pipeline. We used this prototype to collect qualitative feedback from domain experts actively engaged in assessing computational drug repurposing candidates. The results demonstrate the potential of our approach to support insights and reasoning in this process and reveal directions for enhancements and future work.
Citation
Amal
Alnouri,
Andreas
Hinterreiter,
Christian A.
Steinparz,
Labinot Bajraktari,
Sebastian Burgstaller-Muehlbacher,
Markus Bauer,
Gregorio Alanis-Lobato,
Marc
Streit
molIEreVIS: Exploring and Interpreting the Evidence Behind Drug Repurposing Predictions
frontiers,
2026.
BibTeX
@article{,
title = {molIEreVIS: Exploring and Interpreting the Evidence Behind Drug Repurposing Predictions},
author = {Amal Alnouri and Andreas Hinterreiter and Christian A. Steinparz and Labinot Bajraktari and Sebastian Burgstaller-Muehlbacher and Markus Bauer and Gregorio Alanis-Lobato and Marc Streit},
journal = {frontiers},
month = {February},
year = {2026}
}
