DRIVE: A Data-Driven Platform for Disease Visualization and Drug Repurposing

Alejandro Rodríguez González, Lucía Prieto Santamaría, Belén Otero-Carrasco, Andrea Álvarez Pérez, David Juste Urraca

Abstract: The development of new drugs is a costly and time-consuming process, often spanning over a decade. Drug repurposing has emerged as a promising alternative, leveraging existing compounds to identify novel therapeutic uses. In this context, DRIVE (Data-dRiven platform for dIsease Visualization and Drug rEpurposing) is a platform designed to integrate and exploit heterogeneous biomedical data to support hypothesis generation in drug repurposing. Developed at the Medical Data Analytics Laboratory (MEDAL) of Universidad Politécnica de Madrid, DRIVE combines disease-centered network visualizations and six complementary computational methods, ranging from data-driven-based approaches to graph neural network models. The platform is powered by data from DISNET and integrates phenotypic, molecular, and pharmacological layers of knowledge. Users can interactively explore disease mechanisms, visualize multi-layer disease networks, and obtain ranked repurposing candidates through a web interface. This poster presents the platform architecture, discusses the methodologies implemented, and illustrates the capabilities of DRIVE through representative use cases.