  Drug-target interaction (DTI) prediction is a challenging, albeit essential
task in drug repurposing. Learning on graph models have drawn special attention
as they can significantly reduce drug repurposing costs and time commitment.
However, many current approaches require high-demanding additional information
besides DTIs that complicates their evaluation process and usability.
Additionally, structural differences in the learning architecture of current
models hinder their fair benchmarking. In this work, we first perform an
in-depth evaluation of current DTI datasets and prediction models through a
robust benchmarking process, and show that DTI prediction methods based on
transductive models lack generalization and lead to inflated performance when
evaluated as previously done in the literature, hence not being suited for drug
repurposing approaches. We then propose a novel biologically-driven strategy
for negative edge subsampling and show through in vitro validation that newly
discovered interactions are indeed true. We envision this work as the
underpinning for future fair benchmarking and robust model design. All
generated resources and tools are publicly available as a python package.
