Towards a more inductive world for drug repurposing approaches
This work addresses the problem of unreliable benchmarking and poor generalization in drug-target interaction prediction for drug repurposing researchers, though it appears incremental rather than paradigm-shifting.
The paper identified that current drug-target interaction prediction methods based on transductive models lack generalization and show inflated performance in existing benchmarks, making them unsuitable for drug repurposing. The authors proposed a novel biologically-driven negative edge subsampling strategy and validated newly discovered interactions through in vitro experiments.
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.