Exploring drug-target interaction prediction on cold-start scenarios via meta-learning-based graph transformer

Predicting drug-target interaction (DTI) is of great importance for drug discovery and development. With the rapid development of biological and chemical technologies, computational methods for DTI prediction are becoming a promising approach. However, there are few solutions to the cold-start problem in DTI prediction scenarios, as these methods rely on existing interaction information to support their modeling. Consequently, they are unable to effectively predict DTIs for new drugs or targets with limited interaction data in the existing work. To this end, we propose a graph transformer method based on meta-learning named MGDTI (short for Meta-learning-based Graph Transformer for Drug-Target Interaction prediction) to fill this gap. Technically, we employ drug-drug similarity and target-target similarity as additional information to mitigate the scarcity of interactions. Besides, we trained MGDTI via meta-learning to be adaptive to cold-start tasks. Moreover, we employed graph transformer to prevent over-smoothing by capturing long-range dependencies. Extensive results on the benchmark dataset demonstrate that MGDTI is effective on DTI prediction under cold-start scenarios.