A computational framework for predicting drug-target interactions by fusing gene ontology information with cross attention.

MOTIVATION: Identifying drug-target interactions (DTIs) is a critical step in both drug discovery and drug repurposing. Accurate in silico prediction of DTIs can substantially reduce development time and costs. Recent advances in sequence-based methods have leveraged attention mechanisms to improve prediction accuracy. However, these approaches typically rely solely on the molecular structures of drugs and proteins, overlooking higher-level semantic information that reflects functional and biological relationships. RESULTS: In this work, we propose GODTI, a novel Gene Ontology-guided Drug-Target Interaction prediction model that enhances the performance through multimodal feature integration. GODTI comprises three major components: a feature extraction module, a multimodal fusion module, and an intermolecular interaction modeling module. In the protein feature extractor, both functional descriptors derived from Gene Ontology and sequence-based embeddings from amino acid sequences are obtained and combined. These protein representations are then integrated with drug molecular features via the multimodal fusion module and subsequently processed by the interaction modeling module to predict potential interactions. We evaluated GODTI under four realistic experimental settings, demonstrating consistent improvements over state-of-the-art baselines. Furthermore, case studies validated the practical utility of GODTI in accurately identifying novel, low-cost DTIs, underscoring its potential to accelerate drug discovery workflows.