PRADA-DTI: A Prototype-Retrieval Augmented Domain-Adaptation Framework for Drug-Target Interaction Prediction

Drug target interaction (DTl) prediction is a fundamental task in computational drug discovery. However, most existing DTI models assume a static learning environment, whereas real-world biomedical data are dynamic, characterized by the continuous emergence of new protein families and interaction patterns. This poses major challenges for model generalization and continual adaptation, especially under privacy and data-access constraints. To address these issues, we introduce PRADA-DTI, a retrieval-augmented, parameter-efficient framework for domain-incremental DTI learning. Built on a shared physicochemical backbone, PRADA-DTI learns domain-specialized prompts for representation-level modulation, coupled with Low-Rank Adaptation (LoRA) modules for parameter-space adaptation. At inference, protein embeddings query a compact prototype memory without storing raw molecular data, retrieving similar domains to dynamically compose the relevant prompts and LoRA parameters without requiring domain labels. This retrieval-guided composition enables continual learning from new protein domains while mitigating catastrophic forgetting on previous ones. On BindingDB and BIOSNAP benchmarks, PRADA-DTI substantially outperforms state-of-the-art continual learning and parameter-efficient baselines in both predictive accuracy and forgetting mitigation with minimal parameter updates. Interpretability analysis through residue-level attribution visualization demonstrates that the model correctly attends to binding pocket regions across different protein domains, confirming that the retrieval-guided adaptation mechanism captures biologically relevant structural patterns. These results demonstrate the effectiveness of retrievalaugmented parameter adaptation for continual drug discovery.