A progressive fine-tuning framework with dynamic parameter selection for low-resource peptide–GPCR interaction prediction

Abstract G protein-coupled receptors (GPCRs) are among the most important drug targets, and peptide therapeutics are rapidly emerging. However, accurate prediction of peptide–GPCR interactions (PepGI) remains challenging due to the scarcity of high-quality data and the poor generalization of existing drug–target interaction (DTI) models, which are largely trained on small molecule data. Here, we introduce a progressive fine-tuning framework with a dynamic parameter selection strategy that adaptively selects critical fine-tuning parameters using Fisher information. Our method begins with pretraining on a large small molecule-GPCR dataset, followed by intermediate fine-tuning on peptide–target data to alleviate the representation mismatch across heterogeneous ligand modalities. Finally, the task-specific fine-tuning is performed on the low-resource PepGI scenario. Extensive experiments show that our approach significantly outperforms baselines across multiple evaluation metrics, and exhibits robust generalization under few-shot and practical cold-start settings. Overall, this work offers an effective solution for low-resource peptide–GPCR prediction and presents a transferable framework for cross-structure DTI modeling.