gPPIpred: A User-Friendly PPI Predictor Based on Protein Molecular Graphs

Protein-protein interactions (PPIs) govern essential cellular processes but remain challenging to characterize experimentally due to high cost and labor intensity. We present gPPIpred, a scalable computational framework leveraging graph neural networks (GNNs) and attention mechanisms to predict PPIs at residue-level resolution. Proteins are encoded as spatially informed molecular graphs integrating physicochemical features. Using curated structural datasets for training and validation, gPPIpred was fine-tuned to reliably predict positive interactions and actual interacting sites. Attention scores highlight key residues mediating interactions, offering interpretable insights to guide experimental design. gPPIpred combines high predictive performance with explainability, providing a user-friendly pipeline for large-scale PPI discovery.