MFD–GDrug: multimodal feature fusion-based deep learning for GPCR–drug interaction prediction

Abstract

The accurate identification of drug–protein interactions (DPIs) is crucial in drug development, especially concerning G protein-coupled receptors (GPCRs), which are vital targets in drug discovery. However, experimental validation of GPCR–drug pairings is costly, prompting the need for accurate predictive methods. To address this, we propose MFD–GDrug, a multimodal deep learning model. Leveraging the ESM pretrained model, we extract protein features and employ a CNN for protein feature representation. For drugs, we integrated multimodal features of drug molecular structures, including three-dimensional features derived from Mol2vec and the topological information of drug graph structures extracted through Graph Convolutional Neural Networks (GCN). By combining structural characterizations and pretrained embeddings, our model effectively captures GPCR–drug interactions. Our tests on leading GPCR–drug interaction datasets show that MFD–GDrug outperforms other methods, demonstrating superior predictive accuracy.