An interpretable geometric graph neural network for enhancing the generalizability of drug-target interaction prediction.

BACKGROUND

Accurate prediction of drug-target interactions (DTIs) is essential for advancing drug discovery. Although numerous computational methods have been proposed, many exhibit limited generalization, particularly when dealing with unseen drugs or targets.

RESULTS

To address this challenge, we introduce GPS-DTI, a deep learning framework designed to capture both local and global features of drugs and proteins, thereby enhancing predictive robustness. Specifically, GPS-DTI employs a graph isomorphism network with edge features (GINE)-based graph neural network, combined with a multi-head attention mechanism (MHAM), to effectively model the structural characteristics of drug molecules. For proteins, representations are derived from the pre-trained Evolutionary Scale Model (ESM-2) model and further refined through convolutional neural networks (CNNs), yielding rich feature embeddings. A cross-attention module integrates drug and protein features, uncovering biologically meaningful interactions and improving model interpretability.

CONCLUSIONS

Comprehensive benchmarking across in-domain and cross-domain DTI prediction tasks demonstrates that GPS-DTI outperforms existing methods, underscoring its strong generalization capability. Notably, the model achieves state-of-the-art performance on drug-target affinity (DTA) tasks and shows robust adaptability when evaluated on an independent Coronavirus Disease 2019 (COVID-19)-related test set. Furthermore, visualization of cross-attention maps offers interpretable insights into key molecular interactions, highlighting the potential of GPS-DTI in real-world drug discovery applications.