November 30, 2025Open Access

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

Key Points

Outperforms existing methods in drug-target interaction predictions, highlighting its effectiveness.
Achieves state-of-the-art performance in drug-target affinity tasks, showcasing its generalization ability.
Utilizes graph neural network architecture, providing insights through cross-attention maps in molecular interactions.
Highlights significant potential for applications in real-world drug discovery, especially in assessing COVID-19-related interactions.

Abstract

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.

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Cite This Study

Xiong et al. (Wed,) studied this question.

synapsesocial.com/papers/692b944c1d383f2b2a378cdd https://doi.org/https://doi.org/10.1186/s12915-025-02456-9

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