GraphormerDTI: A graph transformer-based approach for drug-target interaction prediction

作者：Mengmeng Gao, Daokun Zhang, Yi Chen, Yiwen Zhang, Zhikang Wang, Xiaoyu Wang, Shanshan Li, Yuming Guo, Geoffrey I. Webb, Thi Nguyen, Lauren T. May, Jiangning Song · 发表于：Computers in Biology and Medicine · 年份：2024 · DOI：10.1016/j.compbiomed.2024.108339 · 被引用次数：72 · 研究领域：Computational Drug Discovery Methods、Machine Learning in Materials Science、Protein Structure and Dynamics

The application of Artificial Intelligence (AI) to screen drug molecules with potential therapeutic effects has revolutionized the drug discovery process, with significantly lower economic cost and time consumption than the traditional drug discovery pipeline. With the great power of AI, it is possible to rapidly search the vast chemical space for potential drug-target interactions (DTIs) between candidate drug molecules and disease protein targets. However, only a small proportion of molecules have labelled DTIs, consequently limiting the performance of AI-based drug screening. To solve this problem, a machine learning-based approach with great ability to generalize DTI prediction across molecules is desirable. Many existing machine learning approaches for DTI identification failed to exploit the full information with respect to the topological structures of candidate molecules. To develop a better approach for DTI prediction, we propose GraphormerDTI, which employs the powerful Graph Transformer neural network to model molecular structures. GraphormerDTI embeds molecular graphs into vector-format representations through iterative Transformer-based message passing, which encodes molecules' structural characteristics by node centrality encoding, node spatial encoding and edge encoding. With a strong structural inductive bias, the proposed GraphormerDTI approach can effectively infer informative representations for out-of-sample molecules and as such, it is capable of predicti...