Behavior-aware contextual representation learning for drug-target interaction prediction

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Abstract

Accurate prediction of drug-target interactions (DTIs) is a fundamental task in computational drug discovery, as it can effectively reduce the cost and time required for experimental screening. However, although most existing methods consider both sides of an interaction, they still focus mainly on the intrinsic features of drugs and proteins and are therefore insufficient to capture higher-order behavioral context and complementary interaction patterns in DTIs. To address this issue, we propose a novel DTI prediction framework that integrates drug molecular structure, protein sequence information, and behavior-aware contextual representations within a unified architecture. Specifically, drug compounds are encoded by a graph neural network (GNN) to capture molecular topology, protein sequences are modeled to learn semantic features, and a protein-centric behavioral sequence is introduced to characterize pharmacological interaction patterns. In addition, an attention-based fusion mechanism is introduced to model dependencies within the behavioral sequence and selectively aggregate informative behavioral contextual signals. Extensive experiments on three benchmark datasets demonstrate that the proposed method consistently outperforms representative baseline models. These results indicate that the proposed framework provides an effective computational solution for DTI prediction.

Keywords

drug-target interaction prediction / graph neural networks / behavioral sequence / attention mechanism

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