Fourier-Enhanced Kolmogorov-Arnold Network With Attention for Drug-Target Interaction Prediction

Identifying drug-target interactions (DTI) is a fundamental yet costly step in drug discovery, motivating the development of accurate and efficient computational prediction methods. In this paper, we propose FKAN-a, a DTI prediction framework that integrates Fourier-enhanced Kolmogorov-Arnold networks (KAN) with attention mechanisms under a contrastive learning paradigm. Drug molecules and protein sequences are preprocessed and encoded using pretrained representations to capture interaction-relevant features. The resulting embeddings are further transformed through KAN with learnable Fourier bases to model complex nonlinear relationships. A cross-modality attention module is introduced to enhance the modeling of fine-grained drug-protein associations. Experiments conducted on three public benchmark datasets demonstrate that FKAN-a consistently outperforms representative state-of-the-art methods in terms of prediction performance and computational efficiency. These results indicate that the proposed framework provides an effective solution for DTI prediction and practical candidate prioritization in drug discovery.