KAN-MoDTI: Drug target interaction prediction based on Kolmogorov-Arnold network and multimodal feature fusion

Abstract

Drug-target interaction (DTI) prediction is a crucial task in computational drug discovery and repurposing, as it accelerates candidate identification while reducing development costs. Despite the advancements in deep learning, existing methods still face challenges in effectively modeling multi-modal data, fusing heterogeneous features, and capturing complex nonlinear relationships. We propose KAN-MoDTI to tackle these challenges by integrating Kolmogorov-Arnold Networks (KAN) with multimodal feature fusion and adaptive gating mechanisms, effectively combining heterogeneous drug and target representations to better capture the complex interactions between them. In the feature encoding stage, we use a dual-branch approach: For drugs, we combine SMILES sequence embeddings with structural representations from a KAN-based graph encoder. For targets, we integrate N-gram sequence embeddings with biochemical descriptor features. In the feature fusion stage, we introduce the FeatureFusionKAN module, which uses a gating mechanism to assign adaptive weights and KAN to perform the integration of heterogeneous modal features. KAN is also utilized in the final prediction layer to enhance the model’s ability to accurately predict complex drug-target interactions. Comprehensive experiments on datasets such as DrugBank, BindingDB, and Human show that KAN-MoDTI consistently outperforms or matches recent state-of-the-art baselines across metrics like AUROC and AUPRC.The source code implementation can be found at: https://github.com/jiahaoxin/KAN-MoDTI.