Authors
Guishen Wang, Yuxiang Kong, Yuyouqiang Fu, Xinyue You, Chen Cao, Gaoyang Li
Published in
IEEE journal of biomedical and health informatics. Volume PP. Jun 18, 2026. Epub Jun 18, 2026.
Abstract
Drug-target binding affinity (DTBA) prediction is a cornerstone of AI-aided drug discovery. Although multimodal integration and explicit pocket modeling have recently boosted predictive accuracy, existing methods predominantly rely on feature concatenation or fixed-kernel cross-attention mechanisms to fuse heterogeneous modalities, struggling to capture the complex non-linear synergy between chemical structure and biological sequence data, while failing to dynamically quantify inter-modal importance, thereby limiting model interpretability and generalization. To bridge this gap, we propose TabKAN-DTA, a framework centered on a FastKAN-based adaptive gating mechanism that performs input-adaptive nonlinear transformations on heterogeneous modality feature spaces through learnable activation functions, enabling dynamic quantification of inter-modal importance. We further introduce a TabPFN-based tabular encoder to systematically supplement the physicochemical properties and global molecular descriptors absent from sequence and graph models. Comprehensive experiments on standard DTBA benchmarks demonstrate state-of-the-art performance, and ablation studies further reveal that the tabular modality contributes a disproportionately large share of the performance gain. By disentangling modality-specific contributions on a non-small cell lung cancer drug repurposing dataset, we validate that our framework not only improves predictive accuracy but also establishes a new paradigm for interpretable multimodal DTBA prediction. Code is available at https://github.com/trybestxk/TabKAN-DTA.
PMID:
42313595
Bibliographic data and abstract were imported from PubMed on 19 Jun 2026.
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