AD-LSF: A Multimodal Framework for Drug-Target Interaction Prediction Based on Asymmetric Dynamic Gated Modulation and Adaptive Latent Signal Fusion
Year: 2026
Type: article
Abstract: Accurate prediction of drug-target interactions (DTIs) is fundamental to drug discovery and repurposing. Although deep learning-based multimodal integration has advanced DTI modeling, current approaches remain limited in capturing complex cross-modal nonlinear dependencies and in reconciling distributional heterogeneity across data modalities. To address these limitations, we propose a multimodal DTI prediction framework termed Asymmetric Dynamic Gated Modulation and Adaptive Latent Signal Fusion (AD-LSF), which is built upon asymmetric dynamic gating modulation and adaptive latent signal fusion. Specifically, we develop an asymmetric dynamic gating modulation module that leverages anisotropic convolution and dynamic frequency decomposition to capture directional structural dependencies and multiscale details within molecular sequences. In addition, we introduce an adaptive latent signal fusion mechanism that integrates latent signal coordination with bidirectional interactive alignment, enabling deep alignment and adaptive aggregation of heterogeneous features. Across four benchmark data sets, AD-LSF consistently outperforms state-of-the-art methods in AUROC, AUPRC, F1 score, recall, and accuracy.
Source: ACS Omega
Authors: Bo Li, Zhichong Cao, Jingzhao Chen, Jing Xie
Institutions: Wuhan University of Science and Technology, Sias University
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,935
OpenAlex ID: W7165793191