Aug 2026· Molecular diversity· 0 citations· 18 references
Medicine
TL;DR
The key idea is to stabilize multimodal interaction by jointly modeling adaptive fusion dynamics and representation geometry constraints by introducing a Dynamic Cross-Modal Attention mechanism that adaptively reweights modality contributions conditioned on sample-specific context, mitigating modality dominance.
Drug-target affinity prediction provides a computational basis for virtual screening and drug repositioning optimization by estimating the binding strength between compounds and target proteins. Existing deep learning methods have evolved from early SMILES/amino acid sequence modeling to graph neural networks and multi...
Yu-Ning Liu, Guang-Ze Wang, Dan Liu et al.· European journal of medicina...· 0 citations
Summary Accurate prediction of drug-target interaction (DTI) facilitates the translation of lead compounds into clinical therapeutics. Despite progress in deep learning, two challenges remain: the representation gap between heterogeneous drug and protein modalities and insufficient use of complementary knowledge from p...
Bo-Wen Wang, Xiao-Lan Xie, Hai-Tao Zou et al.· iScience· 0 citations
HFEDTI is proposed, a DTI prediction model that integrates hierarchical feature fusion and weighted ensemble learning that consistently achieves strong performance on the C. elegans and Human datasets, further validating its effectiveness and generalization capability for DTI prediction.
Qing-Chuan Xu, An-Ting Gao, Kai Che et al.· Electronics· 0 citations
Drug-target affinity (DTA) prediction is widely used to prioritize candidate compounds before costly experimental screening. DTA models are often compared under a single data split, even though deployment may require extrapolation to new chemical series, new protein targets, or both. We ask whether the distribution shi...
Minjae Chung, Clara Li, M. Muthukumaran et al.· 0 citations
Drug-target affinity prediction under cold-start set tings suffers from distribution shifts between training and testing data. This leads to poor generalization for unseen drugs or targets. To address this issue, we propose a conditional inter action modulation framework. The model explicitly captures pair-specific int...
Zi-You Zhou, Min Chen· IEEE transactions on computa...· 0 citations
This review provides a unified roadmap to accelerate the clinical deployment of multimodal drug response prediction and proposes five actionable directions, namely privacy-preserving benchmarks, causally interpretable models, temporal dynamic frameworks, cross-domain generalization, and lightweight clinical tools.
Jing-Wen Fang, Zihan Wang, Jun-Hao Shao et al.· Drug Discoveries & Therapeut...· 0 citations
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