Aug 2026· Molecular diversity· 0 citations· 37 references
Medicine
TL;DR
EQTri-DTI is designed to integrate three modality-specific networks to encode 1D protein sequences, 2D molecular images, and 3D drug structures and develops a joint uncertainty quantification scheme by calculating the weight summation of evidential uncertainty and prediction entropy from the aforementioned output, enabling a more comprehensive and nuanced assessment of uncertainties.
Empirical evaluation and robustness experiments show that M2DDI maintains high predictive accuracy even when modality-specific information is partially missing, outperforming existing methods under similar conditions and establish M2DDI as an effective and mechanism-aware solution for comprehensive DDI prediction.
Runqing Xu, Siyi Liu, Hao-Yang Li et al.· Proceedings of the 32nd ACM...· 0 citations
The proposed PCBERT-GAT-DFFNN-DTA model, a three-stage deep cross-modal representation fusion framework for accurate DTA prediction, achieves significant improvements in R2 scores across all datasets, demonstrating its effectiveness in learning complex drug protein interactions for reliable DTA prediction.
Essmily Simon, Sanjay S. Bankapur· Analytical Biochemistry· 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
It is indicated that although many methods report strong performance on standard benchmarks, their effectiveness is often influenced by dataset bias and limited evaluation settings, and most methods exhibit reduced performance in cold-start scenarios, highlighting challenges in generalization.
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.
Zhensong Wang, Weiliang Han, Ge Kong et al.· Molecular diversity· 0 citations
Experimental results on multiple benchmark data sets demonstrate that MMU-DPI outperforms several state-of-the-art DPI prediction methods and indicate that MMU-DPI can serve as a useful computational tool for drug discovery.
Jia-Hao Wei, Tie Shen· Journal of Chemical Informat...· 0 citations
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