Aug 2026· Naunyn-Schmiedeberg's Archives of Pharmacology· 0 citations· 22 references
Medicine
TL;DR
Experiments show that GraESM-FuseDTA achieves competitive overall performance and consistent advantages in ranking-oriented and variance-explanation metrics across warm start, drug cold start, target cold start, and strict pair cold start settings.
A multi-modal deep learning framework to predict drug-target affinity by integrating sequence semantics with graph structural information and design a new symmetric dual cross-attention fusion mechanism for drugs and targets.
Wei Lan, Tian Huang, Guohang He et al.· IEEE journal of biomedical a...· 0 citations
This work proposes GraphTransDTI, a synergistic hybrid framework that integrates a Graph Transformer to represent drug graph structures, a CNN-BiLSTM network to encode protein sequence context, and a Cross-Attention mechanism to model cross-domain interactions.
Vang V. Le, Mai Thi Anh Nhu, Pham Truong Viet Thong· PLoS ONE· 0 citations
Predicting drug–target interactions is critical for drug discovery, yet many deep learning methods overlook atom–residue–level relationships, so PHGDTI is proposed, a multimodal framework that integrates sequence and structural cues for binding prediction.
Hua Qian, Deng Pan, Liangpeng Nie et al.· Journal of Computational Bio...· 0 citations
DeepGCL is presented, a novel multi-modal framework that leverages multi-view graph contrastive learning to capture latent representations of pocket-drug interactions and their underlying molecular determinants and underscores the effectiveness of multi-view learning paradigms in capturing the multifaceted nature of dr...
Hongmei Wang, Shisen Sun, Mujin Li et al.· IEEE journal of biomedical a...· 0 citations
IHLO-DTI, a novel prediction model based on an improved hypergraph neural network and Laplacian matrix optimization, can effectively capture high-order many-to-many interactions between drugs and targets, improving prediction accuracy and robustness.
Guolongwei Dai, Tao Luo, Dan-Dan Li et al.· ACS Synthetic Biology· 0 citations
DQHTFI is proposed, a fine-grained interaction prediction framework for drug–target interaction classification and binding affinity regression that employs BRICS fragments and Pfam functional domains as the basic interaction units and jointly learns semantic and structural representations.
Ke Tao, Haoze Chai, Zhuo Chen et al.· bioRxiv· 0 citations
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