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.
Abstract
Drug-protein interaction (DTI) prediction is a pivotal step in the drug discovery and repurposing process, helping to minimize experimental costs and time. However, existing deep learning methods often face limitations in simultaneously capturing the spatial structure of drug molecules and the deep contextual correlation with protein sequences. To address this issue, we propose 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. Comprehensive experiments on two benchmark datasets, KIBA and Davis, across three rigorous scenarios: random splits, cold drug splits, and cold target splits demonstrate that GraphTransDTI achieves competitive performance compared to current state-of-the-art baseline models. Our findings confirm that the strategic combination of graph structural information and sequential attention mechanisms significantly enhances prediction accuracy and robustness in cold-start scenarios, offering a reliable and well-validated approach for high-precision virtual drug screening systems.
Accurate prediction of drug-target affinities (DTA) is critical for drug discovery. However, this task remains a significant challenge due to the complexity of modeling interactions between small ligands and large targets. In this study, we propose a multi-modal deep learning framework (CrossSG-DTA) to predict drug-target affinity by integrating sequence semantics with graph structural information. We leverage ChemBERTa and ESM-2 to extract rich semantic features for drugs and targets, respectively. In addition, a modified Graph Convolutional Network (GCN) is utilized to simultaneously capture structural data. To effectively fuse these heterogeneous features, we design a new symmetric dual cross-attention fusion mechanism for drugs and targets. This mechanism enables the model to capture complex dependencies between global sequence representations and local topological structures. Subsequently, the fused drug and target features are concatenated and fed into a three-layer Multi-Layer Perceptron (MLP) to obtain the final binding affinity. Experimental results on the Davis and KIBA datasets demonstrate that CrossSG-DTA significantly outperforms state-of-the-art methods. Finally, a case study on a glaucoma-related target highlights the practical utility of our model as a powerful in silico tool for DTA tasks.
Wei Lan, Tian Huang, Guohang He et al.· IEEE journal of biomedical a...· 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 drug-target interactions.
Hongmei Wang, Shisen Sun, Mujin Li et al.· IEEE journal of biomedical a...· 0 citations
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.
SGTL-DDA is proposed, a novel graph transformer framework designed to incorporate structural information and domain-specific knowledge from heterogeneous biological information networks (HBINs) that successfully identifies both known therapeutics and novel repositioning candidates, supported by molecular docking results and literature evidence.
Bowei Zhao, Hui Zhao, Yu-an Huang et al.· IEEE transactions on computa...· 0 citations
Accurate prediction of drug-target interactions is pivotal for accelerating drug discovery and drug repurposing. However, existing advanced methods often fail to effectively characterize the many-to-many interactions between drugs and targets. Furthermore, they struggle to fully mine the structural features of drugs and proteins. To address these limitations, we propose IHLO-DTI, a novel prediction model based on an improved hypergraph neural network and Laplacian matrix optimization. First, we construct drug, protein, and drug−protein pair hypergraphs, where shared-drug and shared-target relationships are used to characterize multi-target activity and shared-target regulation. We then optimize hyperedge weights using a Laplacian matrix to enhance biologically meaningful high-order associations and suppress potential noise. Second, we use simplified graph convolution and graph convolutional network to extract global and local features, enabling efficient modeling of multi-level semantic information for drugs and targets. Next, we introduce a cross-attention mechanism and a dynamic gating module to perform fine-grained fusion of multi-channel features, improving the representation of cross-modal information interactions. Finally, we jointly train the model with contrastive learning and cross-entropy loss to enhance the consistency and discriminability of the learned representations. IHLO-DTI achieves mean AUROC and AUPR values of 0.9777 and 0.9716, respectively, on two public datasets. IHLO-DTI can effectively capture high-order many-to-many interactions between drugs and targets, improving prediction accuracy and robustness. It provides a more reliable computational tool for clinical drug screening, repurposing, and precision medicine research.
Guolongwei Dai, Tao Luo, Dandan Li et al.· ACS Synthetic Biology· 0 citations