Dongjiang Niu

DSC-bsite: a dynamic–static collaborative multimodal graph learning method for protein–small molecule binding site prediction

Minglei Dong, Dongjiang Niu, Yuanxing Peng, Hongle Li et autres

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Conditional molecular dynamics refinement for protein–ligand affinity prediction

Hao Li, Dongjiang Niu, Xiaofeng Wang, Zhiqiang Wei et autres

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GatedGeoGO:Multi-Modal Geometry-Aware Network with Gated Fusion and GO Semantic Attention for Protein Function Prediction

M Dong, Dongjiang Niu, Yuanxing Peng, Hui Li et autres

Proteins play essential roles in diverse biological processes, and accurate function annotation is fundamental for understanding cellular mechanisms and disease pathogenesis. However, existing protein function prediction methods often lack effective multimodal integration and fail to fully exploit the rich semantic information in …

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Comprehensive Review of Contrastive and Generative Self-Supervised Learning for Small Molecular Representation

Zengqian Deng, Dongjiang Niu, Zhiqiang Wei, Zhen Li

Drug discovery is a complex and resource-intensive process, where developing effective computational tools to analyze vast and heterogeneous molecular data is paramount. In this landscape, contrastive and generative learning have emerged as two foundational paradigms for molecular representation learning, driving significant advances. …

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Interaction-aware adaptive network for drug-drug interaction prediction

Dongjiang Niu, Xiaofeng Wang, Zengqian Deng, Bowen Tang et autres

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GTCExplainer: Interpretable Graph Convolutional Networks for Molecular Activity Prediction

Jun Xiao, Dongjiang Niu, Qunhao Zhang, Zhixin Zhang et autres

ABSTRACT With the growing application of Graph Convolutional Networks (GCNs) across various domains, particularly in the field of bioinformatics, the demand for their interpretability has become more urgent. In bioinformatics, many critical tasks, including drug discovery and protein–ligand interaction analysis, rely heavily …

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Geometry-Complete Latent Diffusion Model for 3D Molecule Generation

Qunhao Zhang, Jun Xiao, Dongjiang Niu, Zhixin Zhang et autres

MOTIVATION: Generative models, especially diffusion models, have recently made remarkable progress in fields such as graph generation and drug design. However, current diffusion-based 3D molecule generation models still struggle with adequately modeling the true data distribution. RESULTS: We designed the geometry-complete latent …

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Task-Specific Activity Cliff Prediction Method Based on Transfer Learning and a Hyper Connection Graph Model

Dongjiang Niu, Zengqian Deng, Xiaofeng Wang, Zhen Li

Activity cliffs (ACs) are defined as significant changes in biological activity triggered by minor chemical structural modifications. Accurately predicting ACs is crucial for drug discovery and molecular optimization. Existing approaches often overlook the intricate structural relationships within compounds, limiting both predictive accuracy …

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Multilevel cross-domain relational network for drug repositioning

Dongjiang Niu, Xiaofeng Wang, Zengqian Deng, Bowen Tang et autres

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MAPTrans: mutual attention transformer with dynamic meta-path pruning for drug repositioning

Dongjiang Niu, XiaoFeng Wang, Zhixin Zhang, Qunhao Zhang et autres

Drug repositioning has become a hot topic that could provide an innovative solution in drug discovery by exploring the potential correlation between drugs and diseases. However, existing computational drug repositioning methods fail to effectively integrate heterogeneous data from multiple sources and neglect …

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Drug–drug interaction prediction based on graph contrastive learning and dual-view fusion

Dongjiang Niu, Mingxuan Li, Zhixin Zhang, Zhen Li

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DVGEDR: a drug repositioning method based on dual-view fusion and graph enhancement mechanism in heterogeneous networks

Dongjiang Niu, Lianwei Zhang, Beiyi Zhang, Qiang Zhang et autres

Drug repositioning, the discovery of new therapeutic uses for existing drugs, is increasingly gaining attention as a cost-effective and high-yield drug discovery strategy. Existing methods integrate diverse biological information into heterogeneous networks, providing a comprehensive framework for understanding complex drug–disease associations, which …

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