Xu Gong

Chongqing University of Posts and Telecommunications

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Qun Liu

Chongqing University of Posts and Telecommunications

Guoyin Wang

Chongqing University of Posts and Telecommunications

Maotao Liu

Chongqing University of Posts and Telecommunications

Yike Guo

Hong Kong University of Science and Technology

Rui Han

Chongqing University of Posts and Telecommunications

Qun Liu

Ocean University of China

Rui Han

North China University of Science and Technology

Yifan Yang

Fudan University

Hao Zhou

Sun Yat-sen University

Li Liu

Chongqing University of Posts and Telecommunications

Cooperative Institutions

Chongqing University of Posts and Telecommunications

Chongqing University

Hong Kong University of Science and Technology

University of Hong Kong

Chinese Academy of Sciences

Shanghai University of Engineering Science

Northeast Normal University

Shandong University

Fudan University

Hong Kong Baptist University

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MultiGranDTI: an explainable multi-granularity representation framework for drug-target interaction prediction

Applied Intelligence (2024)

Xu Gong Qun Liu Jing He Yike Guo Guoyin Wang

Granularity

Representation

10.1007/s10489-024-05936-7

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Citations (0)

MIFS: An adaptive multipath information fused self-supervised framework for drug discovery

Neural Networks (2025)

Xu Gong Qun Liu Rui Han Yike Guo Guoyin Wang

10.1016/j.neunet.2024.107088

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Citations (1)

WRND: A weighted oversampling framework with relative neighborhood density for imbalanced noisy classification

Expert Systems with Applications (2023)

Min Li Hao Zhou Qun Liu Xu Gong Guoyin Wang

Oversampling

Benchmark (surveying)

Sample (material)

Interpolation

10.1016/j.eswa.2023.122593

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Citations (6)

HS-DTI: Drug-target Interaction Prediction Based on Hierarchical Networks and Multi-order Sequence Effect

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2022)

Xu Gong Maotao Liu Haichao Sun Min Li Qun Liu

Computer-aided drug design utilizes powerful computers to simulate the tasks in drug design, which could significantly accelerate drug development and reduce cost consumption. Drug-target interaction (DTI) prediction is the critical stage in computer-aided drug design and has drawn increased interest from both academia and pharmacogenomics. A variety of computational models for DTI have been available that rely only on the linear representation of compounds and amino acid sequences. However, the significant drawbacks are that these models do not consider the structures and functions of specific functional groups in drugs as well as the multi-order sequence effect of proteins. In this study, we present a novel computational model named HS-DTI that attempts to extract plentiful features of drugs and proteins by capturing functional group structures and incorporating multi-order sequence information, respectively. Specifically, the probability assignment matrix is learned through the hierarchical network module to identify the functional group structures in reconstructed drug graphs on the drug encoder. Next, modelling 1st-order and 2nd-order sequence information based on the hydrophilicity, hydrophobicity, and side-chain mass of the most contiguous and the second-most contiguous amino acid residues. Finally, we comprehensively evaluated the performance of HS-DTI with several state-of-the-art methods on real-world datasets. Experimental results clearly show the effectiveness of the proposed model. Additionally, ablation experiments are also being devised to validate this performance improvement.

Sequence (biology)

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Pharmacogenomics

10.1109/bibm55620.2022.9994908

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Citations (4)

MDFCL: Multimodal data fusion-based graph contrastive learning framework for molecular property prediction

Pattern Recognition (2025)

Xu Gong Maotao Liu Qun Liu Yike Guo Guoyin Wang

10.1016/j.patcog.2025.111463

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APK-MRL: An Adaptive Pre-training Framework with Knowledge-enhanced for Molecular Representation Learning

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2024)

Xu Gong Qun Liu Rui Han Liu Li Yike Guo

Representation

10.1109/bibm62325.2024.10821780

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HierMRL: Hierarchical Structure-Aware Molecular Representation Learning for Property Prediction

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2022)

Maotao Liu Yifan Yang Xu Gong Li Liu Qun Liu

Deep graph neural networks have recently demonstrated powerful representation learning capabilities in bioinformatics. It is still crucial and challenging to design a representation model fusing the fundamental chemistry and biology knowledge. However, most existing representation models not only ignore domain knowledge but are also built on labeled datasets. To address this issue, this paper proposed a hierarchical structure-aware pre-training model that used contrastive learning to improve molecular representations with unlabeled datasets. We conducted comprehensive experiments on 13 molecular benchmark datasets from different application domains. The results demonstrate that our hierarchical structure-aware pre-trained model achieves superior performance against state-of-the-art baselines.

Representation

Benchmark (surveying)

Feature Learning

10.1109/bibm55620.2022.9995310

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Citations (4)