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Pengwei Hu

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10 papers

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AAAI Conference 2026 Conference Paper

Dual-Channel Learning Framework for Zero-Shot CircRNA-miRNA Interaction Prediction via State Space Modeling

Mengmeng Wei
Lei Wang
Zhu-Hong You
Pengwei Hu
Bowei Zhao
Zhi-An Huang
Yu-An Huang
Haicheng Yi

CircRNA-miRNA interaction (CMI) plays a pivotal role in disease therapeutics and drug discovery. However, existing methods face several challenges in modeling complex biological networks and zero-shot learning scenarios. Biological networks encapsulate rich biological information, yet current approaches often fail to fully exploit this depth. Moreover, zero-shot prediction requires models to identify new interactions without relying on previously observed samples, imposing stringent requirements on generalization capabilities. To address these limitations, we propose a dual-channel learning framework leveraging State space modeling for Zero-shot CMI prediction (ZeroStem). ZeroStem first enhances the biological relevance of node using prior knowledge, and employs a graph Transformer to extract macro-topological representations. Subsequently, it generates semantic subgraphs based on meta-paths to focus on specific biological relationships, utilizing the Mamba to extract micro-semantic representations via state space modeling. Finally, macro-topological and micro-semantic representations are seamlessly integrated through linear transformation and residual connections, enabling high-precision zero-shot CMI prediction. Extensive experiments on multiple benchmark datasets demonstrate that ZeroStem significantly outperforms existing methods, validating its efficiency and robust generalization in CMI prediction. Case studies further illustrate that ZeroStem offers novel insights into the molecular mechanisms underlying intricate disease-associated networks.

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JBHI Journal 2026 Journal Article

FD-MSGL: Drug Repositioning via Frequency-Domain Multi-Source Synergistic Graph Learning

Xiaobo Zhu
Xun Deng
Zimai Zhang
Yujie Qi
Hengchuang Yin
Xueyan Long
Yu-An Huang
Thomas Herget

Drug repositioning accelerates therapeutic development by revealing novel indications for approved compounds, yet existing methods typically rely on single information sources and fail to integrate multi-scale biological mechanisms spanning molecular similarity, target selectivity, and multi-step regulatory pathways. Here we introduce FD-MSGL, a Frequency-Domain Multi-Source Synergistic Graph Learning framework that addresses this challenge by integrating three complementary biological information sources. FD-MSGL constructs homogeneous semantic graphs to capture molecular similarity in chemical space, heterogeneous graphs to model direct drug-protein-disease interactions revealing target selectivity, and pathway regulation graphs to trace indirect therapeutic mechanisms through multi-step regulatory cascades. Through frequency-domain decomposition, FD-MSGL simultaneously models local specificity of molecular recognition and global organizational patterns of drug families. The framework quantifies biological synergies across molecular, target, and pathway levels, integrates complementary evidence from chemical similarity, target selectivity, and regulatory mechanisms, and dynamically balances precise structural matching with global therapeutic pattern consistency. Empirical evaluation across three benchmark datasets demonstrates that FD-MSGL achieves competitive performance.

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JBHI Journal 2026 Journal Article

LLM-DDI: Leveraging Large Language Models for Drug-Drug Interaction Prediction on Biomedical Knowledge Graph

Dongxu Li
Yue Yang
Ziwen Cui
Hengchuang Yin
Pengwei Hu
Lun Hu

Drug-drug interaction (DDI) refers to the interaction relationships between drugs. Discovering new DDIs is crucial for advancing drug development and enhancing clinical treatments. Given the significant progress achieved through graph neural networks (GNNs), network-based models have become a prevalent approach for tackling this challenge. However, current network-based approaches are incapable of seamlessly integrating a wide range of information. Motivated by this discovery, we propose a novel model, namely LLM-DDI, which aims to comprehensively tackle DDI prediction tasks by integrating various information of molecules in the BKG. LLM-DDI initially incorporates the generative pre-trained transformer (GPT) model to generate embeddings for each molecule within the biomedical knowledge graph (BKG). These embeddings encompass diverse types of information pertaining to each molecule. Subsequently, LLM-DDI utilizes a message-passing GNN framework to enhance the learning of molecular representations with the embeddings derived from GPT as input. LLM-DDI governs the propagation of information within the BKG by semantic relationships. These semantic relationships determine how information flows and is exchanged between different entities in the BKG. Finally, LLM-DDI leverages the learned drug representations to predict potential DDIs. Experiments show the effectiveness of LLM-DDI, as it achieves the best performance on two real-world datasets, providing valuable guidance for drug development and clinical treatment.

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JBHI Journal 2026 Journal Article

Multi-View Contrastive Learning for Drug-Drug Interaction Event Prediction

Dongxu Li
Feifan Zhao
Yue Yang
Ziwen Cui
Pengwei Hu
Lun Hu

Drug-drug interactions (DDIs) represent a critical challenge in pharmacology, often leading to adverse effects and compromised therapeutic efficacy. Accurate prediction of DDI events, which involve not only identifying interacting drug pairs but also characterizing the specific nature and context of their interactions, is essential for drug safety and personalized medicine. In this study, we propose a novel Multi-view Contrastive Learning framework, namely MCL-DDI, for DDI Event Prediction by leveraging multi-view representations of drugs to enhance predictive performance. MCL-DDI integrates molecular structures and network features, capturing complementary information about drug properties and interactions. By employing contrastive learning, we align and unify drug representations across these diverse views, enabling the framework to distinguish complex interaction patterns. Extensive experiments on benchmark datasets demonstrate that MCL-DDI outperforms state-of-the-art methods in terms of predictive accuracy. Furthermore, case studies highlight the model’s ability to identify clinically relevant DDIs, offering practical insights for drug development and risk assessment. Our work establishes a robust and accurate paradigm for DDI event prediction, paving the way for safer and more effective pharmacological interventions.

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JBHI Journal 2025 Journal Article

Knowledge Graph Neural Network With Spatial-Aware Capsule for Drug-Drug Interaction Prediction

Xiaorui Su
Bowei Zhao
Guodong Li
Jun Zhang
Pengwei Hu
Zhuhong You
Lun Hu

Uncovering novel drug-drug interactions (DDIs) plays a pivotal role in advancing drug development and improving clinical treatment. The outstanding effectiveness of graph neural networks (GNNs) has garnered significant interest in the field of DDI prediction. Consequently, there has been a notable surge in the development of network-based computational approaches for predicting DDIs. However, current approaches face limitations in capturing the spatial relationships between neighboring nodes and their higher-level features during the aggregation of neighbor representations. To address this issue, this study introduces a novel model, KGCNN, designed to comprehensively tackle DDI prediction tasks by considering spatial relationships between molecules within the biomedical knowledge graph (BKG). KGCNN is built upon a message-passing GNN framework, consisting of propagation and aggregation. In the context of the BKG, KGCNN governs the propagation of information based on semantic relationships, which determine the flow and exchange of information between different molecules. In contrast to traditional linear aggregators, KGCNN introduces a spatial-aware capsule aggregator, which effectively captures the spatial relationships among neighboring molecules and their higher-level features within the graph structure. The ultimate goal is to leverage these learned drug representations to predict potential DDIs. To evaluate the effectiveness of KGCNN, it undergoes testing on two datasets. Extensive experimental results demonstrate its superiority in DDI predictions and quantified performance.

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JBHI Journal 2024 Journal Article

Discovering Consensus Regions for Interpretable Identification of RNA N6-Methyladenosine Modification Sites via Graph Contrastive Clustering

Guodong Li
Bowei Zhao
Xiaorui Su
Yue Yang
Pengwei Hu
Xi Zhou
Lun Hu

As a pivotal post-transcriptional modification of RNA, N6-methyladenosine (m6A) has a substantial influence on gene expression modulation and cellular fate determination. Although a variety of computational models have been developed to accurately identify potential m6A modification sites, few of them are capable of interpreting the identification process with insights gained from consensus knowledge. To overcome this problem, we propose a deep learning model, namely M6A-DCR, by discovering consensus regions for interpretable identification of m6A modification sites. In particular, M6A-DCR first constructs an instance graph for each RNA sequence by integrating specific positions and types of nucleotides. The discovery of consensus regions is then formulated as a graph clustering problem in light of aggregating all instance graphs. After that, M6A-DCR adopts a motif-aware graph reconstruction optimization process to learn high-quality embeddings of input RNA sequences, thus achieving the identification of m6A modification sites in an end-to-end manner. Experimental results demonstrate the superior performance of M6A-DCR by comparing it with several state-of-the-art identification models. The consideration of consensus regions empowers our model to make interpretable predictions at the motif level. The analysis of cross validation through different species and tissues further verifies the consistency between the identification results of M6A-DCR and the evolutionary relationships among species.

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AAAI Conference 2024 Conference Paper

Dual-Channel Learning Framework for Drug-Drug Interaction Prediction via Relation-Aware Heterogeneous Graph Transformer

Xiaorui Su
Pengwei Hu
Zhu-Hong You
Philip S. Yu
Lun Hu

Identifying novel drug-drug interactions (DDIs) is a crucial task in pharmacology, as the interference between pharmacological substances can pose serious medical risks. In recent years, several network-based techniques have emerged for predicting DDIs. However, they primarily focus on local structures within DDI-related networks, often overlooking the significance of indirect connections between pairwise drug nodes from a global perspective. Additionally, effectively handling heterogeneous information present in both biomedical knowledge graphs and drug molecular graphs remains a challenge for improved performance of DDI prediction. To address these limitations, we propose a Transformer-based relatIon-aware Graph rEpresentation leaRning framework (TIGER) for DDI prediction. TIGER leverages the Transformer architecture to effectively exploit the structure of heterogeneous graph, which allows it direct learning of long dependencies and high-order structures. Furthermore, TIGER incorporates a relation-aware self-attention mechanism, capturing a diverse range of semantic relations that exist between pairs of nodes in heterogeneous graph. In addition to these advancements, TIGER enhances predictive accuracy by modeling DDI prediction task using a dual-channel network, where drug molecular graph and biomedical knowledge graph are fed into two respective channels. By incorporating embeddings obtained at graph and node levels, TIGER can benefit from structural properties of drugs as well as rich contextual information provided by biomedical knowledge graph. Extensive experiments conducted on three real-world datasets demonstrate the effectiveness of TIGER in DDI prediction. Furthermore, case studies highlight its ability to provide a deeper understanding of underlying mechanisms of DDIs.

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JBHI Journal 2023 Journal Article

Predicting Drug-Target Interactions Over Heterogeneous Information Network

Xiaorui Su
Pengwei Hu
Haicheng Yi
Zhuhong You
Lun Hu

Identifying Drug-Target Interactions (DTIs) is a critical step in studying pathogenesis and drug development. Due to the fact that conventional experimental methods usually suffer from high costs and low efficiency, various computational methods have been proposed to detect potential DTIs by extracting features from the biological information of drugs and their target proteins. Though effective, most of them fall short of considering the topological structure of the DTI network, which provides a global view to discover novel DTIs. In this paper, a network-based computational method, namely LG-DTI, is proposed to accurately predict DTIs over a heterogeneous information network. For drugs and target proteins, LG-DTI first learns not only their local representations from drug molecular structures and protein sequences, but also their global representations by using a semi-supervised heterogeneous network embedding method. These two kinds of representations consist of the final representations of drugs and target proteins, which are then incorporated into a Random Forest classifier to complete the task of DTI prediction. The performance of LG-DTI has been evaluated on two independent datasets and also compared with several state-of-the-art methods. Experimental results show the superior performance of LG-DTI. Moreover, our case study indicates that LG-DTI can be a valuable tool for identifying novel DTIs.

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IJCAI Conference 2020 Conference Paper

BlueMemo: Depression Analysis through Twitter Posts

Pengwei Hu
Chenhao Lin
Hui Su
Shaochun Li
Xue Han
Yuan Zhang
Jing Mei

The use of social media runs through our lives, and users' emotions are also affected by it. Previous studies have reported social organizations and psychologists using social media to find depressed patients. However, due to the variety of content published by users, it isn't effortless for the system to consider the text, image, and even the hidden information behind the image. To address this problem, we proposed a new system for social media screening of depressed patients named BlueMemo. We collected real-time posts from Twitter. Based on the posts, learned text features, image features, and visual attributes were extracted as three modalities and were fed into a multi-modal fusion and classification model to implement our system. The proposed BlueMemo has the power to help physicians and clinicians quickly and accurately identify users at potential risk for depression.

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AAAI Conference 2018 Short Paper

Dialogue Generation With GAN

Hui Su
Xiaoyu Shen
Pengwei Hu
Wenjie Li
Yun Chen

This paper presents a Generative Adversarial Network (GAN) to model multi-turn dialogue generation, which trains a latent hierarchical recurrent encoder-decoder simultaneously with a discriminative classiﬁer that make the prior approximate to the posterior. Experiments show that our model achieves better results.

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