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Xiaowen Hu

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8 papers

2 author rows

AAAI Conference 2026 Conference Paper

Hierarchical Structure-Property Alignment for Data-Efficient Molecular Generation and Editing

Ziyu Fan
Zhijian Huang
Yahan Li
Xiaowen Hu
Siyuan Shen
Yunliang Wang
Zeyu Zhong
Shuhong Liu

Property-constrained molecular generation and editing are crucial in AI-driven drug discovery but remain hindered by two factors: (i) capturing the complex relationships between molecular structures and multiple properties remains challenging, and (ii) the narrow coverage and incomplete annotations of molecular properties weaken the effectiveness of property-based models. To tackle these limitations, we propose HSPAG, a data-efficient framework featuring hierarchical structure–property alignment. By treating SMILES and molecular properties as complementary modalities, the model learns their relationships at atom, substructure, and whole-molecule levels. Moreover, we select representative samples through scaffold clustering and hard samples via an auxiliary variational auto-encoder (VAE), substantially reducing the required pre-training data. In addition, we incorporate a property relevance-aware masking mechanism and diversified perturbation strategies to enhance generation quality under sparse annotations. Experiments demonstrate that HSPAG captures fine-grained structure–property relationships and supports controllable generation under multiple property constraints. Two real-world case studies further validate the editing capabilities of HSPAG.

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JBHI Journal 2025 Journal Article

AGCLNDA: Enhancing the Prediction of ncRNA-Drug Resistance Association Using Adaptive Graph Contrastive Learning

Yanhao Fan
Che Zhang
Xiaowen Hu
Zhijian Huang
Lei Deng

Non-coding RNAs (ncRNAs), which do not encode proteins, have been implicated in chemotherapy resistance in cancer treatment. Given the high costs and time requirements of traditional biological experiments, there is an increasing need for computational models to predict ncRNA-drug resistance associations. In this study, we introduce AGCLNDA, an adaptive contrastive learning method designed to uncover these associations. AGCLNDA begins by constructing a bipartite graph from existing ncRNA-drug resistance data. It then utilizes a light graph convolutional network (LightGCN) to learn vector representations for both ncRNAs and drugs. The method assesses resistance association scores through the inner product of these vectors. To tackle data sparsity and noise, AGCLNDA incorporates learnable augmented view generators and denoised view generators, which provide contrastive views for enhanced data augmentation. Comparative experiments demonstrate that AGCLNDA outperforms five other advanced methods. Case studies further validate AGCLNDA as an effective tool for predicting ncRNA-drug resistance associations.

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JBHI Journal 2025 Journal Article

Medical Graph Diffusion: Hybrid Graph Diffusion With Heterogeneous Graph Convolutional Networks for Medical Text Classification

Guishen Wang
Shengnan Li
Keshuang Liu
Xiaowen Hu
Chen Cao

Text classification is a critical task for understanding the knowledge behind text, especially in medical text. In this paper, we propose a medical graph diffusion model, named the MGD model, for the medical text classification task. To model more structural relationships within a document, our MGD model constructs a text heterogeneous graph to represent word-level, sentence-level, and word-sentence-level structural relationships. To overcome the limitation of only considering direct neighbors, a graph diffusion convolution is employed to reconstruct the text heterogeneous graph. Subsequently, a heterogeneous graph convolutional network and a multilayer perceptron are used to complete the medical text classification task. To evaluate the performance of our MGD model, various text classification benchmarks, including long text standard benchmarks, short text standard benchmarks, and medical text benchmarks, are used to comprehensively assess the effectiveness and robustness of our MGD model. Compared with other representative baselines, it achieved notable improvements in both Accuracy and F1 score evaluation metrics. Ablation experiment results further demonstrated that the construction of heterogeneous graphs and the use of diffusion graph convolutional networks significantly impact the performance of our MGD model.

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JBHI Journal 2025 Journal Article

MMDDI-SSE: A Novel Multi-Modal Feature Fusion Model With Static Subgraph Embedding for Drug-Drug Interaction Event Prediction

Guishen Wang
Honghan Chen
Handan Wang
Hairong Gao
Xiaowen Hu
Chen Cao

Artificial intelligence techniques play a pivotal role in the accurate identification of drug-drug interaction (DDI) events, thereby informing clinical decisions and treatment regimens. While existing DDI prediction models have made significant progress by leveraging sequence features such as chemical substructures, targets, and enzymes, they often face limitations in integrating and effectively utilizing multi-modal drug representations. To address these limitations, this study proposes a novel multi-modal feature fusion model for DDI event prediction: MMDDI-SSE. Our approach integrates drug sequence modality with DDI graph representations through a novel architecture that employs static subgraph generation to capture structural properties. The model utilizes a graph autoencoder architecture to learn both local and global topological features from these subgraphs, while simultaneously processing diverse sequence-based characteristics including semantically enhanced pharmacodynamic features, chemical substructures, target proteins, and enzyme information. Through comprehensive evaluation on two distinct datasets, MMDDI-SSE demonstrates superior predictive performance compared to state-of-the-art baselines. Ablation studies further validate the effectiveness of each architectural component in enhancing DDI prediction accuracy.

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JBHI Journal 2024 Journal Article

AntiViralDL: Computational Antiviral Drug Repurposing Using Graph Neural Network and Self-Supervised Learning

Pan Zhang
Xiaowen Hu
Guangdi Li
Lei Deng

Viral infections have emerged as significant public health concerns for decades. Antiviral drugs, specifically designed to combat these infections, have the potential to reduce the disease burden substantially. However, traditional drug development methods, based on biological experiments, are resource-intensive, time-consuming, and low efficiency. Therefore, computational approaches for identifying antiviral drugs can enhance drug development efficiency. In this study, we introduce AntiViralDL, a computational framework for predicting virus-drug associations using self-supervised learning. Initially, we construct a reliable virus-drug association dataset by integrating the existing Drugvirus2 database and FDA-approved virus-drug associations. Utilizing these two datasets, we create a virus-drug association bipartite graph and employ the Light Graph Convolutional Network (LightGCN) to learn embedding representations of viruses and drugs. To address the sparsity of virus-drug association pairs, AntiViralDL incorporates contrastive learning to improve prediction accuracy. We implement data augmentation by adding random noise to the embedding representation space of virus and drug nodes, as opposed to traditional edge and node dropout. Finally, we calculate an inner product to predict virus-drug association relationships. Experimental results reveal that AntiViralDL achieves AUC and AUPR values of 0. 8450 and 0. 8494, respectively, outperforming four benchmarked virus-drug association prediction models. The case study further highlights the efficacy of AntiViralDL in predicting anti-COVID-19 drug candidates.

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JBHI Journal 2024 Journal Article

DeepFusionCDR: Employing Multi-Omics Integration and Molecule-Specific Transformers for Enhanced Prediction of Cancer Drug Responses

Xiaowen Hu
Pan Zhang
Jiaxuan Zhang
Lei Deng

Deep learning approaches have demonstrated remarkable potential in predicting cancer drug responses (CDRs), using cell line and drug features. However, existing methods predominantly rely on single-omics data of cell lines, potentially overlooking the complex biological mechanisms governing cell line responses. This paper introduces DeepFusionCDR, a novel approach employing unsupervised contrastive learning to amalgamate multi-omics features, including mutation, transcriptome, methylome, and copy number variation data, from cell lines. Furthermore, we incorporate molecular SMILES-specific transformers to derive drug features from their chemical structures. The unified multi-omics and drug signatures are combined, and a multi-layer perceptron (MLP) is applied to predict IC50 values for cell line-drug pairs. Moreover, this MLP can discern whether a cell line is resistant or sensitive to a particular drug. We assessed DeepFusionCDR's performance on the GDSC dataset and juxtaposed it against cutting-edge methods, demonstrating its superior performance in regression and classification tasks. We also conducted ablation studies and case analyses to exhibit the effectiveness and versatility of our proposed approach. Our results underscore the potential of DeepFusionCDR to enhance CDR predictions by harnessing the power of multi-omics fusion and molecular-specific transformers. The prediction of DeepFusionCDR on TCGA patient data and case study highlight the practical application scenarios of DeepFusionCDR in real-world environments.

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EAAI Journal 2024 Journal Article

Exploratory parallel hybrid sampling framework for imbalanced data classification

Ming Zheng
Zhuo Zhao
Fei Wang
Xiaowen Hu
Sheng Xu
Wanggen Li
Tong Li

Current engineering application scenarios often face the challenge of imbalanced data, hybrid sampling is an effective method to deal with the imbalanced data classification issue, which can avoid the issues of overfitting and mistakenly deleting useful majority samples when using oversampling approach and undersampling approach alone. However, at present most of the hybrid sampling approaches are implemented serially, and the implementation of oversampling and undersampling approaches alone will cause mutual interference and influence between them. This study proposes a parallel hybrid sampling framework based on the idea of parallel engineering and theoretically analyzes its superiority. The experimental results show that when applied to five classification algorithms with three performance evaluation metrics, the proposed framework outperforms the two mainstream hybrid sampling frameworks. Moreover, the proposed framework can effectively reduce the time consumption of hybrid sampling process.

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LOPSTR Conference 2022 Conference Paper

Building a Join Optimizer for Soufflé

Samuel Arch
Xiaowen Hu
David Zhao 0001
Pavle Subotic
Bernhard Scholz

Abstract Datalog has grown in popularity as a domain-specific language (DSL) for real-world applications. Crucial to its resurgence has been the advent of high-performance Datalog compilers, including Soufflé. Yet this high performance is unobtainable for users unless they provide performance hints such as join orders for rules. In this paper, we develop a join optimizer for Soufflé that automatically computes high-quality join orders using a feedback-directed optimization strategy: In a profiling stage, the compiler obtains join size estimates, and in a join ordering stage, an offline join optimizer derives cost-optimal join orders. The performance of the automatically optimized joins is demonstrated using complex real-world applications, including DOOP, DDISASM, and VPC, surpassing the performance of un-tuned join orders by a geometric mean speedup of \(12. 07\times \).

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