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Dual-stream decoupled hypergraph learning for compound-target interaction prediction in traditional Chinese medicine

Chen L, Luo M, Wang L, Yan C

Comput Biol Chem, 2026 Oct 26;126(Pt 2):109507.
PMID: 42956504 DOI: 10.1016/j.compbiolchem.2026.109507

Traditional Chinese medicine (TCM) is a complex therapeutic system characterized by interactions among multiple herbal components. However, its multi-component and multi-target nature leads to highly complex biological relationships, making it difficult to systematically understand the underlying mechanisms and limiting its broader application in precision medicine. To address this issue, we propose DSDHG, a dual-stream decoupled hypergraph learning framework for therapeutic target identification of natural compounds in TCM. A four-layer heterogeneous biomedical network is constructed by integrating herbs, natural compounds, target proteins, and diseases. Global structural representations are initialized through random walk-based methods and Skip-Gram embedding. To capture higher-order biological associations, compound-herb and target-disease interactions are modeled as hypergraphs. Based on these structures, DSDHG adopts a dual-stream learning strategy to separate semantic and topological information. The semantic stream employs relation-aware global attention to capture long-range biological dependencies, while the topological stream uses linear propagation to preserve structural connectivity and alleviate over-smoothing. In addition, a cross-correlation decorrelation constraint is introduced to support disentangled representation learning, enabling latent factors to encourage representation diversity and reduce feature redundancy. Experiments on three benchmark datasets demonstrate that DSDHG outperforms existing state-of-the-art methods across multiple evaluation metrics. Case studies further show that the proposed framework can effectively identify potential therapeutic targets of natural compounds and facilitate target discovery in TCM. The code is publicly available at https://github.com/27167199/DSDHG.
Fulltext Development and Validation of a Deep Learning Model for Predicting Treatment Response in Patients With Newly Diagnosed Epilepsy

Hakeem H, Feng W, Chen Z, Choong J, Brodie MJ, Fong SL, et al.

JAMA Neurol, 2022 Oct 01;79(10):986-996.
PMID: 36036923 DOI: 10.1001/jamaneurol.2022.2514

IMPORTANCE: Selection of antiseizure medications (ASMs) for epilepsy remains largely a trial-and-error approach. Under this approach, many patients have to endure sequential trials of ineffective treatments until the "right drugs" are prescribed.
OBJECTIVE: To develop and validate a deep learning model using readily available clinical information to predict treatment success with the first ASM for individual patients.
DESIGN, SETTING, AND PARTICIPANTS: This cohort study developed and validated a prognostic model. Patients were treated between 1982 and 2020. All patients were followed up for a minimum of 1 year or until failure of the first ASM. A total of 2404 adults with epilepsy newly treated at specialist clinics in Scotland, Malaysia, Australia, and China between 1982 and 2020 were considered for inclusion, of whom 606 (25.2%) were excluded from the final cohort because of missing information in 1 or more variables.
EXPOSURES: One of 7 antiseizure medications.
MAIN OUTCOMES AND MEASURES: With the use of the transformer model architecture on 16 clinical factors and ASM information, this cohort study first pooled all cohorts for model training and testing. The model was trained again using the largest cohort and externally validated on the other 4 cohorts. The area under the receiver operating characteristic curve (AUROC), weighted balanced accuracy, sensitivity, and specificity of the model were all assessed for predicting treatment success based on the optimal probability cutoff. Treatment success was defined as complete seizure freedom for the first year of treatment while taking the first ASM. Performance of the transformer model was compared with other machine learning models.
RESULTS: The final pooled cohort included 1798 adults (54.5% female; median age, 34 years [IQR, 24-50 years]). The transformer model that was trained using the pooled cohort had an AUROC of 0.65 (95% CI, 0.63-0.67) and a weighted balanced accuracy of 0.62 (95% CI, 0.60-0.64) on the test set. The model that was trained using the largest cohort only had AUROCs ranging from 0.52 to 0.60 and a weighted balanced accuracy ranging from 0.51 to 0.62 in the external validation cohorts. Number of pretreatment seizures, presence of psychiatric disorders, electroencephalography, and brain imaging findings were the most important clinical variables for predicted outcomes in both models. The transformer model that was developed using the pooled cohort outperformed 2 of the 5 other models tested in terms of AUROC.
CONCLUSIONS AND RELEVANCE: In this cohort study, a deep learning model showed the feasibility of personalized prediction of response to ASMs based on clinical information. With improvement of performance, such as by incorporating genetic and imaging data, this model may potentially assist clinicians in selecting the right drug at the first trial.

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