Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Journal of chemical information and modeling
HyperDC: A Non-Uniform Hypergraph Framework for Dual- and Higher-Order Drug Combination Recommendation Across Diverse Complex Diseases
Complex diseases are commonly characterized by dysregulation across multiple targets and pathways, making drug combinations an important therapeutic strategy. This creates a growing need for computational methods to prioritize candidate combinations, yet current methods face several practical challenges, including cross-disease application, unified prediction of dual- and multidrug combinations, lack of reliable negative labels, and prediction for low-resource diseases. In this study, we proposed HyperDC, a unified cross-disease drug combination recommendation framework. HyperDC constructs a nonuniform hypergraph based on clinical and knowledge-driven drug-disease association data, representing single drugs and dual- and multidrug combinations in a unified modeling space. It further integrates knowledge graph pretraining and adversarial negative sampling to enhance model discrimination across tasks with different difficulty levels. In the unified dual-drug benchmark and disease-specific prediction tasks, HyperDC outperformed representative methods by up to 12.8 and 30.0 percentage points, respectively. In fixed-anchor clinical ranking tasks, HyperDC showed superior performance across multiple top-ranked recall settings and preferentially recalled FDA-approved dual-drug combinations and clinically supported three-drug combinations. In the data-sparse metabolic dysfunction-associated steatohepatitis (MASH) scenario, HyperDC completed a full workflow from anchor-drug identification and combination partner prioritization to in vitro experimental validation: 70% of the top 10 single-drug candidates were supported by recent literature, and 4 of 6 tested combinations showed synergistic lipid-lowering and anti-inflammatory effects, yielding a hit rate of 66.7%. Overall, HyperDC may narrow the screening space, improve prioritization efficiency, and provide methodological support for the development of combination therapy strategies for complex diseases.
29 July 2026
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A Unified Molecular Graph and Protein Language Model Framework for Predicting Human Drug-Hormone Receptor Interactions with Structure-Aware Validation
Hormones regulate many essential biological processes by interacting with specific receptors that control gene expression, metabolism, growth, and immune function. Because numerous therapeutic compounds can influence or disrupt hormone signaling pathways, understanding drug-hormone receptor interactions (DHRI) is crucial for ensuring both therapeutic efficacy and endocrine safety. However, computational approaches for predicting DHRI remain limited, and most existing models do not explicitly incorporate hormone receptor-specific information. In this study, we propose a receptor-aware deep learning framework for DHRI prediction that integrates structural drug features with contextually embedded hormone receptor information. Drug molecules are represented using a hybrid encoding strategy that combines Morgan fingerprints and graph transformer-based molecular features to capture both global chemical properties and local structural information, while hormone receptor sequences are encoded using the pretrained ESM2 protein language model to obtain biologically meaningful sequence representations. The fused drug-hormone receptor features are then processed through a multilayer neural network to predict interaction probabilities. Model performance was evaluated using three splitting strategies, including random split, cold-drug split, and scaffold-based split, to assess both predictive accuracy and generalization ability. Under the random split setting, the model achieved an accuracy of 0.93, sensitivity of 0.94, specificity and precision of 0.92, F1-score of 0.93, and MCC of 0.86 on an independent data set, while also maintaining comparable performance under the more stringent cold-drug and scaffold-based settings. Feature importance analysis showed that atomic identity, hybridization, atom degree, and bond type were key shared determinants, while receptor-stratified results revealed receptor-dependent contributions from secondary features such as aromaticity, chirality, etc. In addition, t-SNE visualization showed clear class separation after training, and molecular docking across estrogen, androgen, and glucocorticoid receptors further supported the biological relevance of the predictions. Together, these findings demonstrate that incorporating hormone receptor-specific sequence information enables more reliable and biologically meaningful prediction of drug-hormone receptor interactions.
28 July 2026
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