Aug 2026· IEEE transactions on computational biology and bioinformatics· Vol PP· 0 citations
Medicine
TL;DR
KGDDA is proposed, a multimodal framework designed for drug-disease association prediction that synergistically integrates KGs with medical ontologies, enabling the adaptive capture of intricate drug-disease interactions.
Abstract
Drug repurposing represents a cost-effective strategy to identify novel therapeutic applications for existing pharmaceuticals, circumventing the protracted timelines of traditional drug discovery. While knowledge graph (KG) based methods excel at integrating heterogeneous biomedical data, they often struggle to harmonize high-level domain knowledge with fine-grained molecular mechanisms. We propose KGDDA, a multimodal framework designed for drug-disease association prediction that synergistically integrates KGs with medical ontologies. By leveraging an attention-driven fusion mechanism, KGDDA dynamically merges contextual topological embeddings with ontology-derived priors, enabling the adaptive capture of intricate drug-disease interactions. Extensive evaluations on two benchmark datasets demonstrate that KGDDA consistently outperforms state-of-the-art baselines in both predictive accuracy and generalization. Furthermore, case studies on head and neck cancer and small cell lung cancer validate KGDDA's ability to provide actionable mechanistic insights, highlighting its potential to accelerate therapeutic discovery and precision medicine.
SGTL-DDA is proposed, a novel graph transformer framework designed to incorporate structural information and domain-specific knowledge from heterogeneous biological information networks (HBINs) that successfully identifies both known therapeutics and novel repositioning candidates, supported by molecular docking results and literature evidence.
Bowei Zhao, Hui Zhao, Yu-an Huang et al.· IEEE transactions on computa...· 0 citations
With the increasing use of multiple medications in clinical practice, accurate and interpretable prediction of organ-level adverse drug reactions (ADRs) induced by drug combinations is essential for drug safety assessment and precision medicine. Existing knowledge graph (KG)-based methods primarily model biomedical associations but leave direct structure-level interactions within drug pairs undercharacterized, while molecular representation methods often rely on whole-molecule or latent substructure encodings, offering limited chemically meaningful evidence for ADR risks. This study proposes MolADR, a multiscale complementary learning framework that integrates GNN-based KG learning with dual-granularity molecular cross-attention modeling to combine macro-level biomedical associations with microlevel molecular interaction cues. Under an emerging-drug setting, MolADR achieves PR-AUC scores of 81.17 ± 3.97, 84.04 ± 4.67, and 74.37 ± 9.90 on three data sets, consistently outperforming state-of-the-art baselines, with further analyses supporting its robustness and suggesting its ability to highlight chemically plausible atoms and functional groups for organ-level ADR prediction.
Yifan Qi, Q. Ren, Chen-Xu Wang et al.· Journal of Chemical Informat...· 0 citations
Drug-drug interaction (DDI) event prediction is critical for ensuring patient safety and optimizing therapeutic outcomes. Existing computational approaches are limited by their inability to jointly model the heterogeneous mechanisms underlying DDIs, which span molecular structure, pharmacodynamic function, and network-mediated relations. To address this limitation, we introduce M2DDI, a unified framework for dynamic multimodal fusion in DDI prediction. M2DDI utilizes a Mixture-of-Experts architecture, with each expert dedicated to a distinct pharmacological modality. A novel prior-enhanced dual-path gating strategy adaptively selects relevant experts for each drug pair by integrating mechanism-matched feature queries and ATC-based biomedical priors, thereby aligning expert selection with underlying pharmacological mechanisms and addressing the challenge of data incompleteness. Empirical evaluation on benchmark datasets demonstrates that M2DDI achieves state-of-the-art performance, particularly in new drug scenarios. Additional robustness experiments show that M2DDI maintains high predictive accuracy even when modality-specific information is partially missing, outperforming existing methods under similar conditions. Analysis of expert selection patterns further confirms alignment with established pharmacological mechanisms. These results establish M2DDI as an effective and mechanism-aware solution for comprehensive DDI prediction. The code is available at: https://github.com/RunqingXuCn/M2DDI.
Runqing Xu, Siyi Liu, Haoyang Li et al.· Proceedings of the 32nd ACM...· 0 citations
Results indicate that FragSyn, through the synergistic design of fragment-level representation and cellular context awareness, provides an effective and interpretable new approach to synergistic drug combination prediction.
Lifeng Shao, Jianqiang Sun, Hong-Zhan Ma et al.· Journal of Chemical Informat...· 0 citations
Combination therapy is widely used to treat complex diseases, yet predicting synergistic drug combinations remains challenging for rare and underrepresented cell lines due to severe data sparsity. Existing methods rely primarily on observed drug–cell responses and learn representations within a limited experimental knowledge regime, resulting in poor generalization in cold-start and long-tail settings. To address this limitation, we propose L2aCo, a model-agnostic knowledge alignment framework that leverages large language models as external biomedical knowledge sources to expand the effective knowledge boundary for drug combinations. L2aCo augments drug and cell-line representations with LLM-inferred semantic profiles capturing biological mechanisms and contextual relations beyond experimental measurements. These semantic representations are integrated with conventional molecular and cellular features through a representation-level alignment mechanism, which injects relational knowledge while preserving task-relevant experimental signals. By enabling effective knowledge transfer under data scarcity, L2aCo substantially improves generalization for cold-start and long-tail cell lines. Experiments on multiple public benchmarks show consistent and significant performance gains for novel cell lines when L2aCo is integrated with drug synergy predictors, with pronounced improvements on novel cell lines.
Tengfei Ma, Yuqin He, Zhonghao Ren et al.· Proceedings of the 32nd ACM...· 0 citations
Experiments demonstrate that HSAF-DDI achieves superior overall performance compared to state-of-the-art methods, indicating the critical role of fine-grained biological features in improving the DDI prediction performance.
Xiaoli Lin, Si-Yuan Zhang, Bo Li et al.· Journal of Chemical Informat...· 0 citations
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