Apr 2026· Journal of Chemical Information and Modeling· Vol 66 9, pp.
5008-5017
· 10 citations· ⚡ 1 influential· 56 references
Computer ScienceMedicine
TL;DR
The DRHIN platform provides a code-free portal supporting three key predictive tasks: discovering drug-disease associations, repurposing existing drugs for new indications, and identifying potential therapies for specific diseases, making analyses accessible and reproducible.
Abstract
Drug repositioning (DR) identifies new therapeutic uses for approved drugs, reducing development burdens and offering safer treatment options for patients. While high-throughput technologies generate complex, large-scale multiomics data, existing DR tools struggle to comprehensively analyze the resulting biological networks. To address this challenge, we present DRHIN, an integrated, interactive web server for DR over heterogeneous information networks (HINs) using advanced deep learning techniques. DRHIN integrates transcriptomics, proteomics, and microbiome data, incorporating eight biological entities and 19 association types to build diverse HINs and elucidate the underlying molecular mechanisms. It includes 19 state-of-the-art graph representation algorithms, enabling flexible training, comparison, and evaluation of heterogeneous network data. The platform provides a code-free portal supporting three key predictive tasks: discovering drug-disease associations, repurposing existing drugs for new indications, and identifying potential therapies for specific diseases, making analyses accessible and reproducible. Leveraging high-performance computing, DRHIN efficiently processes million-scale networks, ensuring practical applicability in real-world scenarios. The web server is freely accessible at http://drhin.tianshanzw.cn.
MKASynergy, an adaptive drug synergy prediction method based on a mixture-of-experts kernel mechanism, achieves competitive predictive performance and visualization analysis confirms the model’s effectiveness in feature decoupling and helps interpret latent drug synergistic mechanisms.
Cundong Lin, Jiancheng Ni, Ying Yang et al.· Network Modeling Analysis in...· 0 citations
Empirical evaluation and robustness experiments show that M2DDI maintains high predictive accuracy even when modality-specific information is partially missing, outperforming existing methods under similar conditions and establish M2DDI as an effective and mechanism-aware solution for comprehensive DDI prediction.
Runqing Xu, Siyi Liu, Hao-Yang Li et al.· Proceedings of the 32nd ACM...· 0 citations
IHLO-DTI, a novel prediction model based on an improved hypergraph neural network and Laplacian matrix optimization, can effectively capture high-order many-to-many interactions between drugs and targets, improving prediction accuracy and robustness.
Guolongwei Dai, Tao Luo, Dan-Dan Li et al.· ACS Synthetic Biology· 0 citations
An overview of how artificial intelligence might serve not only as a tool but also as a strategic solution for safe prescribing and tailored pharmacotherapy, hence opening up new avenues for the field of drug safety science is presented.
D. Tripathi, A. Wal, Vivek Kumar Gupta et al.· Current Bioinformatics· 0 citations
The key innovation of LOGIC lies in constructing a dictionary of functional groups and symptoms, and performing a simple and intuitive multi-hot encoding of drugs and diseases at the micro-scale, and in employing large language models (LLMs) to derive the meso-scale features of diseases without requiring additional dom...
Yun-Fei He, Shi-Kai Chen, Yu-Chen Zhao et al.· IEEE Transactions on Computa...· 0 citations
This review systematically summarizes the latest research progress in druggable protein prediction algorithms, providing a comprehensive analysis across three dimensions: data resources, feature engineering, and predictive models.
Hong-Qi Zhang, Hong-Ling Wang, Shang-Hua Liu et al.· Current Drug Targets· 0 citations
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